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FAST NUCES (Karachi) - CS401 - Spring 2020", + "dirs_total_count": 15, + "files_root_entries": [ + { + "path": ".gitignore", + "size": 12, + "type": "file" + }, + { + "path": ".vscode", + "size": 0, + "type": "dir" + }, + { + "path": "A1", + "size": 0, + "type": "dir" + }, + { + "path": "A2", + "size": 0, + "type": "dir" + }, + { + "path": "A3", + "size": 0, + "type": "dir" + }, + { + "path": "README.md", + "size": 2919, + "type": "file" + }, + { + "path": "_config.yml", + "size": 25, + "type": "file" + } + ], + "files_total_count": 54, + "first_commit_date_default_branch": "2020-02-21T16:32:32Z", + "forks": 2, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": "https://hassan11196.github.io/AI-Assignments/", + "input_url": "https://github.com/hassan11196/AI-Assignments", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Jupyter Notebook", + "size": 2390311 + }, + { + "language": "Python", + "size": 20186 + } + ], + "last_commit_date_default_branch": "2020-07-05T14:33:19Z", + "last_commit_oid_default_branch": "4ac5e7fd6998f011bb3f535ddc87bc039cc6c8ff", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/AI-Assignments", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "Jupyter Notebook", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2020-07-05T14:33:20Z", + "readme_length": 2914, + "readme_text": "# AI - Assignments\n\n## A1 \nFollowing are the strategies to be implemented.\n* Breadth first search\n* Uniform cost search\n* Greedy best first search\n* Iterative deepening depth first search\n\nA comparison of these four needs to be done. Complete list of pathway and path cost of each algorithm has to be calculated so that it shows clearly that which algorithm is best out of all in ascending order.\n\n### Search Algorithms Sorted in order of Path Cost : \n\nstart_node : Arad\nend_node : Bucharest\n\n1. Uniform Cost Search Path : ['Arad', 'Sibiu', 'Rimnicu Vilcea', 'Pitesti', 'Bucharest'] Cost : 418\n2. Breadth First Search Path : ['Arad', 'Sibiu', 'Fagaras', 'Bucharest'] Cost : 450\n3. Greedy Best First Search Path : ['Arad', 'Sibiu', 'Fagaras', 'Bucharest'] Cost : 450\n4. Iterative Deepening Depth First Search Path : ['Arad', 'Sibiu', 'Fagaras', 'Bucharest'] Cost : 450\n\n### Visualizations :\n * #### Uniform Cost Search:\n ![](A1/ucs-gif.gif)\n * #### Iterative Deepening Depth First Search:\n ![](A1/iddfs-gif.gif) \n\nConclusions : \n * Uniform Cost Search is Best as it is complete and optimal for edges with non uniform costs.\n * Breadth First Search and Iterative Deepening Depth First Search is only optimal for edges with uniform cost\n * Greedy Best First Search Path is not optimal.\n\n\n### References : \nhttp://theory.stanford.edu/~amitp/GameProgramming/Heuristics.html\n\n\n## A2\n\n### Question 1: Exploring a Simple Genetic Algorithm\n\n![](A2/genetic.jpeg)\n\n\n\n### Question 2: N-Queens using backtracking\n\n![](A2/nqueens.gif)\n\n### References:\n\nhttps://towardsdatascience.com/introduction-to-genetic-algorithms-including-example-code-e396e98d8bf3\n\nhttps://towardsdatascience.com/introduction-to-optimization-with-genetic-algorithm-2f5001d9964b\n\nhttp://see.stanford.edu/materials/icspacs106b/H19-RecBacktrackExamples.pdf\n\n## A3\n Assignment Report Available [here](https://github.com/hassan11196/AI-Assignments/blob/master/A3/Submission/AI-A3-K173654-D.docx).\n### Question 1: Gaussian Naïve Bayes Classifier Written from scratch.\n Classify the Parkinson's disease (PD) data set using Gaussian Naïve Bayes Classifier.\n Solution in [Notebook](https://github.com/hassan11196/AI-Assignments/blob/master/A3/Submission/Q1/K173654-A3-Q1.ipynb) \n \n### Question 2: Naïve Bayes Classifier using RapidMiner - Wine Dataset:\n - Simple Naive Bayes\n ![](A3/images/Q2-nb-simple-model.png)\n - Kernel Naive Bayes\n ![](A3/images/Q2-nb-kernel-model.png)\n\n### Question 3: Naïve Bayes Classifier using RapidMiner - Parkinson's Dataset:\n - Simple Naive Bayes\n ![](A3/images/Q3-nb-simple-model.png)\n - Kernel Naive Bayes\n ![](A3/images/Q3-nb-kernel-model.png)\n\n### Question 4: K- MEANS Clustering using RapidMiner - Wine Dataset:\n - Accuracy \n ![](A3/images/Q4-kmeans-accuracy.png)\n - Precision\n ![](A3/images/Q4-kmeans-precision.png) \n - Recall\n ![](A3/images/Q4-kmeans-recall.png)", + "readme_title": "AI - Assignments", + "releases_count": 0, + "repo": "AI-Assignments", + "repo_name": "AI-Assignments", + "stars": 4, + "topics": [ + "python", + "jupyter-notebook", + "jupyter", + "networkx", + "artificial-intelligence", + "ai", + "graph-algorithms", + "search-algorithm", + "rapidminer", + "kmeans-clustering", + "naive-bayes-classifier", + "genetic-algorithm", + "nqueens-problem" + ], + "updated_at": "2025-11-17T04:21:57Z", + "url": "https://github.com/hassan11196/AI-Assignments", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/AI-Assignments" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/AMLSim" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 165, + "contributors_count": 5, + "contributors_top": [ + { + "contributions": 158, + "html_url": "https://github.com/hkanezashi", + "login": "hkanezashi" + }, + { + "contributions": 3, + "html_url": "https://github.com/suzumura", + "login": "suzumura" + }, + { + "contributions": 2, + "html_url": null, + "login": "TOYOTARO SUZUMURA" + }, + { + "contributions": 1, + "html_url": "https://github.com/zygm0nt", + "login": "zygm0nt" + }, + { + "contributions": 1, + "html_url": "https://github.com/stevemart", + "login": "stevemart" + } + ], + "created_at": "2020-10-02T14:18:44Z", + "default_branch": "master", + "description": "The AMLSim project is intended to provide a multi-agent based simulator that generates a series of banking transaction data together with a set of known money laundering patterns - mainly for the purpose of testing your machine learning models and graph algorithms. We welcome you to enhance this effort since data set is critical to advance our detection capabilities of money laundering activities .", + "dirs_total_count": 33, + "files_root_entries": [ + { + "path": ".gitignore", + "size": 546, + "type": "file" + }, + { + "path": "LICENSE", + "size": 11357, + "type": "file" + }, + { + "path": "README.md", + "size": 5310, + "type": "file" + }, + { + "path": "conf.json", + "size": 2349, + "type": "file" + }, + { + "path": "janusgraph.properties", + "size": 3160, + "type": "file" + }, + { + "path": "jars", + "size": 0, + "type": "dir" + }, + { + "path": "outputs", + "size": 0, + "type": "dir" + }, + { + "path": "paramFiles", + "size": 0, + "type": "dir" + }, + { + "path": "requirements.txt", + "size": 75, + "type": "file" + }, + { + "path": "sample", + "size": 0, + "type": "dir" + }, + { + "path": "scripts", + "size": 0, + "type": "dir" + }, + { + "path": "src", + "size": 0, + "type": "dir" + } + ], + "files_total_count": 167, + "first_commit_date_default_branch": "2018-12-18T16:14:57Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/AMLSim", + "is_archived": false, + "is_fork": true, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Python", + "size": 200607 + }, + { + "language": "Java", + "size": 119318 + }, + { + "language": "Groovy", + "size": 16885 + }, + { + "language": "Shell", + "size": 3800 + } + ], + "last_commit_date_default_branch": "2020-05-07T19:38:08Z", + "last_commit_oid_default_branch": "62ad4b8171cbd0fb6ff18bb2b60235715198481f", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "Apache License 2.0", + "license_spdx": "Apache-2.0", + "name_with_owner": "hassan11196/AMLSim", + "owner": "hassan11196", + "parent_repo": "IBM/AMLSim", + "parent_url": "https://github.com/IBM/AMLSim", + "primary_language": "Python", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2020-05-07T19:38:09Z", + "readme_length": 5308, + "readme_text": "Please cite our following paper if you use the data set for your publications. \n\nEvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs\nhttps://arxiv.org/abs/1902.10191\n\nScalable Graph Learning for Anti-Money Laundering: A First Look\nhttps://arxiv.org/abs/1812.00076\n\n**Important: Please use the \"master\" branch for the practical use and testing. Other branches such as \"new-schema\" are outdated and unstable. [Wiki pages](https://github.com/IBM/AMLSim/wiki/) are still under construction and some of them do not catch up with the latest implementations. Please refer this README.md instead.**\n\n# AMLSim\nThis project aims at building a multi-agent simulator of anti-money laundering - namely AML, and sharing synthetically generated data so that researchers can design and implement their new algorithms over the unified data.\n\n\n# Dependencies\n- Java 8 (Download and copy all jar files to `jars` directory: See also `jars/README.md`)\n - [MASON](https://cs.gmu.edu/~eclab/projects/mason/) version 18\n - [PaySim](https://github.com/EdgarLopezPhD/PaySim) (jar file already exists)\n - [Commons-Math](http://commons.apache.org/proper/commons-math/download_math.cgi) version 3.6.1\n - [JSON in Java](https://jar-download.com/artifacts/org.json/json/20180813) version 20180813\n - [WebGraph](http://webgraph.di.unimi.it/) version 3.6.1\n - [DSI Utilities](http://dsiutils.di.unimi.it/) version 2.5.4\n - [fastutil](http://fastutil.di.unimi.it/) version 8.2.3\n - [Sux for Java](http://sux.di.unimi.it/) version 4.2.0\n - [JSAP](http://www.martiansoftware.com/jsap/) version 2.1\n - [SLF4J](https://www.slf4j.org/download.html) version 1.7.25\n - [MySQL Connector for Java](https://dev.mysql.com/downloads/connector/j/5.1.html) version 5.1\n- Python 3.7 (The following packages can be installed with `pip3 install -r requirements.txt`)\n - numpy\n - networkx==1.11 (We do not support version 2.* due to performance issues for large graphs)\n - matplotlib==2.2.3 (The latest version is not compatible)\n - pygraphviz\n - powerlaw\n - python-dateutil\n\n\n# Directory Structure\nSee Wiki page [Directory Structure](https://github.com/IBM/AMLSim/wiki/Directory-Structure) for details.\n\n\n\n# Introduction for Running AMLSim\nSee Wiki page [Quick Introduction to AMLSim](https://github.com/IBM/AMLSim/wiki/Quick-Introduction-to-AMLSim) for details.\n\n## 1. Generate transaction CSV files from parameter files (Python)\nBefore running the Python script, please check and edit configuration file `conf.json`.\n```json5\n{\n//...\n \"input\": {\n \"directory\": \"paramFiles/1K\", // Parameter directory\n \"schema\": \"schema.json\", // Configuration file of output CSV schema\n \"accounts\": \"accounts.csv\", // Account list parameter file\n \"alert_patterns\": \"alertPatterns.csv\", // Alert list parameter file\n \"degree\": \"degree.csv\", // Degree sequence parameter file\n \"transaction_type\": \"transactionType.csv\", // Transaction type list file\n \"is_aggregated_accounts\": true // Whether the account list represents aggregated (true) or raw (false) accounts\n },\n//...\n}\n```\n\nThen, please run transaction graph generator script.\n```bash\ncd /path/to/AMLSim\npython3 scripts/transaction_graph_generator.py conf.json\n```\n\n## 2. Build and launch the transaction simulator (Java)\nParameters for the simulator are defined at the \"general\" section of `conf.json`. \n\n```json5\n{\n \"general\": {\n \"random_seed\": 0, // Seed of random number\n \"simulation_name\": \"sample\", // Simulation name (identifier)\n \"total_steps\": 720, // Total simulation steps\n \"base_date\": \"2017-01-01\" // The date corresponds to the step 0 (the beginning date of this simulation)\n },\n//...\n}\n```\n\nPlease compile Java files (if not yet) and launch the simulator.\n```bash\nsh scripts/build_AMLSim.sh\nsh scripts/run_AMLSim.sh conf.json\n```\n\n\n## 3. Convert the raw transaction log file\nThe file names of the output data are defined at the \"output\" section of `conf.json`.\n```json5\n{\n//...\n\"output\": {\n \"directory\": \"outputs\", // Output directory\n \"accounts\": \"accounts.csv\", // Account list CSV\n \"transactions\": \"transactions.csv\", // All transaction list CSV\n \"cash_transactions\": \"cash_tx.csv\", // Cash transaction list CSV\n \"alert_members\": \"alert_accounts.csv\", // Alerted account list CSV\n \"alert_transactions\": \"alert_transactions.csv\", // Alerted transaction list CSV\n \"sar_accounts\": \"sar_accounts.csv\", // SAR account list CSV\n \"party_individuals\": \"individuals-bulkload.csv\",\n \"party_organizations\": \"organizations-bulkload.csv\",\n \"account_mapping\": \"accountMapping.csv\",\n \"resolved_entities\": \"resolvedentities.csv\",\n \"transaction_log\": \"tx_log.csv\",\n \"counter_log\": \"tx_count.csv\",\n \"diameter_log\": \"diameter.csv\"\n },\n//...\n}\n```\n\n```bash\npython3 scripts/convert_logs.py conf.json\n```\n\n## 4. Export statistical information of the output data to image files (optional)\n\n```bash\npython3 scripts/visualize/plot_distributions.py conf.json\n```\n\n## 5. Validate alert transaction subgraphs by comparison with the parameter file (optional)\n```\npython3 scripts/validation/validate_alerts.py conf.json\n```\n\n\n\n## Remove all log and generated image files from `outputs` directory and a temporal directory\n```bash\nsh scripts/clean_logs.sh\n```", + "readme_title": "AMLSim", + "releases_count": 0, + "repo": "AMLSim", + "repo_name": "AMLSim", + "stars": 0, + "topics": [], + "updated_at": "2020-10-02T14:18:47Z", + "url": "https://github.com/hassan11196/AMLSim", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/AMLSim" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + 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"license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/AlgorithmsVisualized", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "Jupyter Notebook", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2020-01-01T22:04:26Z", + "readme_length": 432, + "readme_text": "# AlgorithmsVisualized\n\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/hassan11196/AlgorithmsVisualized.git/master)\n\nA Jupyter Notebook with Different Graph Based Traversal and Search Algorithms Visualized.\n\nCurrent list of Algorithms : \n\n- Breadth First Search\n- A* Algorithm\n- Best First Algorithm\n\nVisualizations :\n\n### Breadth First Search\n\n![](breadth_first_search.gif)\n\n### A Star\n\n![](A_star.gif)", + "readme_title": "AlgorithmsVisualized", + "releases_count": 0, + "repo": "AlgorithmsVisualized", + "repo_name": "AlgorithmsVisualized", + "stars": 0, + "topics": [ + "jupyter-notebook", + "python", + "astar-algorithm", + "bfs-algorithm", + "visualization" + ], + "updated_at": "2020-01-10T13:26:28Z", + "url": "https://github.com/hassan11196/AlgorithmsVisualized", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/AlgorithmsVisualized" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/Bank-Transaction-Categorisation" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 2, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 1, + "html_url": "https://github.com/natchezl", + "login": 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"https://github.com/hassan11196/Bank-Transaction-Categorisation", + "is_archived": false, + "is_fork": true, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Jupyter Notebook", + "size": 4021245 + }, + { + "language": "Python", + "size": 45401 + } + ], + "last_commit_date_default_branch": "2019-07-26T19:30:23Z", + "last_commit_oid_default_branch": "ce870f790396b1e189f5bc8277f22802933ca8c9", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/Bank-Transaction-Categorisation", + "owner": "hassan11196", + "parent_repo": "natchezl/Bank-Transaction-Categorisation", + "parent_url": "https://github.com/natchezl/Bank-Transaction-Categorisation", + "primary_language": "Jupyter Notebook", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2019-07-26T19:30:57Z", + "readme_length": 2819, + "readme_text": "# Bank-Transaction-Categorisation\nThis repository is the code of my dissertation project for MSc Business Analytics, University College London\n\n## Dependencies\n-\tScikit-learn version 0.20.3 \n-\tSpacy version 2.1.4 \n-\tGensim version 3.7.3 \n-\tFastText version 0.9.1 \n- xgboost version 0.9\n-\tKeras version 2.2.4 \n\n## Prereqresites\nDownload pre-trained word-embeddings files\n1. Word2Vec `GoogleNews-vectors-negative300.bin` (from : https://code.google.com/archive/p/word2vec/)\n2. FastText `wiki-news-300d-1M-subword.bin` (from : https://fasttext.cc/docs/en/english-vectors.html)\n3. GloVe `glove.42B.300d.zip` and `glove.840B.300d.zip` (from : https://github.com/stanfordnlp/GloVe) \n and pre-process GloVe pre-trained models with `./utils/load_glove.py`\n## Running the code\n1. Edit `ROOT_PATH`, `DATA_FILES` and pre-trained word-embedding file paths in `./utils/config.py`. \n Note that the `DATA_FILES` is in a dict object (file_reference: filename.xlsx) For example, \n ```\n DATA_FILES = {\n 'file1': 'file1.xlsx',\n 'file2': 'file2.xlsx',\n ...\n }\n ```\n2. Put transaction data files (.xlsx) into folder `./Transaction Data`\n3. Run Jupyter Notebook files in `./notebooks/' in order\n\n## Project file structure\n```\n.\n├── notebooks\n│   ├── 0_Split_Train_Test_Data.ipynb\n│   ├── 1_Select_Text_Representation.ipynb\n│   ├── 2_Add_numeric_features_and_model_selection.ipynb\n│   ├── 3_Neural_Network.ipynb\n│   ├── 4_Predict_Test_Data.ipynb\n│   ├── grid_search.ipynb.ipynb\n├── output\n│   ├── embedding_benchmark_results\n│   │   └── \n│   ├── embedding_results\n│   │   └── \n│   ├── final_model\n│   │   └── \n│   ├── model_results\n│   │   └── \n│   └── text_embeddings\n│      └── \n├── resources\n│   ├── country_code.csv\n│   ├── currency.csv\n│   ├── nz_cities.csv\n│   ├── nzpostcodes_v2.csv\n│   └── stop_words.csv\n├── Transaction Data\n│   └── \n└── utils\n ├── config.py -> Edit config in this file\n ├── feature_eng_utils.py\n ├── load_glove.py\n ├── misc_utils.py\n ├── ml_models.py\n ├── neural_network_model.py\n ├── text_embedding.py\n └── txn_reader.py\n ```", + "readme_title": "Bank-Transaction-Categorisation", + "releases_count": 0, + "repo": "Bank-Transaction-Categorisation", + "repo_name": "Bank-Transaction-Categorisation", + "stars": 0, + "topics": [], + "updated_at": "2021-06-19T18:37:58Z", + "url": "https://github.com/hassan11196/Bank-Transaction-Categorisation", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/Bank-Transaction-Categorisation" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/Best-websites-a-programmer-should-visit" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 826, + "contributors_count": 173, + "contributors_top": [ + { + "contributions": 454, + "html_url": "https://github.com/sdmg15", + "login": "sdmg15" + }, + { + "contributions": 43, + "html_url": "https://github.com/ityler", + "login": "ityler" + }, + { + "contributions": 36, + "html_url": "https://github.com/rachmadaniHaryono", + "login": "rachmadaniHaryono" + }, + { + "contributions": 27, + "html_url": "https://github.com/lalitmee", + "login": "lalitmee" + }, + { + "contributions": 10, + "html_url": "https://github.com/srinivasreddy", + "login": "srinivasreddy" + }, + { + "contributions": 9, + "html_url": "https://github.com/Ankk98", + "login": "Ankk98" + }, + { + "contributions": 5, + "html_url": "https://github.com/iArunava", + "login": "iArunava" + }, + { + "contributions": 5, + "html_url": "https://github.com/douglasg14b", + "login": "douglasg14b" + } + ], + "created_at": "2019-12-07T14:38:28Z", + "default_branch": "master", + "description": ":link: Some useful websites for programmers.", + "dirs_total_count": 0, + "files_root_entries": [ + { + "path": ".travis.yml", + "size": 520, + "type": "file" + }, + { + "path": "CODE_OF_CONDUCT.md", + "size": 3221, + "type": "file" + }, + { + "path": "CONTRIBUTING.md", + "size": 334, + "type": "file" + }, + { + "path": "LICENSE", + "size": 1073, + "type": "file" + }, + { + "path": "README.md", + "size": 75853, + "type": "file" + }, + { + "path": "_config.yml", + "size": 29, + "type": "file" + }, + { + "path": "package.json", + "size": 102, + "type": "file" + }, + { + "path": "white_listed_sites.txt", + "size": 777, + "type": "file" + } + ], + "files_total_count": 8, + "first_commit_date_default_branch": "2017-03-05T20:25:18Z", + "forks": 1, + "has_ci": false, + "has_contributing": true, + "has_docker": false, + "has_docs": false, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/Best-websites-a-programmer-should-visit", + "is_archived": false, + "is_fork": true, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [], + "last_commit_date_default_branch": "2019-12-07T07:08:14Z", + "last_commit_oid_default_branch": "af830eda181441374b5b76e0d26d884daff650f2", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "MIT License", + "license_spdx": "MIT", + "name_with_owner": "hassan11196/Best-websites-a-programmer-should-visit", + "owner": "hassan11196", + "parent_repo": "sdmg15/Best-websites-a-programmer-should-visit", + "parent_url": "https://github.com/sdmg15/Best-websites-a-programmer-should-visit", + "primary_language": null, + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2019-12-07T07:08:15Z", + "readme_length": 75798, + "readme_text": "

\n\t
\n\t\"awesome\"\n\t
\n

\n\n[![Build Status](https://api.travis-ci.org/sdmg15/Best-websites-a-programmer-should-visit.svg?branch=master)](https://travis-ci.org/sdmg15/Best-websites-a-programmer-should-visit)\n\n# Best-websites-a-programmer-should-visit\nSome useful websites for programmers.\n\nWhen learning CS, there are some useful sites you must know to get always informed in order to do your technologies even better and learn new things. Here is a non exhaustive list of some sites you should visit. This list will get updated as soon as I can get another link, but you can also contribute by adding those you know :wink:\n\n**Note** : [Chinese Version](https://github.com/tuteng/Best-websites-a-programmer-should-visit-zh)\n\n

Index

\n\n\n## When you get stuck\n- [Codementor](https://www.codementor.io) : A mentorship community to learn from fellow developers via live 1:1 help and more.\n- [devRant](https://www.devrant.io) : Community where you can rant and release your stress\n- [Learn Anything](https://learn-anything.xyz) : Community curated knowledge graph of best paths for learning anything\n- [Quora](https://www.quora.com) : A place to share knowledge and better understand the world\n- [Stack Overflow](https://stackoverflow.com) : subscribe to their weekly newsletter and any other topic which you find interesting\n\n\n## News\n- [ACM TechNews](http://technews.acm.org) : TechNews\n- [Stratechery](https://stratechery.com) : Stratechery by Ben Thompson\n- [AlternativeTo](https://alternativeto.net) : Crowdsourced software recommendations\n- [Ars Technica](https://arstechnica.com) : posts unique quality articles\n- [Better Dev Links](https://betterdev.link) : Weekly links to help you become a better developer\n- [DevOpsLinks](http://devopslinks.com) : An online community of thousands of developers and IT experts interested in DevOps\n- [GSMArena.com](http://www.gsmarena.com) : news related to latest mobile phones and android.\n- [Hacker News Digest](https://hndigest.com) : curated automatically, delivered as frequently as you want\n- [Hacker News](https://news.ycombinator.com) : news aggregator for programmers where civility is king, try a newsletter to get top news to your inbox:\n- [Hacker Newsletter](http://www.hackernewsletter.com) : curated by hand, delivered weekly\n- [Hacker Noon](https://hackernoon.com) : How hackers start their afternoons.\n- [High Scalability](http://highscalability.com) : Success stories of various companies on their apps, infra scaling.\n- [Lobsters](https://lobste.rs) : Lobsters is a technology-focused community centered around link aggregation and discussion.\n- [product hunt](https://www.producthunt.com) : Discover your next favorite thing\n- [Recode](https://www.recode.net) : Tech news that focuses on the business of Silicon Valley\n- [Reddit.com/r/programming](https://www.reddit.com/r/programming/) : Subreddit with aggregated tech news and articles\n- [Slashdot](https://slashdot.org) : News, Social news (Originally \"News for Nerds. Stuff that Matters\")\n- [StackShare](https://stackshare.io) : See what tools are popular with developers and companies and read about their technology decisions\n- [TechCrunch](https://techcrunch.com) : dedicated to obsessively profiling startups, reviewing new Internet products, and breaking tech news\n- [The DEV Community](https://dev.to) : Where programmers share ideas and help each other grow.\n- [The Verge](https://www.theverge.com) : More product oriented tech news\n- [WCCF Tech](https://wccftech.com) : curated news related to Computer Hardware, Software and everything else in the Tech World. \n- [XDA](https://www.xda-developers.com) : Android Developers News\n- [Hashnode](https://hashnode.com/) : Hashnode is a friendly and inclusive community for software developers\n\n## Magazines\n- [MIT Technology Review](https://www.technologyreview.com/magazine/) : MIT's tech review magazine.\n- [Nautilus](http://nautil.us) : NewYorker for tech.\n- [LWN](https://lwn.net) : Weekly news coverage of opensource technologies, programming, etc. ( Originally Linux Weekly News).\n\n\n## Coding practice for beginners\n- [freeCodeCamp](https://www.freecodecamp.com) : Learn to code and build projects for nonprofits. Build your full stack web development portfolio today\n- [Blackbird School](https://blackbird.school) : Learn to code online with our beginner-friendly platform.\n- [Glitch](https://glitch.com/) : Great place to play around with code in an easy-to-set-up environment.\n- [Reddit.com/r/dailyprogrammer](https://www.reddit.com/r/dailyprogrammer/) : interesting programming challenges where you can learn from looking at other's code , even if you are not able to solve code you can look at how others solved.\n- [Programming by Doing](http://programmingbydoing.com) : very good site for those who want to start with absolute basics\n- [CodeAbbey - a place where everyone can master programming](http://www.codeabbey.com) : Best place to begin with problems that start at the easiest and gradually increase difficulty with each problem.\n- [Exercism.io](http://exercism.io) : download and solve practice problems in over 30 different languages, and share your solution with others.\n- [InterviewBit | Coding Interview Questions](https://www.interviewbit.com) : Gamifies the experience of practicing for your interview and includes lots of sample problems to solve.\n- [karan/Projects-Solutions](https://github.com/karan/Projects-Solutions) : Solutions to most of the problems in the link above\n- [Lod - Cloud](http://lod-cloud.net) : The Linking Open Data cloud diagram\n- [Cave of programming](https://caveofprogramming.com) : Learn to program, Upgrade your skills.\n- [Vim adventures](https://vim-adventures.com/) : Learn VIM by playing\n- [Programming Tasks](http://rosettacode.org/wiki/Category:Programming_Tasks) : large collection of small programs\n- [Pramp](https://www.pramp.com) : It's your turn to be the interviewer. when done, click on the swap roles button on the left\n- [Paqmind](http://paqmind.com) : Guides and challenges to learn programming\n- [gitignore](https://www.gitignore.io/) : A collection of useful .gitignore templates for your project. Select from 442 Operating System, IDE, and Programming Language\n- [Codeacademy](https://www.codecademy.com) : Learn to code interactively, for free.\n- [treehouse](https://teamtreehouse.com) : The fast, easy, and affordable way to build your skills.\n- [GeeksforGeeks](https://practice.geeksforgeeks.org) : Learn to code, Study for placement, Do competitive coding.\n\n\n## CryptoCurrency\n- [Blockchain demo](https://blockchaindemo.io/) : A visual demo of Blockchain technology\n- [Coin demo](https://coindemo.io/) : CryptoCurrency demo\n- [Lopp Bitcoin Resources](https://lopp.net/bitcoin.html) : Some Bitcoin Resources\n- [Blockchain Basics](https://www.sitepen.com/blog/2017/09/21/blockchain-basics/) : Great introduction to blockchain\n- [GitCoin](https://gitcoin.co) : Gitcoin is the easiest way to monetize or incentivize work in Open Source Software.\n- [Lite Paper](https://litepaper.com/) : Cryptocurrencies & Blockchain made effortless\n\n\n## For those who want to start a small project but can't find the ideas\n- [freeCodeCamp | React project ideas](https://medium.freecodecamp.org/every-time-you-build-a-to-do-list-app-a-puppy-dies-505b54637a5d?gi=c786640fbd11) : 27 fun app ideas you can build while learning React.\n- [martyr2s-mega-project-ideas-list](http://www.dreamincode.net/forums/topic/78802-martyr2s-mega-project-ideas-list/) : contains about 125 project ideas from beginner to intermediate level.\n- [karan/Projects](https://github.com/karan/Projects) : a large collection of small projects for beginners with\n- [Wrong \"big projects\" for beginners](http://rodiongork.tumblr.com/post/108155476418/wrong-big-projects-for-beginners) : How to choose where to start\n- [vicky002/1000-Projects](https://github.com/vicky002/1000_Projects) : Mega List of practical projects that one can solve in any programming language!\n- [reddit.com/r/AppIdeas](https://www.reddit.com/r/AppIdeas/) : A place to discuss ideas for applications, for bored developers.\n- [reddit.com/r/SomebodyMakeThis](https://www.reddit.com/r/SomebodyMakeThis/) : A home for ideas by people who lack time, money, or skills.\n\n\n## General Coding advice\n- [10-ways-to-be-a-better-developer](https://stephenhaunts.files.wordpress.com/2014/04/10-ways-to-be-a-better-developer.png) : Ways to become a better dev!\n- [Code Review Best Practices](https://www.kevinlondon.com/2015/05/05/code-review-best-practices.html) : Kevin London's blog\n- [Design Patterns](https://sourcemaking.com/design_patterns) : Design Patterns explained in detail with examples.\n- [Develop for Performance](http://developforperformance.com) : High-performance computing techniques for software architects and developers\n- [How to become a programmer, or the art of Googling well](https://okepi.wordpress.com/2014/08/21/how-to-become-a-programmer-or-the-art-of-googling-well/) : How to become a programmer, or the art of Googling well\n- [How to escape tutorial purgatory as a new developer — or at any time in your career](https://medium.freecodecamp.org/how-to-escape-tutorial-purgatory-as-a-new-developer-or-at-any-time-in-your-career-e3a4b2384a40) : How to escape tutorial purgatory\n- [JS Project Guidelines](https://github.com/wearehive/project-guidelines) : A set of best practices for JavaScript projects.\n- [Learn to Code With Me](https://learntocodewith.me) : A comprehensive site resource by Laurence Bradford for developers who aims to build a career in the tech world\n- [Lessons From A Lifetime Of Being A Programmer](http://thecodist.com/article/lessons_from_a_lifetime_of_being_a_programmer) : The Codist Header Lessons From A Lifetime Of Being A Programmer \n- [Programming Principles](https://webpro.github.io/programming-principles/) : Categorized overview of Programming Principles & Patterns\n- [Software design pattern](https://en.wikipedia.org/wiki/Software_design_pattern) : The entire collection of Design Patterns.\n- [Things I Wish Someone Had Told Me When I Was Learning How to Code — Free Code Camp](https://medium.freecodecamp.com/things-i-wish-someone-had-told-me-when-i-was-learning-how-to-code-565fc9dcb329?gi=fc6d0a309be ) : What I’ve learned from teaching others\n- [What every computer science major should know](http://matt.might.net/articles/what-cs-majors-should-know/) : The Principles of Good Programming\n- [Working as a Software Developer](https://henrikwarne.com/2012/12/12/working-as-a-software-developer/) : Henrik Warne's blog\n- [The Open Web Application Security Project (OWASP)](https://www.owasp.org) : OWASP is an open community dedicated to enabling organizations to conceive, develop, acquire, operate, and maintain applications that can be trusted.\n\n\n## Coding Style\n- [Airbnb JS Style Guide](https://github.com/airbnb/javascript) : A mostly reasonable approach to JavaScript\n- [Airbnb Ruby Style Guide](https://github.com/airbnb/ruby) : A ruby style guide by Airbnb\n- [Ruby coding style guide](https://github.com/bbatsov/ruby-style-guide) : A community-driven Ruby coding style guide\n- [Angular 1 Style Guide](https://github.com/johnpapa/angular-styleguide/tree/master/a1) : Officially endorsed style guide by John Pappa\n- [CS 106B Coding Style Guide](http://stanford.edu/class/archive/cs/cs106b/cs106b.1158/styleguide.shtml) : must see for those who create spaghetti\n- [Debugging Faqs](http://www.umich.edu/~eecs381/generalFAQ/Debugging.html) : Check out how to debug your program\n- [Directory of CS Courses (many with online lectures)](https://github.com/prakhar1989/awesome-courses) : Another online CS courses\n- [Directory of Online CS Courses](https://github.com/ossu/computer-science) : Free online CS courses\n- [Good C programming habits. • /r/C_Programming](https://www.reddit.com/r/C_Programming/comments/1vuubw/good_c_programming_habits/) : C programming habits to adopt\n- [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html)\n- [How to Report Bugs Effectively](https://www.chiark.greenend.org.uk/~sgtatham/bugs.html) : Want to report a bug but you don't how? Check out this post\n- [What are some bad coding habits you would recommend a beginner avoid getting into?](https://www.reddit.com/r/learnprogramming/comments/1i4ds4/what_are_some_bad_coding_habits_you_would/) : Bad habits to avoid when you get start\n- [PEP8 - Style Guide for Python Code](https://www.python.org/dev/peps/pep-0008/) : Style Guide for Python Code\n- [Standard JS Style Guide](https://standardjs.com) : JavaScript style guide, with linter & automatic code fixer\n- [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html) : Google Python Style Guide\n- [Aurelia Style Guide](https://github.com/behzad888/Aurelia-styleguide) : An Aurelia style guide by Behzad Abbasi(Behzad888)\n- [Source Making ](https://sourcemaking.com/): Design Patterns & Refactoring\n\n## General Tools\n- [CodePad](https://codepad.remoteinterview.io/DDCUYLAEYS) : Quickly Conduct Coding Interviews and Phone Screen Interviews.\n- [CodePen](https://codepen.io) : Front End Developer Playground & Code Editor in the Browser\n- [Devicons](http://vorillaz.github.io/devicons/#/main) : Cheatsheet for devs icons\n- [regex101](https://regex101.com) : Online regex tester and debugger: PHP, PCRE, Python, Golang and JavaScript\n- [regexr](https://regexr.com) : Another online tool to learn, build & test Regular Expressions\n- [Wit AI](https://wit.ai) : Natural Language for Developers\n- [Seymour](https://harc.github.io/seymour-live2017) : Live Programming for the Classroom\n- [Code share](https://codeshare.io) : Share code in real-time with other developers\n- [Solid Tools for Developers](https://soliddevtools.com) : Online debugging tools for developers and system administrators\n- [OS Query](https://osquery.io) : Easily ask questions about your Linux, Windows, and macOS infrastructure\n- [LaunchPad](https://launchpad.graphql.com) : Appollo launchepad for testing GraphQl queries\n- [IDE Onlang](https://ide.onelang.io) : Write in one language and get the same result in other languages.\n- [PullRequest](https://www.pullrequest.com/) : Code review as a service from vetted, professional reviewers\n- [Extends Class](https://extendsclass.com/) : Online developer tools: REST and SOAP clients, SQLite browser, testers (Regex, XPath, JSONPath) and other tools (Encoders, Converters and formatters)\n- [EmailDrop](https://www.emaildrop.io/): Emaildrop is a free disposable email provider.\n\n\n## Bash and Shell scripting \n- [Advanced Bash-Scripting Guide](http://tldp.org/LDP/abs/html/) : An in-depth exploration of the art of shell scripting\n- [Bash Guide for Beginners](http://www.tldp.org/LDP/Bash-Beginners-Guide/html/) : Bash Guide for Beginners Machtelt Garrels\n- [Bash Programming](http://tldp.org/HOWTO/Bash-Prog-Intro-HOWTO.html) : by Mike G mikkey at dynamo.com.ar\n- [Bash Reference Manual](https://www.gnu.org/software/bash/manual/bashref.html) : Bash Reference Manual\n- [BashGuide](http://mywiki.wooledge.org/BashGuide) : BashGuide - Greg's Wiki\n- [Conquering the Command Line](http://conqueringthecommandline.com/book/frontmatter) : Unix and Linux Commands for Developers\n- [Airbon OS](https://www.airbornos.com) : Private Google Docs Alternative\n\n## Interview Preparation\n- [/r/cscareerquestions](https://www.reddit.com/r/cscareerquestions/comments/20ahfq/heres_a_pretty_big_list_of_programming_interview/) : Here's a pretty big list of programming interview questions I compiled while studying for big 4 interviews. I think you guys will find it useful!\n- [10 Frequently asked SQL Query Interview Questions](http://www.java67.com/2013/04/10-frequently-asked-sql-query-interview-questions-answers-database.html)\n- [A Collection of Quant Riddles With Answers](http://puzzles.nigelcoldwell.co.uk)\n- [Algorithm design canvas](https://www.hiredintech.com/algorithm-design)\n- [Aptitude Questions and Answers](http://www.indiabix.com) : Quant and aptitude preparation\n- [A site for technical interview questions, brain teasers, puzzles, quizzles](https://www.techinterview.org) : whatever the heck those are) and other things that make you think!\n- [BeehYve](https://www.beehyve.io) : Student crowdsourced resources for any topic\n- [Big collection of interview preparation links • /r/cscareerquestions](https://www.reddit.com/r/cscareerquestions/comments/2lzc4h/big_collection_of_interview_preparation_links/)\n- [Big-O Algorithm Complexity Cheat Sheet](http://bigocheatsheet.com/#)\n- [BIG O Misconceptions](http://ssp.impulsetrain.com/big-o.html)\n- [Bitwise tricks](https://gist.github.com/dideler/2365607)\n- [ChiperSoft/InterviewThis](https://github.com/Twipped/InterviewThis) : questions to ask during on a interview to know more about the company.\n- [Code Project](https://www.codeproject.com) : For those who code!\n- [Core Java Interview questions - Interview question on each topic](http://javahonk.com/core-java-interview-questions/)\n- [C PUZZLES, Some interesting C problems](http://www.gowrikumar.com/c/index.php)\n- [CS9: Problem-Solving for the CS Technical Interview](http://web.stanford.edu/class/cs9/)\n- [Delightful Puzzles](http://gurmeet.net/puzzles/)\n- [Determining the big-O runtimes of these different loops?](https://stackoverflow.com/questions/11094330/determining-the-big-o-runtimes-of-these-different-loops) : really good stackoverflow question that covers basics of calculating runtime complexity.\n- [five-essential-phone-screen-questions - steveyegge2](https://sites.google.com/site/steveyegge2/five-essential-phone-screen-questions)\n- [Freshers Interviews](http://placementsindia.blogspot.com)\n- [Gainlo](http://www.gainlo.co/#!/) : Mock interview from professionals \n- [GeeksforGeeks | A computer science portal for geeks](http://www.geeksforgeeks.org) : also subscribe to their feeds to get links to their new articles.\n- [github.com/odino/interviews](https://github.com/odino/interviews) : list of important questions for interview\n- [Give your résumé a face lift](http://www.lifeclever.com/give-your-resume-a-face-lift/)\n- [Here's How to Prepare for Tech Interviews • /r/cscareerquestions](https://www.reddit.com/r/cscareerquestions/comments/1jov24/heres_how_to_prepare_for_tech_interviews/)\n- [How to Answer \"Tell Me a Little About Yourself\" | The Art of Manliness](http://www.artofmanliness.com/2016/01/05/tell-me-a-little-about-yourself/)\n- [How to Answer the Toughest 40 Job Interview Questions | ICS Job Portal](http://www.icsjobportal.com/blog/job-interview-questions)\n- [How to Break Into the Tech Industry - a Guide to Job Hunting and Tech Interviews](http://haseebq.com/how-to-break-into-tech-job-hunting-and-interviews)\n- [How to interview](http://kelukelu.me/interview/index.html)\n- [How to prepare for an interview - 1](http://se7so.blogspot.com/2014/01/how-to-prepare-for-interview-1.html)\n- [IIT Delhi Placement Experience](http://placement-iit2013.blogspot.com)\n- [samwincott/Internship-Guide](https://github.com/samwincott/Internship-Guide) : An aggregrate of links related to internships.\n- [Interviewing At Jane Street](https://blog.janestreet.com/interviewing-at-jane-street/) : Interviewing At Jane Street\n- [Interview Archives - Java Honk](http://javahonk.com/category/interview/)\n- [Job Interview: How to Ace a Job Interview | The Art of Manliness](http://www.artofmanliness.com/2012/08/06/how-to-ace-a-job-interview/)\n- [Job interviews News, Videos, Reviews and Gossip - Lifehacker](https://lifehacker.com/tag/job-interviews)\n- [Job Interview Questions and Best Answers](https://www.thebalance.com/job-interview-questions-and-answers-2061204)\n- [kimberli/interviews](https://github.com/kimberli/interviews) : study sheet for Interview\n- [LeetCode](https://leetcode.com/) : A new way to learn.here you can prepare for your interview.\n- [Mission-peace/interview problems](https://github.com/mission-peace/interview/wiki) : A large collection of coding interview problems\n- [Pramp | A free on demand interview practice platform for Software Engineers](https://www.pramp.com/ref/gt1) : Practice coding interviews with real peers\n- [Problems | LeetCode OJ](https://leetcode.com/problemset/algorithms/) : Coding practice for interviews\n- [Programmer And Software Interview Questions Answers](http://www.programmerinterview.com)\n- [Reddit.com/user/ashish2199/m/puzzles](https://www.reddit.com/user/ashish2199/m/puzzles/) : Logic Puzzles\n- [SQL interview questions](https://www.jitbit.com/news/181-jitbits-sql-interview-questions/) : great SQL test\n- [SQL Joins explained using venn diagram](http://stevestedman.com/wp-content/uploads/VennDiagram1.pdf)\n- [svozniuk/java-interviews](https://github.com/svozniuk/java-interviews) : Java interview questions\n- [The 25 most difficult HR questions](http://www.datsi.fi.upm.es/~frosal/docs/25mdq.html)\n- [Top 10 Algorithms for Coding Interview](https://www.programcreek.com/2012/11/top-10-algorithms-for-coding-interview/) : Algorithms for Coding Interview\n- [Unix / Linux Cheat Sheet](http://cheatsheetworld.com/programming/unix-linux-cheat-sheet/)\n- [Unsolicited_advice_for_job_seekers_and_employers](https://www.reddit.com/r/india/comments/1clgdj/unsolicited_advice_for_job_seekers_and_employers/)\n- [Visualising Data Structures and Algorithms through Animation](https://visualgo.net/en)\n- [We Help Coders Get Hired](https://www.hiredintech.com/app) : website offering courses on system design, interview strategies, soft skills etc\n- [what_are_your_goto_questions_for_the_do_you_have?](https://www.reddit.com/r/cscareerquestions/comments/209rkq/what_are_your_goto_questions_for_the_do_you_have/)\n- [Why You Make Less Money • /r/cscareerquestions](https://www.reddit.com/r/cscareerquestions/comments/1b8wa3/why_you_make_less_money/)\n- [wu :: riddles(hard)](https://www.ocf.berkeley.edu/~wwu/riddles/hard.shtml) : logic puzzles and riddles\n\n\n## Documentaries\n- [Breaking the Code](https://ia801908.us.archive.org/27/items/youtube-S23yie-779k/) : Biography of Alan Turing\n- [Cracking The Code Interview](https://www.youtube.com/watch?v=4NIb9l3imAo) : Cracking the Code Interview\n- [Cracking the Coding Interview](https://www.youtube.com/watch?v=Eg5-tdAwclo) : Cracking the Coding Interview, Fullstack Speaker Series\n- [Download: The True Story of the Internet](https://www.youtube.com/playlist?list=PL_IlIlrxhtPMqW4b0-v8OgLvFZQes6SoZ) : Play-list of discovery channel documentary on browser wars, dot com bubble and more.\n- [Harvard CS50 - Asymptotic Notation (video)](https://www.youtube.com/watch?v=iOq5kSKqeR4) : Asymptotic Notation explained by Harvard\n- [How does CPU execute program (video)](https://www.youtube.com/watch?v=42KTvGYQYnA) : Take a look at how a CPU executes programs\n- [Machine Code Instructions (video)](https://www.youtube.com/watch?v=Mv2XQgpbTNE) : Code instructions\n- Machine that Changed the World - a very good documentary about history of computers\n - Part 1 is unavailable for free streaming due to widespread copyright claims.\n - [Part 2: Inventing the Future](https://www.youtube.com/watch?v=0iPiYxjsYKk)\n - [Part 3: The Paperback Computer](https://www.youtube.com/watch?v=d7DKVfOXr54)\n - [Part 4: The Thinking Machine](https://www.youtube.com/watch?v=enWWlx7-t0k)\n - [Part 5: The World at Your Fingertips](https://www.youtube.com/watch?v=fLLXiP7diEo)\n- [Mechanical Computer (All Parts)](https://www.youtube.com/watch?v=s1i-dnAH9Y4) : a very good video from 1950s explaining how mechanical computers used to work without all the modern day electronics.\n- [Project Code Rush](https://www.youtube.com/watch?v=a-49a_CjH0M) : The Beginnings of Netscape / Mozilla Documentary\n- [Revolution OS Linux Documentary](https://www.youtube.com/watch?v=4vW62KqKJ5A) : a film that traces the history of GNU, Linux, open source, and the free software movement.\n- [Teach Yourself Computer Science](https://teachyourselfcs.com) : Teach Yourself Computer Science\n- [The Code](https://www.youtube.com/watch?v=XMm0HsmOTFI) : Story of Linux documentary\n- [Triumph of the Nerds](https://www.youtube.com/playlist?list=PLn-Y3vvQbmHO5WUcBdIWqiUfNawhC1cn3) : Play-list\n- [The Internet's Own Boy](https://www.youtube.com/watch?v=9vz06QO3UkQ) : The Story of Aaron Swartz\n\n\n## MOOCs for learning something new\n- [Class Central](https://www.class-central.com) : a directory of 100,000+ student reviews of thousands of MOOCs.\n- [Classpert](https://classpert.com) : a website that gathers MOOCs and online courses from several providers, focusing on computer science and data science courses.\n- [Computer Science Resources](https://docs.google.com/spreadsheets/d/1BD8BJJUNaX63m2QmySWMGDp71nx4W4MyyiIBlfMoN3Q/htmlview?sle=true#) : list of MOOCs for autodidacts\n- [Coursera.org](https://www.coursera.org) : Take the world's best courses, online.\n- [CS50](https://www.youtube.com/user/cs50tv/videos) : A set of goods tutorials from cs50\n- [edX](https://www.edx.org) : Free Online Courses, Advance Your Career, Improve Your Life.\n- [Kadenze | Creative Programming](https://www.kadenze.com/courses?subjects%5B%5D=7) : Programming courses focused on art and creativity\n- [MIT OCW Electrical Engineering and Computer Science](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/)\n- [MOOC.fi](http://mooc.fi/english.html) : Free online courses from the University of Helsinki\n- [NPTEL](http://nptel.ac.in) : Free online courses by IIT with certificates\n- [prakhar1989/awesome-CS-courses](https://github.com/prakhar1989/awesome-courses/blob/master/README.md) : List containing large amount of CS courses\n\n\n## Sites related to your preferred programming language (For me C++)\n- [Best books for learning java must read](https://javahungry.blogspot.com/2014/02/best-books-for-learning-java-must-read.html) : Get basics of Java\n- [Bjarne Stroustrup's C++ Style and Technique FAQ](http://www.stroustrup.com/bs_faq2.html) : The C++ FAQ\n- [Bjarne Stroustrup's FAQ](http://www.stroustrup.com/bs_faq.html) : The C++ FAQ\n- [C++11 - the new ISO C++ standard](http://www.stroustrup.com/C++11FAQ.html) : The C++11 FAQ\n- [Compilers (video)](https://www.youtube.com/playlist?list=PLO9y7hOkmmSGTy5z6HZ-W4k2y8WXF7Bff) : A set of videos on how the GC works\n- [Deep Dive Java](https://www.infoq.com/presentations/garbage-collection-benefits) : Garbage Collection is Good!\n- [Free Online Chapters of Inside the Java Virtual Machine by Bill Venners](http://www.artima.com/insidejvm/ed2/index.html) : Java Corner\n- [How Garbage Collection Works](https://www.dynatrace.com/resources/ebooks/javabook/how-garbage-collection-works/) : Java memory management\n- [Implementation of Algorithms and Data Structures, Interview Questions and Answers](https://github.com/sherxon/AlgoDS)\n- [IntelliJ Keyboard Shortcuts](https://www.jetbrains.com/help/idea/keyboard-shortcuts-you-cannot-miss.html) : Keyboard shortcuts to enhance your productivity when working in IntelliJ.\n- [Java Corner at Artima.com](http://www.artima.com/java/index.html) : Java Corner at Artima.com\n- [Java Lecture Notes](http://www.cafeaulait.org/course/) : Java Student's Resource\n- [Java Off Heap](http://www.javaoffheap.com) : Java Off the Heap house\n- [Java Revisited](http://javarevisited.blogspot.com) : good for learning about Java Language and interview preparation.\n- [Java-source](http://www.java-source.net) : Java source\n- [Java Visualizer](http://www.cs.princeton.edu/~cos126/java_visualize/) : helps visualize references, values of variables, etc\n- [JournalDev - Java, Java EE, Android, Web Development Tutorials](https://www.journaldev.com) : Java, Java EE, Android, Web Development Tutorials\n- [Learning Java](http://chimera.labs.oreilly.com/books/1234000001805/index.html) : a free online textbook for learning Java\n- [Netbeans Keyboard Shortcuts](https://netbeans.org/project_downloads/usersguide/shortcuts-80.pdf) : Keyboard shortcuts to enhance your productivity when working in Netbeans.\n- [Search Open Source Java API](http://www.docjar.com) : view source of java library and learn how things are implemented.\n- [The C++ Programming Language](http://www.stroustrup.com/C++.html) : The C++ Programming Language.\n- [The Java Memory Model](http://www.cs.umd.edu/~pugh/java/memoryModel/): The Java Memory Model\n- [The Java™ Tutorials](https://docs.oracle.com/javase/tutorial/) : The best tutorials for Java.\n- [Understanding JVM Internals](http://www.cubrid.org/blog/understanding-jvm-internals) : Understanding JVM Internals\n- [what-is-garbage-collection](https://plumbr.eu/handbook/what-is-garbage-collection) : Demystify the garbage collection\n- [JavaWorld](https://www.javaworld.com) : Welcome to Javaworld\n- [XyzWs Java FAQs](http://www.xyzws.com/javafaq/page/1) : large collection of java interview questions\n- [JavatPoint](https://www.javatpoint.com/java-tutorial) : Best website to get a basic Java programming tutorial\n\n\n## Learn AI\n- [aima](http://aima.cs.berkeley.edu) : The leading textbook in Artificial Intelligence (4th most cited publication of the century). Includes Github repositories and more AI resources!\n- [fast.ai](http://course.fast.ai) : Free practical *deep learning* course for coders without grad-level maths!\n- [grakn.ai](https://grakn.ai) : The Database for AI\n- [Robots that learn](https://blog.openai.com/robots-that-learn/) : Robots that Learn\n- [Unsupervised Sentiment Neuron](https://blog.openai.com/unsupervised-sentiment-neuron/) : Unsupervised Sentiment Neuron\n- [What's the difference between AI- DP and ML?](https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/) : Difference artificial intelligence, machine-learning, deep-learning-ai\n- [TensorFlow](https://www.tensorflow.org) : An open-source software library for Machine Intelligence\n- [Scikit-learn](http://scikit-learn.org) : A Python module for machine learning build on top of SciPy\n- [DeepLearning.ai](https://www.deeplearning.ai) : Deep Learning course by Andrew Ng, Founder of coursera\n- [Coding the History of Deep Learning](http://blog.floydhub.com/coding-the-history-of-deep-learning/) : Coding the History of Deep Learning\n- [Serpent AI](https://github.com/SerpentAI/SerpentAI) : Game Agent Framework. Helping you create AIs / Bots to play any game you own! BETA\n- [Blog Floydhub](https://blog.floydhub.com/colorizing-b&w-photos-with-neural-networks/) : Colorizing B&W Photos with Neural Networks\n- [MLCOURSE.AI](https://mlcourse.ai/) : Open Machine Learning course by OpenDataScience\n\n\n## Seminar , research writing , talks etc\n- [Advice on Research and Writing](http://www.cs.cmu.edu/~mleone/how-to.html) : A collection of advice about how to do research and how to communicate effectively (primarily for computer scientists).\n- [PHD MS Articles](http://www.cse.iitd.ac.in/~srsarangi/articles.html) : articles and views\n- [Seminar and reports](https://www.cse.iitb.ac.in/~ranade/communicationskills.html) : Everyone must read this tiny book before writing the seminar report\n- [Latex reference](http://latex.knobs-dials.com) : Arbitrary reference\n- [Begin Latex in minutes](https://github.com/LewisVo/Begin-Latex-in-minutes) : Brief Intro to LaTeX for beginners that helps you use LaTeX with ease\n- [Lshort](https://tobi.oetiker.ch/lshort/lshort.pdf) : The Not So Short Introduction to LATEX2ε\n\n\n## Everything in one place\n- [reddit.com/user/ashish2199/m/cs_student_subs](https://www.reddit.com/user/ashish2199/m/cs_student_subs/) : a multisubreddit of all subreddits of topics related to computer science and programming.\n- [Websites a programmer should visit](https://www.quora.com/What-are-the-best-websites-a-programmer-should-visit/answer/Ashish-Padalkar?srid=OH96) : Response on Quora by ashish2199\n- [Rico's cheatsheets](https://devhints.io) : A set of good cheatsheets\n- [API Documentation](http://devdocs.io) : A one-place well known API Documentation with searchable interface\n- [MDN](https://developer.mozilla.org/en-US/) : A place with all the documentation of the web standards\n- [cheat.sh](https://github.com/chubin/cheat.sh) : `curl cheat.sh` — the only cheat sheet you need — instant answers on programming questions with `curl`\n\n\n## YouTube Channels\n- [C++Now (BoostCon)](https://www.youtube.com/channel/UC5e__RG9K3cHrPotPABnrwg) : C++Now (previously BoostCon) conference\n- [code::dive conference](https://www.youtube.com/channel/UCU0Rt8VHO5-YNQXwIjkf-1g) : code::dive conference organized by NOKIA Wrocław Technology Center\n- [Coding Blocks](https://www.youtube.com/user/codingblocks) : Tutorials, how to's, tips and tricks\n- [Computerphile](https://www.youtube.com/user/Computerphile/videos) : Must watch for every CS student\n- [ComputerHistory](https://www.youtube.com/user/ComputerHistory/videos) : for those who like to know how we reached where we are.\n- [CppCon](https://www.youtube.com/user/CppCon/videos?shelf_id=0&view=0&sort=dd) : C++ Conference\n- [Facebook Developers](https://www.youtube.com/user/FacebookDevelopers/videos)\n- [Google Developers](https://www.youtube.com/user/GoogleDevelopers/videos)\n- [GoogleTechTalks](https://www.youtube.com/user/GoogleTechTalks/videos) : videos on trending topics and cool stuff happening in the tech industry.\n- [Gynvael Coldwin](https://www.youtube.com/user/GynvaelEN) : Awesome reverse engineering and hacking(CTF) videocasts. Every wednesday are new live streams.\n- [HowToBecomeTV](https://www.youtube.com/user/HowToBecomeTV/videos) : contains good interviews of developers and people related to tech industry.\n- [Java](https://www.youtube.com/user/java/videos) : talks related to java\n- [JavaOne](https://www.youtube.com/channel/UCdDhYMT2USoLdh4SZIsu_1g/videos) : Java Conference\n- [javidx9](https://www.youtube.com/channel/UC-yuWVUplUJZvieEligKBkA/videos) : Game and graphics tutorials\n- [Meeting C++ YT Kanalseite](https://www.youtube.com/user/MeetingCPP/videos) : Talks on C++\n- [MIT OpenCourseWare](https://www.youtube.com/user/MIT/) : MIT OpenCourseWare for learning in depth algorithms, data structures and computer engineering\n- [Netflix UI Engineering](https://www.youtube.com/channel/UCGGRRqAjPm6sL3-WGBDnKJA/videos) : great videos to watch for web developers, mobile developers and those interested in some of Netflix's tech stack\n- [O'Reilly](https://www.youtube.com/user/OreillyMedia/videos) : interviews and talks of world's best technical writers.\n- [Placement Grid](https://www.youtube.com/user/PlacementGrid/videos) : Interview and campus placement experience\n- [Scott Meyers: Past Talks](http://www.aristeia.com/presentations.html)\n- [Siraj Raval](https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A) : Artificial Intelligence and deep learning tutorials videos\n- [ThinMatrix](https://www.youtube.com/user/ThinMatrix/videos) : blogs and tutorials developer making a 3d game in Java using opengl\n- [thoughtbot](https://www.youtube.com/user/ThoughtbotVideo/videos) : talks on various topics\n- [Traversy Media](https://www.youtube.com/user/TechGuyWeb/videos) :Web development and programming\n- [yegor256](https://www.youtube.com/user/technoparkcorp/videos)\n- [GOTO Conference](https://www.youtube.com/user/GotoConferences) : tech talks from the GOTO Conference by Developers for Developers\n- [freeCodeCamp](https://www.youtube.com/channel/UC8butISFwT-Wl7EV0hUK0BQ) : freecodecamp youtube channel\n- [Bo Qian](https://www.youtube.com/channel/UCEOGtxYTB6vo6MQ-WQ9W_nQ) : Learn advanced c++\n- [Geeksforgeeks](https://www.youtube.com/channel/UC0RhatS1pyxInC00YKjjBqQ/videos) : geeksforgeeks youtube\n- [Hacker earth](https://www.youtube.com/channel/UCYU6nvKyRYnE5kiG9JXkXpA) : Hacker earth youtube\n- [Hak5](https://www.youtube.com/user/Hak5Darren) : Put together by a band of IT ninjas, security professionals and hardcore gamers, Hak5 isn't your typical tech show. We take on hacking in the old-school sense.\n- [Khan Academy](https://www.youtube.com/channel/UC4a-Gbdw7vOaccHmFo40b9g) : Khan Academy youtube\n- [LearnCode.academy](https://www.youtube.com/channel/UCVTlvUkGslCV_h-nSAId8Sw) : 100% FREE Web Development tutorials, web site design tutorials and more. Including, but not limited to: HTML, CSS, JavaScript, CSS Layouts, Responsive Design, React.js, Node.js, Angular.js, Docker, Dev\n- [Rachit Jain](https://www.youtube.com/channel/UC9fDC_eBh9e_bogw87DbGKQ/featured) : competetive programming\n- [sentdex](https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ) : Python Programming tutorials, going further than just the basics. Learn about machine learning, finance, data analysis, robotics, web development, game development and more.\n- [Steve Griffith](https://www.youtube.com/channel/UCTBGXCJHORQjivtgtMsmkAQ) : The videos on this channel are largely about web design & development with a good dose of mobile development thrown in just for fun. \n- [The Coding Train](https://www.youtube.com/channel/UCvjgXvBlbQiydffZU7m1_aw) : In this YouTube channel I publish \"creative coding\" video tutorials every week. Subjects covered range from the basics of programming languages like JavaScript (with p5.js) and Java (with Processing) to generative algorithms like physics simulation, computer vision, and data visualization.\n- [TheHappieCat](https://www.youtube.com/channel/UCBsuOBu-dxj5bx1KMgmar5g) : Videos on technology, storytelling, and being happie.\n- [Tutorials Point (India) Pvt. Ltd.](https://www.youtube.com/channel/UCVLbzhxVTiTLiVKeGV7WEBg) : Tutorials Point originated from the idea that there exists a class of readers who respond better to online content and prefer to learn new skills at their own pace from the comforts of their drawing rooms. We bring simple to understand Text and Video tutorials at zero cost and cover many subjects including IT, Engineering, MBA, MCA, Management, Various Exams Preparation and Academic subjects. If you are really not extravagant and willing to save your money and time then we recommend you to try our tutorials.experience\n- [Udacity](https://www.youtube.com/channel/UCBVCi5JbYmfG3q5MEuoWdOw) : Udacity, a pioneer in online education, is building \"University by Silicon Valley\", a new type of online university\n- [V. Anton Spraul](https://www.youtube.com/channel/UCLEMTlEe5RE04EoULMHWEEQ) : Videos for people who want to understand software better, including explanations of how software performs some of its most important jobs and tools programmers can use to to solve programming problems.\n- [xdadevelopers](https://www.youtube.com/channel/UCk1SpWNzOs4MYmr0uICEntg) : You have found the world-famous XDA-Developers.com YouTube Channel, known as XDA TV. We cover Android and Windows Mobile from HTC, Samsung, Motorola, Sony, and more. Learn about rooting your Android, how to build applications, and more.\n- [KudVenkat](https://www.youtube.com/user/kudvenkat/) : Microsoft .NET technologies and SQL Server tutorials.\n- [The Net Ninja](https://www.youtube.com/channel/UCW5YeuERMmlnqo4oq8vwUpg): Web development tutorials\n\n\n## Good Articles\n- [40 Keys Computer Science Concepts Explained In Layman’s Terms](http://carlcheo.com/compsci)\n- [A Gentle Introduction To Graph Theory](https://dev.to/vaidehijoshi/a-gentle-introduction-to-graph-theory)\n- [A programmer friendly language that compiles to Lua.](http://moonscript.org)\n- [A Software Developer’s Reading List](https://stevewedig.com/2014/02/03/software-developers-reading-list/) : Some good books and links in there.\n- [Code a TCP/IP stack](http://www.saminiir.com/lets-code-tcp-ip-stack-5-tcp-retransmission/) : Let's code a TCP/IP stack, 5: TCP Retransmission\n- [Codewords.recurse](https://codewords.recurse.com/issues/four/the-language-of-choice) : The language of choice\n- [Data structure and Algorithms](https://techiedelight.quora.com/500-Data-Structures-and-Algorithms-practice-problems-and-their-solutions) : List of some algorithms and datastructure with their solutions.\n- [Dive into the byte code](https://www.wikiwand.com/en/Java_bytecode)\n- [Expectations of a Junior Developer](http://blog.thefirehoseproject.com/posts/expectations-of-a-junior-developer/)\n- [Linux Inside](https://0xax.gitbooks.io/linux-insides/content/Booting/linux-bootstrap-1.html)\n- [List of algorithms](https://www.wikiwand.com/en/List_of_algorithms)\n- [Step by Step Guide to Database Normalization](https://www.databasestar.com/normalization-in-dbms/): A guide to database normalization.\n- [The Key To Accelerating Your Coding Skills](http://blog.thefirehoseproject.com/posts/learn-to-code-and-be-self-reliant/)\n- [Unicode](https://www.joelonsoftware.com/2003/10/08/the-absolute-minimum-every-software-developer-absolutely-positively-must-know-about-unicode-and-character-sets-no-excuses/)\n- [We are reinventing the retail industry through innovative technology](http://multithreaded.stitchfix.com)\n- [What every programmer absolutely, positively needs to know about encodings and character sets to work with text](http://kunststube.net/encoding/)\n- [What every programmer should know about memory - PDF](http://futuretech.blinkenlights.nl/misc/cpumemory.pdf)\n- [Why fast pages are important](https://fly.io/articles/why-fast-pages-are-important/) : Why App Speed Matters, Revenue\n- [qotoqot - improving-focus](https://qotoqot.com/blog/improving-focus/) : How I got to 200 productive hours a month\n- [Pixel Beat - Unix](http://www.pixelbeat.org/docs/unix-parallel-tools.html) : Parallel processing with unix tools\n- [Learning Vim](https://hackernoon.com/learning-vim-what-i-wish-i-knew-b5dca186bef7) : What I Wish I Knew\n- [Write a Kernel](http://arjunsreedharan.org/post/82710718100/kernel-101-lets-write-a-kernel) : Kernel 101 – Let’s write a Kernel\n- [Learning JavaScript Design Patterns](https://addyosmani.com/resources/essentialjsdesignpatterns/book/) : online version of the Learning JavaScript Design Patterns published by O'Reilly, released by the author Addy Osmani under CC BY-NC-ND 3.0\n- [Working with Webhooks](https://requestbin.com/blog/working-with-webhooks/) : a comprehensive guide on webhooks\n\n## Podcasts\n- [Coding Blocks](http://www.codingblocks.net) : A podcast covering topics such as best programming practices, design patterns, coding for performance, object oriented coding, database design and implementation, tips, tricks and a whole lot of other things.\n- [Developer On Fire](http://developeronfire.com/episodes) : A podcast that shares the humanity of developers and tells stories of some of the amazing people in software, hosted by Dave Rael.\n- [Developer Tea](https://spec.fm/podcasts/developer-tea) : A podcast for developers designed to fit inside your tea break.\n- [Front End Happy Hour](http://frontendhappyhour.com) : A podcast featuring a panel of Software Engineers from Netflix, Evernote, Atlassian & LinkedIn talking over drinks about all things Front End development.\n- [Full Stack Radio](http://www.fullstackradio.com) : Everything from product design and user experience to unit testing and system administration.\n- [Groovy Podcast](http://groovypodcast.podbean.com) : A podcast dedicated to the Groovy programming language and its ecosystem.\n- [IPhreaks](https://devchat.tv/iphreaks) : A weekly group discussion about iOS development and related technology by development veterans. We discuss Apple, tools, practices, and code.\n- [JavaScript Jabber](https://devchat.tv/js-jabber) : A weekly discussion about JavaScript, front-end development, community, careers, and frameworks.\n- [Learn To Code With Me Podcast](https://learntocodewith.me/podcast/) : A Season by season of tech podcast episodes by Laurence Bradford with topics ranging from Career in Tech to lessons in doing tech business\n- [MS Dev Show](http://msdevshow.com) : Jason Young and Carl Schweitzer talk about the latest in developer news covering topics such as the Azure cloud, Windows, Windows Phone, Visual Studio, and cross-platform development using the Microsoft platform.\n- [React Native Radio](https://devchat.tv/react-native-radio) : A weekly discussion of the tools, techniques, and technologies used to build mobile applications with JavaScript and React.\n- [Soft Skills Engineering](https://softskills.audio/) : A weekly advice podcast for software developers about non-technical topics.\n- [Software Engineering Daily](https://softwareengineeringdaily.com) : A daily technical interview about software topics.\n- [Software Engineering Radio](http://www.se-radio.net) : A podcast targeted at the professional software developer. The goal is to be a lasting educational resource, not a newscast.\n- [Syntax](https://syntax.fm) : A Tasty Treats Podcast for Web Developers by Wes Bos & Scott Tolinski.\n- [The Bike Shed](http://bikeshed.fm) : Guests discuss their development experience and challenges with Ruby, Rails, JavaScript, and others.\n- [The Changelog](https://changelog.com/podcast) : A weekly conversation that gets to the heart of open source technologies and the people who create them.\n- [The Cynical Developer](https://cynicaldeveloper.com) : A podcast that aims to help you to improve your development knowledge and career, through explaining the latest and greatest in development technology and providing you with what you need to succeed as a developer. Covering Desktop, web and mobile development, mainly around the .Net Stack but often looking into other software and frameworks.\n- [Blockchain Insider by 11:FS](blockchain.global/blockchain-innovation) : Podcast to learn about the Blockchain Technology\n- [Unchained](unchainedpodcast.co) Podcast to learn about the Blockchain Technology\n- [Talk python to me](https://talkpython.fm/) Podcast to learn about Python through interviews and discussions \n- [Python bytes](https://pythonbytes.fm) Podcast to learn about the latest happenings and trends in Python\n\n\n## Building a Simple Compiler/Interpreter\n- [:snowman: Possibly the smallest compiler ever](https://github.com/thejameskyle/the-super-tiny-compiler) : This is an ultra-simplified example of all the major pieces of a modern compiler written in easy to read JavaScript.\n- [Awesome Compilers](http://aalhour.com/awesome-compilers/) : Curated list of awesome resources on Compilers, Interpreters and Runtimes.\n- [Growing a compiler](http://www.cs.dartmouth.edu/~mckeeman/cs48/mxcom/gem/html/GrowingCompiler.html) : Learn how to grow a compiler\n- [Let’s Build A Simple Interpreter. Part 1.](https://ruslanspivak.com/lsbasi-part1/) : Try to demystify compilers by building one\n- [Resources for Amateur Compiler Writers](http://c9x.me/compile/bib/) : Resources for Amateur Compiler Writers\n- [Structure and Interpretation of Computer Programs](https://sarabander.github.io/sicp/html/index.xhtml) : Structure and Interpretation of Computer Programs\n- [Writing My First Compiler](https://dev.to/fcpauldiaz/writing-my-first-compiler) : Write out your first compiler\n- [An Intro to Compilers](https://nicoleorchard.com/blog/compilers) : How to Speak to Computers, Pre-Siri\n- [Write your own compiler](http://staff.polito.it/silvano.rivoira/HowToWriteYourOwnCompiler.htm) : How to write your own compiler\n- [Crafting Interpreters](http://craftinginterpreters.com/) A handbook for writing interpreters, first implementing a tree walking interpreter and later a bytecode virtual machine\n\n\n\n## Tutorials\n- [A Hacker's Guide to Git](https://wildlyinaccurate.com/a-hackers-guide-to-git/) : for those wanting to learn git with a solid foundation\n- [Best Of - Gustavo Duarte](http://duartes.org/gustavo/blog/best-of/) : contains articles on various topics\n- [CMSI 281: Data Structures](http://cs.lmu.edu/~ray/classes/dsa/) : light weight introduction to DS\n- [Collecting all the cheat sheets](http://overapi.com) : cheat sheets for lots of programming languages\n- [C Programming](http://users.cs.cf.ac.uk/Dave.Marshall/C/CE.html)\n- [Programming Community Curated C++ Resources](https://hackr.io/tutorials/learn-c-plus-plus) : resources recommended by developers\n- [Deep C](https://www.slideshare.net/olvemaudal/deep-c) : very good presentation on C language\n- [Design Patterns: Elements of Reusable Object-Oriented Software](https://www.amazon.com/Design-Patterns-Elements-Reusable-Object-Oriented/dp/0201633612) : aka the \"Gang Of Four\" book, or GOF\n- [Dynamic programming - PrismoSkills](http://prismoskills.appspot.com/lessons/Dynamic_Programming/Chapter_01_-_Introduction.jsp) : very good resource if want to learn how to solve DP problems.\n- [Git from the inside out](https://maryrosecook.com/blog/post/git-from-the-inside-out)\n- [Head First Design Patterns](https://www.amazon.com/Head-First-Design-Patterns-Brain-Friendly/dp/0596007124)\n- [How to Program in C++](http://cs.fit.edu/~mmahoney/cse2050/how2cpp.html) : Good resource for revising C++ topics and STL\n- [http://www.mysqltutorial.org/](http://www.mysqltutorial.org)\n- [indradhanush tutotials](https://indradhanush.github.io/blog/writing-a-unix-shell-part-3/) : Writing a Unix Shell\n- [Introduction to C Programming](http://www.le.ac.uk/users/rjm1/cotter/index.htm)\n- [Learn UNIX in 10 minutes](http://freeengineer.org/learnUNIXin10minutes.html)\n- [Learning the shell.](http://linuxcommand.org)\n- [Linux Journey](https://linuxjourney.com) : good site for learning linux\n- [Linux Tutorial](https://ryanstutorials.net/linuxtutorial/) : good resource for learning Linux\n- [More about Github-flavored markdown](https://guides.github.com/features/mastering-markdown/)\n- [MySQL Essentials](http://www.techotopia.com/index.php/MySQL_Essentials)\n- [Open Data Structures](http://opendatastructures.org) : Excellent resource for learning about DS and algos, provides code in various languages C++, Java and pseudocode.\n- [OS Course Notes](https://www2.cs.uic.edu/~jbell/CourseNotes/OperatingSystems/) : Chapter wise course notes according to Galvin's book\n- [Programming, Web Development, and DevOps news, tutorials and tools for beginners to experts](https://dzone.com)\n- [SQL (Structured Query Language) in one page : SQL.SU](http://www.cheat-sheets.org/sites/sql.su/) : a very good SQL cheat sheet\n- [Subtle | Poor Man's CI](https://www.subtle.press/course/poor-mans-ci) : Learn how continuous integration platforms work under the hood, by building one of your own on top of git with Node.js\n- [TCP/IP Illustrated Series](https://en.wikipedia.org/wiki/TCP/IP_Illustrated)\n- [The Bash Guide](http://guide.bash.academy) : very good guide for learning the Bash Shell\n- [The Descent to C](https://www.chiark.greenend.org.uk/~sgtatham/cdescent/) : for those moving to C from some higher programming language like java or python.\n- [The Linux Command Line: A Complete Introduction](https://www.amazon.com/Linux-Command-Line-Complete-Introduction/dp/1593273894)\n- [The Unix Programming Environment](http://product.half.ebay.com/The-UNIX-Programming-Environment-by-Brian-W-Kernighan-and-Rob-Pike-1983-Other/54385&tg=info)\n- [TopCoder Tutorials](https://www.topcoder.com/community/data-science/data-science-tutorials/)\n- [Tutorialspoint](https://www.tutorialspoint.com) : Text and Video Tutorials for UPSC, IAS, PCS, Civil Services, Banking, Aptitude, Questions, Answers, Explanation, Interview, Entrance, Exams, Solutions\n- [UNIX and Linux System Administration Handbook, 4th Edition](https://www.amazon.com/UNIX-Linux-System-Administration-Handbook/dp/0131480057)\n- [VimTutor+](https://vimtutorplus.herokuapp.com/exercise/1) : Learn VIM from the browser.\n- [W3Schools Online Web Tutorials](https://www.w3schools.com)\n- [Unix Shell](https://www.dartmouth.edu/~rc/classes/ksh/print_pages.shtml) : Unix shell scripting with ksh/bash\n- [Snap SVG](http://snapsvg.io) : The JavaScript SVG library for the modern web\n- [vim.rtorr](https://vim.rtorr.com) : Vim Cheat Sheet\n- [Open Vim](http://www.openvim.com/tutorial.html) : Interactive Vim tutorials\n- [Algorithm Using Dynamic Programming and A](http://thume.ca/2017/06/17/tree-diffing/) : Designing a Tree Diff Algorithm Using Dynamic Programming and A*\n- [Learn Python](https://www.learnpython.org) : Free Interactive Python Tutorial\n- [C++17](https://www.viva64.com/en/b/0533/) : A guide of C++17\n- [The Bash Academy](http://www.bash.academy) : The Bash Academy is an initiative to promote the bash shell language and educate people on its use.\n- [Learn Shell Programming](http://learnshell.org) : This website is intended for everyone who wishes to learn programming with Unix/Linux shell interpreters.\n- [Java tutorial](https://hackr.io/tutorials/learn-java) : A programming community & a great place to find the best online programming courses and tutorials.\n- [Explain Shell](https://explainshell.com/): Match command-line arguments to their help text\n- [Speaking io](https://speaking.io/) : Tips for public speaking\n\n## Watch others code\n- [Education Ecosystem](https://www.education-ecosystem.com) : screencast of people building application, websites, games, etc.\n- [Twitch.tv](https://www.twitch.tv/directory/game/Science%20%26%20Technology) : The programming community of twitch.\n\n\n## What should a programmer know\n- [GitHub.com Build software better, together](https://github.com) : Place to showcase your project and collaborate with others. (Must know Git in order to use it effectively)\n- [Gitlab offers free unlimited (private) repositories and unlimited collaborators](https://about.gitlab.com)\n- [Programmer Competency Matrix](http://sijinjoseph.com/programmer-competency-matrix/) : article for knowing what our level as a programmer is.\n\n\n## Competitive programming\n- [Topic Wise Problem For Competitive Programmer](https://a2oj.com/categories) : Topic wise Practise Problem\n- [Archived Problems - Project Euler](https://projecteuler.net/archives) : Problems Archives\n- [Art of Problem Solving](https://artofproblemsolving.com) : Is math class too easy for you? You've come to the right place!\n- [CodeChef](https://www.codechef.com) : The only programming contests Web 2.0 platform\n- [Codefights](https://codefights.com) : Test your coding skills\n- [Codeforces](http://codeforces.com) : Programming Competition,Programming Contest,Online Computer Programming\n- [Codewars](https://www.codewars.com) : Rank up by completing code kata\n- [Codility](https://codility.com) : Verify and improve coding skills\n- [Codingame](https://www.codingame.com/start) : Learn coding through games and challenges!\n- [Facebook Hacker Cup](https://www.facebook.com/hackercup/) : Facebook's Programming Contest, past problems solutions and FAQ\n- [Google Code Jam Practice and](https://code.google.com/codejam/past-contests) : past contest problems for practice\n- [HackerEarth - Programming challenges and Developer jobs](https://www.hackerearth.com)\n- [HackerRank](https://www.hackerrank.com) : Practice coding. Compete. Find jobs.\n- [PKU ACM ICPC Practice problems](http://poj.org/problemlist) : Judge online for ACMACPC\n- [Sphere Online Judge (SPOJ)](http://www.spoj.com) : Become a true programming master Learn how to code and build efficient algorithms\n- [Topcoder](https://www.topcoder.com) : Deliver Faster through Crowdsourcing\n- [URI Online Judge](https://www.urionlinejudge.com.br/judge/en/register) : Practice coding, Compete and be a better coder.\n- [UVa Online Judge](https://uva.onlinejudge.org) : hundreds of problems supporting multiple languages.\n- [WakaTime](https://wakatime.com) : leaderboards of coding metrics collected via editor plugins\n\n\n## Computer Books\n- [Become a Programmer, Motherfucker (list of books)](http://programming-motherfucker.com/become.html) : Exhaustive list of books from Zed A. Shaw.\n- [Best books for GATE CSE](http://gatecse.in/best-books-for-gatecse/)\n- [cses.fi/book.html](https://cses.fi/book.html)\n- [github.com/vhf/free-programming-books](https://github.com/EbookFoundation/free-programming-books/blob/master/free-programming-books.md) : More than 500 free ebooks on almost any language you can think of\n- [GitBook](https://www.gitbook.com) : GitBook helps your team write, collaborate and publish content online.\n- [Data Science course](https://jakevdp.github.io/PythonDataScienceHandbook/) : Python Data Science Handbook\n- [Goal Kicker](https://goalkicker.com) : Programming Notes for Professionals books\n- [The GraphQL Guide](https://graphql.guide) : The complete guide to GraphQL, the new REST ✨\n\n\n## Video Tutorials\n- [codedamn](https://www.youtube.com/channel/UCJUmE61LxhbhudzUugHL2wQ/videos) : front end web dev tutorials\n- [Code School](https://www.codeschool.com) : A PluralSight Company and an Interactive learning destination for aspiring and experienced Developers\n- [CodingMadeEasy](https://www.youtube.com/user/CodingMadeEasy/videos) : C++ tutorials\n- [CS1: Higher Computing - Richard Buckland UNSW](https://www.youtube.com/playlist?list=PL6B940F08B9773B9F) : a very good introductory CS course\n- [Derek Banas](https://www.youtube.com/user/derekbanas/videos) : good quality tutorials\n- [Design and Analysis of Algorithms](http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=IntroToAlgorithms)\n- [DevTips](https://www.youtube.com/user/DevTipsForDesigners/videos) : web dev tutorials\n- [FreeCourses](https://freecourses.github.io) : Free courses about programming\n- [Kathryn Hodge](https://www.youtube.com/channel/UC4DwZ2VXM2KWtzHjVk9M_xg/videos) : Has good videos for beginners\n- [mycodeschool](https://www.youtube.com/user/mycodeschool/videos) : Data structures and algorithms tutorials\n- [Pluralsight](https://www.pluralsight.com) : Learn Software Development, DevOps and Data Science through multiple short courses\n- [thenewboston](https://www.youtube.com/user/thenewboston/videos) : good but with too much talk as compared to actual content\n- [Tushar Roy](https://www.youtube.com/user/tusharroy2525/videos) : Algorithm and Data structure tutorial by an Indian Youtuber.\n- [Vim Tutorial Videos - Flarfnoogins](http://derekwyatt.org/vim/tutorials/index.html) : good video tutorial for learning vim\n- [XDA-University - Helping You Learn Android Development](https://forum.xda-developers.com/general/xda-university)\n- [Khan Academy](https://www.khanacademy.org/computing/computer-science) : learn about computer science for free\n- [Functional programming](https://www.youtube.com/watch?v=1PhArSujR_A) : John Carmack on Functional Programming (2013)\n- [Video about vims](https://vimeo.com/album/2838732) : A serie of tutorials about Vim\n\n\n## Online Compiler and Sharing Code snippets\n- [CodePad](https://codepad.remoteinterview.io) : Code editor to try, test and run 25+ languages\n- [Codesandbox.io](https://codesandbox.io) : CodeSandbox makes it easier to create, share and reuse React projects with others.\n- [Godbolt.org](https://godbolt.org) : Excellent tool for exploring the assembly output of different compilers with and without optimization.\n- [Ideone.com](https://ideone.com) : online compiler and debugging tool for more than 60 programming languages\n- [JSFiddle](https://jsfiddle.net) : Test your JavaScript, CSS, HTML or CoffeeScript with online code editor\n- [JSBin](https://jsbin.com/) : Front end playground, Output is not framed, so it allow you to share those snippet that will break inside iframe.\n- [Pastebin.com](https://pastebin.com) : Pastebin can store texts like code, notes, and snippets online for a set period of time which can be shared instantly.\n- [C9.io](https://c9.io) : Your development environment, in the cloud\n- [Github Gist](https://gist.github.com) : Instantly share code, notes, and snippets.\n- [Coder](https://coder.com) : A Web-based development environment using Visual Studio Code as code editor \n\n\n## Blogs of Developers\n- [Algo-Geeks](http://algo-geeks.blogspot.com) : Programming Puzzles, Math Tricks, Algorithms etc\n- [Amit Merchant](https://www.amitmerchant.com) : Tutorials, tips & tricks and rants about programming and design.\n- [Andy Heathershaw](https://www.andyheathershaw.uk) : Personal website and blog of software developer Andy Heathershaw\n- [Antonio081014's Algorithms Codes](http://code.antonio081014.com) : The world is under the RULE. \n- [Archives — Ask a Manager](http://www.askamanager.org/archives) : HR related stuff\n- [Armin Ronacher's Thoughts and Writings](http://lucumr.pocoo.org) : blog on Python and open source\n- [blog.might.net](http://matt.might.net/articles/) : the blog of might dot net\n- [Brendon Gregg - Linux Kernel Dev](http://www.brendangregg.com) : the blog of Brendon D. Gregg\n- [Clean Coder Blog](http://blog.cleancoder.com) : blog of author of book \"Clean Code\"\n- [CodeAhoy](https://codeahoy.com) : Blog on software and human factors. 100% Tested on Humans.\n- [CoderGears Blog Insights from](http://www.codergears.com/Blog/) : the CoderGears Team\n- [Coding Geek - A blog about IT, programming and Java](http://coding-geek.com) : A blog about IT, programming and Java\n- [Coding Horror](https://blog.codinghorror.com) : one the best coding blog\n- [CSE Blog](http://www.cseblog.com) : quant, math, computer science puzzles\n- [Daedtech.com](https://www.daedtech.com) : Stories about software\n- [Dan Dreams of Coding](https://dandreamsofcoding.com)\n- [Daniel Lemire's Blog](https://lemire.me/blog/) : Daniel Lemire's blog\n- [Eli Bendersky](http://eli.thegreenplace.net) : everything from Python to LLVM\n- [Geek Land](https://avidullu.wordpress.com) : My precious collectibles\n- [HackerEarth Blog](http://blog.hackerearth.com) : The hackerearth blog\n- [IT Enthusiast](http://rodiongork.tumblr.com) : IT Enthusiast\n- [Joel on Software](https://www.joelonsoftware.com) : The blog of the CEO of StackOverflow\n- [Late Developer](https://latedev.wordpress.com) : Random thoughts of an old C++ guy\n- [1ucasvb's laboriginal math and physics visualization](http://1ucasvb.tumblr.com) : Lucas Vieira Barbosa's lab original math and physics visualization\n- [Math ∩ Programming](https://jeremykun.com) : Math ∩ Programming\n- [My Tech Interviews](http://www.mytechinterviews.com) : PREPARE FOR A TECHNICAL INTERVIEW\n- [Paul Graham Essays](http://www.paulgraham.com/articles.html) : Paul Grahan Essays\n- [Programming Blog](http://www.yegor256.com) : programming blog of Yegor Bugayenko\n- [Programming in the 21st Century](http://prog21.dadgum.com) : programming in the twenty-first century\n- [rudhakar Rayavaram](http://sudhakar.online) : Sudhakar Rayavaram Blog's\n- [Runhe Tian Coding Practice](https://tianrunhe.wordpress.com) : Technical interview questions from Apple, Google, Facebook, Amazon and Microsoft\n- [Small Programming Challenges and Puzzles](https://www.nayuki.io/category/programming) : Project Nayuki\n- [stevehanov.ca](http://stevehanov.ca/blog/) : I know how to make and sell software online, and I can share my tips with you.\n- [Takipi Blog](http://blog.takipi.com) : mainly focuses on Java and JVM languages\n- [XDA - Android Developer Forum](https://forum.xda-developers.com) : Android Open Source Developers Forum\n- [The Net Ninja](https://www.thenetninja.co.uk/): Web development tutorials\n\n## For improving your English\n- [Englishclub.com/learn-english](https://www.englishclub.com/learn-english.htm)\n- [Guide to Grammar and Writing](http://grammar.ccc.commnet.edu/grammar/) : for those who want to improve their english language skills\n- [Punctuation and Capitalization Rules](http://www.grammarbook.com/english_rules.asp)\n- [Purdue University Online Writing Lab (OWL)](https://owl.english.purdue.edu)\n- [Quia - English](https://www.quia.com/shared/english/)\n\n\n## When you get bored from CS related stuff\n- [Barcroft TV](https://www.youtube.com/user/barcroftmedia/featured) : Daily short documentaries about the incredible variety of people that make up the world\n- [Big Think](https://www.youtube.com/user/bigthink/videos) : Expert driven, actionable, educational content, featuring experts ranging from Bill Clinton to Bill Nye\n- [ColdFusion](https://www.youtube.com/user/coldfustion/videos) : Past, present, and future of technology\n- [CrashCourse](https://www.youtube.com/user/crashcourse/videos) : small courses on various subjects\n- [Every Frame a Painting](https://www.youtube.com/user/everyframeapainting/videos) : High quality analysis of films and filmmaking\n- [National Geographic](https://www.youtube.com/user/NationalGeographic/videos) : High volume of high quality content from all over the world\n- [r/ProgrammerHumor](https://www.reddit.com/r/ProgrammerHumor/) : Subreddit dedicated to exactly what it sounds like\n- [Reddit the front page of the internet](https://www.reddit.com) : Where free time goes to die\n- [Ridddle](https://www.youtube.com/user/VineMontanaTV/videos) : A youtube channel about science, mainly, but not only, the universe and space.\n- [Regex Crossword](https://regexcrossword.com) : A simple crossword game where clues are regex that must be mached.\n- [SciShow](https://www.youtube.com/user/scishow/videos) : Answers to interesting questions that you've always wondered about\n- [SmarterEveryDay](https://www.youtube.com/user/destinws2/videos) : Lots of amazing scientific information about the world around us, usually captured with a high-speed camera\n- [TED](https://www.youtube.com/user/TEDtalksDirector/videos) : Great talks about technology, entertainment, and design\n- [TestTube News](https://www.youtube.com/user/TestTubeNetwork/videos) : Interesting information about news from around the world\n- [How to live for long time ?](http://www.bbc.com/future/story/20170601-the-secret-to-a-long-and-healthy-life-eat-less) : The secret of longevity\n- [Vsauce](https://www.youtube.com/user/Vsauce/videos) : The best youtube channel\n- [Ox A cuk](http://www.ox.ac.uk/research/research-in-conversation/how-live-happy-life/dr-bronwyn-tarr#) : How to live a happy life\n- [lonelyspeck](https://www.lonelyspeck.com/the-milky-way-in-los-angeles-light-pollution/) : Expose to the Right for Astrophotography in Light Pollution – Palos Verdes, Los Angeles, California\n\n## Open Source Websites\n- [Open Hatch](https://openhatch.org) : OpenHatch is a non-profit dedicated to matching prospective free software contributors with communities, tools, and education.\n- [Source Forge](https://sourceforge.net) : SourceForge hosts nearly 280,000 projects (at last count). It serves more than 2 million downloads a day and includes apps and tools in a wide variety of categories.\n- [Google Code](https://code.google.com/projecthosting) : Google offers free hosting for open source projects using the Subversion or Mercurial version control systems. It offers 2 GB of storage, integrated code review tools, a wiki, and an issue tracker. The Google Code site also provides links to Google's many publicly available APIs and other developer tools.\n- [Launch Pad](https://launchpad.net) : Maintained by Canonical, LaunchPad is particularly targeted at projects that run on Ubuntu. It provides hosting for more than 21,000 projects that use the Bazaar version control system.\n- [Google Open Source](https://opensource.google.com) : Google Open Source\n- [Red Hat Developer](https://developers.redhat.com) : The world's leading provider of open source solutions\n- [Open Source](https://opensource.com) : Open Source\n- [Google Summer of Code](https://summerofcode.withgoogle.com) : Google Summer of Code is a global program focused on bringing more student developers into open source software development. Students work with an open source organization on a 3 month programming project during their break from school.\n- [Open Source Web Design](http://www.oswd.org) : Open Source Web Design is a platform for sharing standards-compliant free web design templates. We give web publishers a voice through good design.\n- [Mozilla Winter of Security](https://wiki.mozilla.org/Security/Automation/Winter_Of_Security_2016) : The Winter of Security (MWOS) is a program organized by Mozilla's Security teams to involve students with Security projects. Students who have to perform a semester project as part of their university curriculum can apply to one of the MWOS project.\n- [Eclipse Lab](http://www.eclipse.org/org/foundation/eclipselabs/faq.php) : Eclipse Labs is a community of open source projects that build technology based on the Eclipse platform. It provides the infrastructure services typically required by open source projects, such as code repositories, bug tracking, project web sites/wiki. Eclipse Labs is hosted by Google Code Project Hosting, so it will be very familiar to developers already using Google Code Project Hosting.\n- [Bit Bucket](https://bitbucket.org) : Like GitHub, BitBucket hosts both public and private projects. On this site, open source projects and private projects with fewer than five users are free. It hosts more than 48,000 repositories, many of which are searchable on the site.\n- [Media Wiki](https://www.mediawiki.org/wiki/MediaWiki) : MediaWiki is a free software open source wiki package written in PHP, originally for use on Wikipedia. It is now also used by several other projects of the non-profit Wikimedia Foundation and by many other wikis, including this website, the home of MediaWiki.\n- [Code Curiosity](https://codecuriosity.org) : CodeCuriosity is a platform that encourages contributions to open source. Everyone is rewarded for their efforts, no matter how big or small they are.\n- [Code Triage](https://www.codetriage.com) : Help out your favorite open source projects and become a better developer while doing it.\n- [Issue Hub](http://issuehub.io) : Contribute to Open Source. Search issue labels to find the right project for you\n- [Up for Grabs](http://up-for-grabs.net) : This is a list of projects which have curated tasks specifically for new contributors. These are a great way to get started with a project, or to help share the load of working on open source projects.\n- [First Timers Only](http://www.firsttimersonly.com) : Contributing to open source for the first time can be scary and a little overwhelming. Perhaps you’re a Code Newbie or maybe you’ve been coding for a while but haven’t found a project you felt comfortable contributing to.\n- [Your First PR](http://yourfirstpr.github.io) : Your First PR helps you get started contributing to Open Source by showcasing great starter issues on GitHub and elsewhere.\n\n## Internships\n- [Chegg](http://www.chegg.com) : It is an awesome resource for finding\n\tinternships, scholarships, tutors, etc.\n- [Internshala](https://internshala.com) : You can search internships here\n\taccording to your skill sets for your interested location. It also helps you\n\tin getting a good PPO offer from the company.\n- [Letsintern](https://www.letsintern.com) : Get a smart and challenging\n\tinternship for you from the LetsIntern.\n- [PerfectIntern](https://www.perfectintern.com): Get help finding a paid\n\tinternship, resume prep, interview prep, and more!\n\n## Jobs\n- [AngelList](https://angel.co/) : AngelList is a \n\twebsite for startups, angel investors, and job-seekers \n\tlooking to work at startups. \n- [CareerBuilder](https://hiring.careerbuilder.com) : CareerBuilder is one of\n\tthe largest job boards, providing job listings, resume posting, and career\n\tadvice and resources to job seekers.\n- [Dice](https://www.dice.com) : Dice is the leading site for tech job seekers.\n\tYou can search by company, job title, keyword, employment type, and location.\n- [Devsnap](https://devsnap.io) : Devsnap is a job aggregator for developer jobs. \n- [Glassdoor](https://www.glassdoor.co.in) : Find the job that's right for you.\n\tSearch all the open positions on the web. Get your own personalized salary estimate.\n- [IAESTE](http://iaesteunitedstates.org) : Find more and amazing international internships\n and jobs.\n- [Indeed](https://www.indeed.com) : Find international jobs on Indeed and get\n\ta market salary for your dedication and devotion.\n- [Instahyre](https://www.instahyre.com) : Your dream job is now here. Showcase\n yourself to a curated list of top companies. Complete privacy and no spam.\n- [Mentat](https://thementat.com) : Get your dream job 10x faster. Never apply\n\t for a job ever again, talk directly to decision makers and hiring managers.\n- [The Muse](https://www.themuse.com) : Find everything you need to succeed from\n\tdream jobs to career advice. You can do a lot here on The Muse like exploring\n\tcompanies, browsing jobs, career advice, discover careers, career coaching. Try it.\n- [Paysa](https://www.paysa.com) : Paysa helps you in finding new and\n\tinteresting jobs according to your wish.\n- [SimplyHired](https://www.simplyhired.com) : Simply Hired is a free job search\n\tengine (and mobile app) that takes the hassle out of getting hired and provides\n\tyou with all the information you need to make a sound career move.\n- [SwissDev Jobs](https://swissdevjobs.ch) : Tech job board for Software Engineers that want to work in Switzerland.\n- [Undercover Recruiter](https://theundercoverrecruiter.com) : Become Recruiter,\n Candidate or Employer. You can get your job easily here.\n- [Who is Hiring](https://whoishiring.io) : An awesome resource for searching,\n filtering and finding new and attractive jobs according to your needs and\n interests.\n- [ZipRecruiter](https://www.ziprecruiter.com) : The Smartest Way to Get Hired.\n- [RemoteML](https://remoteml.com/) : Remote Machine Learning jobs.\n- [Linkedin jobs](https://www.linkedin.com/jobs) : Very nice research tool for programming jobs\n\n### Special Thanks\n- Please consider a github star if you find this useful and/or consider making a contribution.\n- A special thanks to Ashish Padalkar (@ashish2199) for contributing a great amount of data and structure to the initial repository [Original Post](https://www.quora.com/How-to-Create-a-Blog-2/answer/Ashish-Padalkar?srid=OH96).", + "readme_title": "Best-websites-a-programmer-should-visit", + "releases_count": 0, + "repo": "Best-websites-a-programmer-should-visit", + "repo_name": "Best-websites-a-programmer-should-visit", + 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"pushed_at": "2026-04-01T05:23:08Z", + "readme_length": 3907, + "readme_text": "# CloudCanvasAI\n\nAn AI-powered document creation and editing platform that combines Claude AI with real-time document preview. Chat with Claude on the left, see generated documents (`.docx`, `.pptx`, `.pdf`, `.xlsx`) rendered live on the right.\n\n## How It Works\n\n```mermaid\nflowchart TD\n User((\"👤 User\")) --> Login[\"🔐 Sign In\"]\n Login --> Chat[\"💬 Chat Panel\"]\n\n Chat -->|\"Send message\"| AI[\"🤖 Claude AI\"]\n AI -->|\"Streams reply\"| Chat\n\n AI -->|\"Generates\"| Docs[\"📝 Documents\\n.docx · .pptx · .pdf · .xlsx\"]\n Docs -->|\"Live render\"| Preview[\"📄 Document Preview\"]\n Docs -->|\"Browse\"| Files[\"📂 File Browser\"]\n Files -->|\"Download\"| User\n\n subgraph \"🖥️ Split-Panel Interface\"\n Chat\n Preview\n end\n\n subgraph \"☁️ Cloud Sandbox — isolated per user\"\n AI\n Docs\n end\n```\n\n## Technical Architecture\n\n```mermaid\ngraph TB\n subgraph \"Frontend · React + Vite\"\n UI[Split-Panel UI]\n Chat[Chat Panel]\n Preview[Document Preview]\n FBAuth[Firebase Auth Client]\n end\n\n subgraph \"Backend · FastAPI\"\n API[REST API + SSE Streaming]\n SandboxMgr[Sandbox Manager]\n FBAdmin[Firebase Admin SDK]\n end\n\n subgraph \"E2B Sandbox · Per-User Isolation\"\n Agent[Claude Agent SDK]\n Skills[Skills: docx / pdf / pptx / xlsx]\n FS[Isolated Filesystem]\n end\n\n subgraph \"External Services\"\n Firebase[(Firebase Auth)]\n Claude[Anthropic Claude API]\n E2B[E2B Platform]\n end\n\n Chat -->|\"SSE / HTTP\"| API\n Preview -->|\"HTTP (file fetch)\"| API\n FBAuth -->|\"ID Token\"| Firebase\n API -->|\"Verify Token\"| FBAdmin\n FBAdmin --> Firebase\n API --> SandboxMgr\n SandboxMgr -->|\"Create / Connect\"| E2B\n E2B --> Agent\n Agent -->|\"API Calls\"| Claude\n Agent --> Skills\n Skills --> FS\n```\n\n## Project Structure\n\n```\nCloudCanvasAI/\n├── backend/ # FastAPI backend (Python)\n│ ├── routers/ # API endpoints (chat SSE, file serving)\n│ ├── sandbox_manager.py # E2B sandbox lifecycle\n│ ├── e2b-template/ # Custom E2B sandbox template\n│ └── skills/ # Document manipulation skills\n├── frontend/ # React + Vite frontend\n│ ├── src/pages/ # Chat page with split-panel UI\n│ ├── src/components/ # DocumentPreview, FileList\n│ └── src/services/ # API client, Firebase auth\n└── skills/ # Git submodule → github.com/anthropics/skills\n```\n\n## Prerequisites\n\n- Python 3.11+\n- Node.js 18+\n- API keys: `ANTHROPIC_API_KEY`, `E2B_API_KEY`\n- Firebase project with Auth enabled\n\n## Setup\n\n### Clone with submodules\n\n```bash\ngit clone --recurse-submodules \ncd CloudCanvasAI\n```\n\nIf already cloned:\n```bash\ngit submodule update --init --recursive\n```\n\n### Backend\n\n```bash\ncd backend\npython -m venv venv\nsource venv/bin/activate\npip install -r requirements.txt\ncp .env.example .env # Fill in your API keys\nuvicorn main:app --reload\n```\n\nAPI available at `http://localhost:8000` (Swagger UI at `/docs`)\n\n### Frontend\n\n```bash\ncd frontend\nnpm install\ncp .env.example .env # Fill in Firebase config\nnpm run dev\n```\n\nApp available at `http://localhost:5173`\n\n## Environment Variables\n\nSee `backend/.env.example` and `frontend/.env.example` for all required variables.\n\nKey variables:\n- `ANTHROPIC_API_KEY` — Claude API access\n- `E2B_API_KEY` — Sandbox creation\n- `E2B_TEMPLATE` — E2B template alias (default: `cloudcanvasai-docs`)\n- `VITE_API_BASE_URL` — Backend URL for production (defaults to localhost in dev)\n- `VITE_FIREBASE_*` — Firebase web config\n\n## Tech Stack\n\n| Layer | Technology |\n|-------|-----------|\n| Frontend | React 19, Vite 7, Firebase Auth |\n| Backend | FastAPI, Uvicorn, Claude Agent SDK |\n| Sandbox | E2B Code Interpreter (per-user isolation) |\n| Doc Rendering | mammoth.js (docx), react-markdown |\n| Auth | Firebase (Google + email/password) |\n| Deployment | Railway (backend), Vercel (frontend) |", + "readme_title": "CloudCanvasAI", + "releases_count": 0, + "repo": "CloudCanvasAI", + "repo_name": "CloudCanvasAI", + "stars": 0, + "topics": [], + "updated_at": "2026-03-15T01:47:46Z", + "url": "https://github.com/hassan11196/CloudCanvasAI", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/CloudCanvasAI" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/CompilerConstruction-A1" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 5, + "contributors_count": 2, + "contributors_top": [ + { + "contributions": 3, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + }, + { + "contributions": 2, + "html_url": "https://github.com/EishaMazhar", + "login": "EishaMazhar" + } + ], + "created_at": "2021-03-08T17:35:04Z", + "default_branch": "main", + "description": null, + "dirs_total_count": 7, + "files_root_entries": [ + { + "path": ".idea", + "size": 0, + "type": "dir" + }, + { + "path": "Assignment1.pdf", + "size": 163795, + "type": "file" + }, + { + "path": "CodeGenerator.iml", + "size": 1202, + "type": "file" + }, + { + "path": "README.md", + "size": 588, + "type": "file" + }, + { + "path": "input_function", + "size": 0, + "type": "dir" + }, + { + "path": "output_code", + "size": 0, + "type": "dir" + }, + { + "path": "pom.xml", + "size": 1522, + "type": "file" + }, + { + "path": "src", + "size": 0, + "type": "dir" + } + ], + "files_total_count": 26, + "first_commit_date_default_branch": "2021-03-08T10:35:00Z", + "forks": 1, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/CompilerConstruction-A1", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 1, + "issues_total": 1, + "languages_top": [ + { + "language": "Java", + "size": 5638 + }, + { + "language": "C", + "size": 1067 + } + ], + "last_commit_date_default_branch": "2021-10-31T16:35:32Z", + "last_commit_oid_default_branch": "73f08bb53a70b5b1983bcef76c8313a588710563", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/CompilerConstruction-A1", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "Java", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 1, + "pull_requests_open": 0, + "pull_requests_total": 1, + "pushed_at": "2021-10-31T16:35:33Z", + "readme_length": 586, + "readme_text": "# Code Generator for C Functions:\n\n## What's code generation?\n\nIn computing, code generation is the process by which a compiler's code generator converts some intermediate representation of source code into a form that can be readily executed by a machine. Sophisticated compilers typically perform multiple passes over various intermediate forms.\n\n## About the project\n\nIn this project, tail end recursive program is generated in C from a JSON object.\n\nInput Directory for JSON: `input_function/`\n\nOutput Directory for C code: `output_code/`\n\n`Build and Run Class CodeGeneratorDriver.`", + "readme_title": "Code Generator for C Functions:", + "releases_count": 0, + "repo": "CompilerConstruction-A1", + "repo_name": "CompilerConstruction-A1", + "stars": 0, + "topics": [ + "hacktoberfest" + ], + "updated_at": "2025-01-12T05:33:45Z", + "url": "https://github.com/hassan11196/CompilerConstruction-A1", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/CompilerConstruction-A1" 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"has_docker": false, + "has_docs": false, + "has_license_file": true, + "has_notebooks": true, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/DAB-DETR", + "is_archived": false, + "is_fork": true, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Jupyter Notebook", + "size": 8635830 + }, + { + "language": "Python", + "size": 392882 + }, + { + "language": "Cuda", + "size": 62010 + }, + { + "language": "C++", + "size": 6172 + }, + { + "language": "Shell", + "size": 1535 + } + ], + "last_commit_date_default_branch": "2022-06-12T01:31:55Z", + "last_commit_oid_default_branch": "3f952553099bbf0ca996a341c66c1a9737d57fb5", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "Apache License 2.0", + "license_spdx": "Apache-2.0", + "name_with_owner": "hassan11196/DAB-DETR", + "owner": "hassan11196", + "parent_repo": "IDEA-Research/DAB-DETR", + "parent_url": "https://github.com/IDEA-Research/DAB-DETR", + "primary_language": "Jupyter Notebook", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2022-06-12T01:32:13Z", + "readme_length": 14339, + "readme_text": "# DAB-DETR\n\nThis is the official pytorch implementation of our ICLR 2022 paper [DAB-DETR](https://arxiv.org/abs/2201.12329). \n\nAuthors: [Shilong Liu](https://www.lsl.zone/), [Feng Li](https://scholar.google.com/citations?hl=zh-CN&user=ybRe9GcAAAAJ), [Hao Zhang](https://scholar.google.com/citations?user=B8hPxMQAAAAJ&hl=zh-CN), Xiao Yang, Xianbiao Qi, [Hang Su](https://www.suhangss.me/), [Jun Zhu](https://ml.cs.tsinghua.edu.cn/~jun/index.shtml), [Lei Zhang](https://www.leizhang.org/)\n\n# News\n[2022/6]: We release a unified detection and segmentation model [Mask DINO](https://arxiv.org/pdf/2206.02777.pdf) that achieves the best results on all the three segmentation tasks (**54.5** AP on [COCO instance leaderboard](https://paperswithcode.com/sota/instance-segmentation-on-coco-minival), **59.4** PQ on [COCO panoptic leaderboard](https://paperswithcode.com/sota/panoptic-segmentation-on-coco-minival), and **60.8** mIoU on [ADE20K semantic leaderboard](https://paperswithcode.com/sota/semantic-segmentation-on-ade20k))! Code will be available [here](https://github.com/IDEACVR/MaskDINO). \\\n[2022/5/28] Code for [DN-DETR](https://arxiv.org/abs/2203.01305) is available [here](https://github.com/IDEA-opensource/DN-DETR)! \\\n[2022/5/22] We release a notebook for visualizion in [inference_and_visualize.ipynb](inference_and_visualize.ipynb). \\\n[2022/4/14] We release the [```.pptx``` file](resources/comparison_raleted_works_raw.pptx) of our [DETR-like models comparison figure](#comparison-of-detr-like-models) for those who want to draw model arch figures in paper. \\\n[2022/4/12] We fix a bug in the file ```datasets/coco_eval.py```. The parameter ```useCats``` of ```CocoEvaluator``` should be ```True``` by default. \\\n[2022/4/9] Our code is available! \\\n[2022/3/9] We build a repo [Awesome Detection Transformer](https://github.com/IDEACVR/awesome-detection-transformer) to present papers about transformer for detection and segmenttion. Welcome to your attention! \\\n[2022/3/8] Our new work [DINO](https://arxiv.org/abs/2203.03605) set a new record of **63.3AP** on the MS-COCO leader board. \\\n[2022/3/8] Our new work [DN-DETR](https://arxiv.org/abs/2203.01305) has been accpted by CVPR 2022! \\\n[2022/1/21] Our work has been accepted to ICLR 2022.\n\n# Abstract\nWe present in this paper a novel query formulation using dynamic anchor boxes for DETR (DEtection TRansformer) and offer a deeper understanding of the role of queries in DETR. This new formulation directly uses box coordinates as queries in Transformer decoders and dynamically updates them layer-by-layer. Using box coordinates not only helps using explicit positional priors to improve the query-to-feature similarity and eliminate the slow training convergence issue in DETR, but also allows us to modulate the positional attention map using the box width and height information. Such a design makes it clear that queries in DETR can be implemented as performing soft ROI pooling layer-by-layer in a cascade manner. As a result, it leads to the best performance on MS-COCO benchmark among the DETR-like detection models under the same setting, e.g., AP 45.7\\% using ResNet50-DC5 as backbone trained in 50 epochs. We also conducted extensive experiments to confirm our analysis and verify the effectiveness of our methods.\n\n# Model\n![arch](figure/arch_new2.png)\n\n\n\n\n\n\n# Model Zoo\nWe provide our models with R50 backbone, including both **DAB-DETR** and **DAB-Deformable-DETR** (See Appendix C of [our paper](https://arxiv.org/abs/2201.12329) for more details). \n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
namebackbonebox APLog/Config/CheckpointWhere in Our Paper
0DAB-DETR-R50R5042.2Google Drive | Tsinghua CloudTable 2
1DAB-DETR-R50(3 pat)1R5042.6Google Drive | Tsinghua CloudTable 2
2DAB-DETR-R50-DC5R5044.5Google Drive | Tsinghua CloudTable 2
3DAB-DETR-R50-DC5-fixxy2R5044.7Google Drive | Tsinghua CloudTable 8. Appendix H.
4DAB-DETR-R50-DC5(3 pat)R5045.7Google Drive | Tsinghua CloudTable 2
5DAB-Deformbale-DETR
(Deformbale Encoder Only)3		R5046.9Baseline for DN-DETR
6DAB-Deformable-DETR-R50-v24R5048.7Google Drive | Tsinghua CloudExtend Results for Table 5,
Appendix C.
\n\nNotes: \n- 1: The models with marks (3 pat) are trained with multiple pattern embeds (refer to Anchor DETR or [our paper](https://arxiv.org/abs/2201.12329) for more details.). \n- 2: The term \"fixxy\" means we use random initialization of anchors and do not update their parameters during training (See Appendix H of [our paper](https://arxiv.org/abs/2201.12329) for more details). \n- 3: The DAB-Deformbale-DETR(Deformbale Encoder Only) is a multiscale version of our DAB-DETR. See [DN-DETR](https://arxiv.org/abs/2203.01305) for more details. \n- 4: The result here is better than the number in our paper, as we use different losses coefficients during training. Refer to our config file for more details.\n\n\n# Usage\n## Installation\nWe use the great DETR project as our codebase, hence no extra dependency is needed for our **DAB-DETR**. For the **DAB-Deformable-DETR**, you need to compile the deformable attention operator manually.\n\nWe test our models under ```python=3.7.3,pytorch=1.9.0,cuda=11.1```. Other versions might be available as well.\n\n1. Clone this repo\n```sh\ngit clone https://github.com/IDEA-opensource/DAB-DETR.git\ncd DAB-DETR\n```\n\n2. Install Pytorch and torchvision\n\nFollow the instrction on https://pytorch.org/get-started/locally/.\n```sh\n# an example:\nconda install -c pytorch pytorch torchvision\n```\n\n3. Install other needed packages\n```sh\npip install -r requirements.txt\n```\n\n4. Compiling CUDA operators\n```sh\ncd models/dab_deformable_detr/ops\npython setup.py build install\n# unit test (should see all checking is True)\npython test.py\ncd ../../..\n```\n\n## Data\nPlease download [COCO 2017](https://cocodataset.org/) dataset and organize them as following:\n```\nCOCODIR/\n ├── train2017/\n ├── val2017/\n └── annotations/\n \t├── instances_train2017.json\n \t└── instances_val2017.json\n```\n\n\n## Run\nWe use the standard **DAB-DETR-R50** and **DAB-Deformable-DETR-R50** as examples for training and evalulation.\n\n### Inference with our pretrained models\nSee [inference_and_visualize.ipynb](inference_and_visualize.ipynb).\n\n### Eval our pretrianed models\n\nDownload our DAB-DETR-R50 model checkpoint from [this link](https://drive.google.com/drive/folders/1pae4Yxi0H9OOFdXZg3zXuKK9yOznhTNU?usp=sharing) and perform the command below. \nYou can expect to get the final AP about ```42.2```.\n\nFor our DAB-Deformable-DETR ([download here](https://drive.google.com/drive/folders/1uP9N0HIBPB6OPxLq5aqeIfKNOFFYQl2t?usp=sharing)), the final AP expected is ```48.7```.\n\n```sh\n# for dab_detr: 42.2 AP\npython main.py -m dab_detr \\\n --output_dir logs/DABDETR/R50 \\\n --batch_size 1 \\\n --coco_path /path/to/your/COCODIR \\ # replace the args to your COCO path\n --resume /path/to/our/checkpoint \\ # replace the args to your checkpoint path\n --eval\n\n# for dab_deformable_detr: 48.7 AP\npython main.py -m dab_deformable_detr \\\n --output_dir logs/dab_deformable_detr/R50 \\\n --batch_size 2 \\\n --coco_path /path/to/your/COCODIR \\ # replace the args to your COCO path\n --resume /path/to/our/checkpoint \\ # replace the args to your checkpoint path\n --transformer_activation relu \\\n --eval\n```\n\n\n### Training your own models\nSimilarly, you can also train our model on a single process: \n```sh\n# for dab_detr\npython main.py -m dab_detr \\\n --output_dir logs/DABDETR/R50 \\\n --batch_size 1 \\\n --epochs 50 \\\n --lr_drop 40 \\\n --coco_path /path/to/your/COCODIR # replace the args to your COCO path\n```\n\n\n### Distributed Run\nHowever, as the training is time consuming, we suggest to train the model on multi-device.\n\nIf you plan to train the models on a cluster with Slurm, here is an example command for training:\n```sh\n# for dab_detr: 42.2 AP\npython run_with_submitit.py \\\n --timeout 3000 \\\n --job_name DABDETR \\\n --coco_path /path/to/your/COCODIR \\\n -m dab_detr \\\n --job_dir logs/DABDETR/R50_%j \\\n --batch_size 2 \\\n --ngpus 8 \\\n --nodes 1 \\\n --epochs 50 \\\n --lr_drop 40 \n\n# for dab_deformable_detr: 48.7 AP\npython run_with_submitit.py \\\n --timeout 3000 \\\n --job_name dab_deformable_detr \\\n --coco_path /path/to/your/COCODIR \\\n -m dab_deformable_detr \\\n --transformer_activation relu \\\n --job_dir logs/dab_deformable_detr/R50_%j \\\n --batch_size 2 \\\n --ngpus 8 \\\n --nodes 1 \\\n --epochs 50 \\\n --lr_drop 40 \n```\n\nThe final AP should be similar to ours. (```42.2``` for **DAB-DETR** and ```48.7``` for **DAB-Deformable-DETR**). Our configs and logs(see the [model_zoo](#model-zoo)) could be used as references as well. \n\nNotes: \n - The results are sensitive to the batch size. We use 16(2 images each GPU x 8 GPUs) by default.\n\n\nOr run with multi-processes on a single node:\n```sh\n# for dab_detr: 42.2 AP\npython -m torch.distributed.launch --nproc_per_node=8 \\\n main.py -m dab_detr \\\n --output_dir logs/DABDETR/R50 \\\n --batch_size 2 \\\n --epochs 50 \\\n --lr_drop 40 \\\n --coco_path /path/to/your/COCODIR\n\n# for dab_deformable_detr: 48.7 AP\npython -m torch.distributed.launch --nproc_per_node=8 \\\n main.py -m dab_deformable_detr \\\n --output_dir logs/dab_deformable_detr/R50 \\\n --batch_size 2 \\\n --epochs 50 \\\n --lr_drop 40 \\\n --transformer_activation relu \\\n --coco_path /path/to/your/COCODIR\n```\n\n\n# Detailed Model\n![arch](figure/arch.png)\n\n# Comparison of DETR-like Models\nThe source file can be found [here](./resources/comparison_raleted_works_raw.pptx).\n\n![comparison](figure/model.png)\n\n\n\n# Links\n

\nDINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection.\n
\nHao Zhang*, Feng Li*, Shilong Liu*, Lei Zhang, Hang Su, Jun Zhu, Lionel M. Ni, Heung-Yeung Shum\n
\narxiv 2022.\n
\n[paper] [code] \n

\n\n

\nDN-DETR: Accelerate DETR Training by Introducing Query DeNoising.\n
\nFeng Li*, Hao Zhang*, Shilong Liu, Jian Guo, Lionel M. Ni, Lei Zhang.\n
\nIEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2022.\n
\n[paper] [code] \n

\n\n# License\nDAB-DETR is released under the Apache 2.0 license. Please see the [LICENSE](LICENSE) file for more information.\n\nCopyright (c) IDEA. All rights reserved.\n\nLicensed under the Apache License, Version 2.0 (the \"License\"); you may not use these files except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n\n\n\n# Citation\n```bibtex\n@inproceedings{\n liu2022dabdetr,\n title={{DAB}-{DETR}: Dynamic Anchor Boxes are Better Queries for {DETR}},\n author={Shilong Liu and Feng Li and Hao Zhang and Xiao Yang and Xianbiao Qi and Hang Su and Jun Zhu and Lei Zhang},\n booktitle={International Conference on Learning Representations},\n year={2022},\n url={https://openreview.net/forum?id=oMI9PjOb9Jl}\n}\n```", + "readme_title": "News", + "releases_count": 0, + "repo": "DAB-DETR", + "repo_name": "DAB-DETR", + "stars": 0, + "topics": [], + "updated_at": "2022-06-12T01:32:16Z", + "url": "https://github.com/hassan11196/DAB-DETR", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/DAB-DETR" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/DB-LAB" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 2, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 2, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + } + ], + "created_at": "2020-01-13T15:42:47Z", + "default_branch": "master", + "description": "DB LAB Tasks - 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FAST NUCES (Karachi) - CS201 - Fall 2018", + "dirs_total_count": 24, + "files_root_entries": [ + { + "path": ".gitignore", + "size": 19, + "type": "file" + }, + { + "path": "DS-A1", + "size": 0, + "type": "dir" + }, + { + "path": "DS-A2", + "size": 0, + "type": "dir" + }, + { + "path": "DS-A3", + "size": 0, + "type": "dir" + }, + { + "path": "README.md", + "size": 7848, + "type": "file" + } + ], + "files_total_count": 127, + "first_commit_date_default_branch": "2020-03-03T14:54:51Z", + "forks": 2, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/DS-Assignments", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "C++", + "size": 181651 + } + ], + "last_commit_date_default_branch": "2020-03-03T14:54:51Z", + "last_commit_oid_default_branch": "66c052f0480e3809adf705b7b51a515315e06df8", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/DS-Assignments", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "C++", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2020-03-03T15:58:26Z", + "readme_length": 7836, + "readme_text": "# Data Structures Assignments\n\n## Assignment 1\n### Problem 1- Way-Out from a Maze\nThis problem uses DynamicSafe2DArray as discussed during the lecture. A maze is an object of\nDynamicSafe2DArray of char type. The content of the array can be any character {- (path), *\n(block), s (start), e (end), ! (visited)}.\nThe input maze may contain only one-path and you need to implement a recursive path finding\napproach that enumerate all cell of the array that are on the path in order of traverse from start s to\nend e. Here is an example of a maze.\n\n * s * * * * * * * *\n * - - * * * * * * *\n * * - - * * * * * *\n * * * - * * * * * *\n * * * - - - - * * *\n * * * * * * - * - e\n * * * * * * - * - *\n * * * * * * - * - *\n * * * * * * - - - *\n * * * * * * * * * *\n\nAll you need to develop a recursive routine for finding path, the search is only allowed to follow\n{Left, Right, Up and Down} from any location. The output for this problem is complete path from\nstarting to end location. The validation of input cases required as start and end location must be at\nthe boundary of the maze. There may be one or no path for all valid input cases.\n\n\n### Problem 2 – Brute Force Sequence matching\nIn bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or\nprotein to identify regions of similarity that may be a consequence of functional, structural, or\nevolutionary relationships between the sequences. In this problem there are two sequences of\nvariable length given to you, you can keep them in simple DynamicSafeArray. The brute force\nsequence matching algorithm finds the maximum sequence of match from the two given sequence.\nObviously this maximum match would not be larger than the smaller sequence. The sequence\nmatching may disallow characters in either sequence that already appears in the earlier match, but\nmissing from the next possible match. For example, consider the sequence S1 and S2\n\n S1: ACTTGTTACTGTTACCT\n\n S2: ACTGTACTGTACT\n\n Matched: ACT*GT*ACTGT*AC*T\n\nHere the matched sequence is greater than the length of the smallest sequence as we are\ndisallowing match of a character that earlier matched. In the above example there are four such\ninstances hence the match sequence is of length |s2| + 4.\n\n### Problem 3 – KQUERY\n\nYou are given an array of numbers, there can be n numbers where n is between (1 <= n <= 30000).\nEach number is between (1 <= ai <= 109). You are also given “t” queries of the form qt ( i, j, k)\nwhere ( 1 <= i <= j <=n) for each query you need to return the number of elements greater than k\nin the sub-sequence ai, ai+1, ....., aj you need to process all t queries in the same fashion.\n\n\n## Assignment 2\n### Problem 1- Optimal Way-Out from a Maze\n\nYou are now well aware of how to get a solution rom a maze. The content of the array can be\nany character {1 (path), 0 (block), s (start), e (end), ! (visited)}.\nThe input maze may contain multiple-paths and you need to implement a recursive path finding\napproach that enumerate all cell of the array that are on the unique path in order of traverse from\nstart s to end e. You need to enumerate all paths. Here is an example of a maze.\n\n s 1 1 1 1 1 0\n 1 0 0 1 1 0 1\n 1 0 0 0 1 0 1\n 1 1 1 e 1 1 1\n\nThere are three possible paths:\n\nPath#1= {(0,0),(0,1),(0,2),(0,3),(1,3),(2,3),(3,3)} Cost=7\n\nPath#2= {(0,0),(1,0),(2,0),(3,0),(3,1),(4,1),(4,2),(4,3),(3,3)} Cost=9\n\nPath#3= {(0,0),(1,0),(2,0),(3,0),(4,0),(4,1),(4,2),(4,3),(3,3)} Cost=9\n\nAll you need to develop a recursive routine for finding path, the search is only allowed to follow\n{Left, Right, Up and Down} from any location. The output for this problem is complete path from\nstarting to end location. The validation of input cases required as start and end location must be at\nthe boundary of the maze. There can be several paths for all valid input cases. You need to\nenumerate all paths from starting to each cell followed till the end. The cost of a path is total\nnumber of cell traveled. Hence the shortest path or optimal path is the minimum length path.\n\n### Problem 2- Infix to Postfix conversion and evaluation\n\nArithmetic expressions are made of operators (+, -, /, *, ^, etc.) and operands (either numbers,\nvariables or, recursively, smaller arithmetic expressions). The expressions can be written in a\nvariety of notations. In this problem, you will focus on two standard arithmetic expression\nnotations: infix and postfix. In Infix expression, operators are written in-between their operands.\nThis is the usual way we write expressions, for example x+y. Similarly, Postfix notation (also\nknown as \"Reverse Polish notation\"): x y + Operators are written after their operands. There is a\ngeneral algorithm that convert an infix expression to postfix using stack (data structure).\n\n### Problem 3- Simulation of Queuing Systems\n\nThe Shop-n-Carry (SC) is a big chain of departmental stores. The SC would like to improve its\ncustomer services. There has been a complained for some period that the waiting time to avail\nPoint-of-Sale terminal services is unbearable for some areas in stores. SC is planning to improve\nthe services by means of installing more POS terminals to those stores, which are in heavy use,\nand customers have to wait for making payments. You have been hired, as a consultant by the SC\npresident to determine whether this idea is feasible or not. The management has decided that if the\naverage waiting time of a user at a POS is greater than 3 minutes to get their services. They will\ninstall one more POS terminal machine. It is provided that the arrival rate of POS customer is\nanytime within the opening hours and the average service time is between 3 to 8 minutes. The POS\nis operational 18 X 7. Your job is to run a simulation program for SC POS for 18 hours, and\ncalculate the average waiting time for customers on hourly basis. Identify the busy hours. Use the\nleast count of the clock as discrete unit of minutes. Put the entire statistic in the output file which\nis as follows:\n- Number of customers\n- Average waiting time (for each hour – in a separate line)\n- Average Customers (for each hour – in a separate line)\n- POS utilization time (Average Service Time)\n\n## Assignment 3\n\n### Problem 1- Determining compatible set of intervals for media channel.\n\nOne of the main application of Binary Search Trees are for handling a dynamically changing\ndatasets. This can be very efficient choice for deciding many compatible issues. One of the media\nchannel is looking to decide a non-overlapping intervals for their transmission. Each program from\nthe channel is comprises of a start time, duration and commercial time for the program. For an\nexample an Interval I (start_time, duration, ctime) = (2, 6, 3) implies that the program starts at 2\nand can be finish by 11 with commercial time included for on airing the show. The program\nmanagers collect all programs information is a single file. There are 5 program managers and each\nprovides program information in a file. All you need to maintain a combine data structures that\nmaintain a non-overlapping compatible set of programs for the channel and maintain a separate\nfile for all those program that are conflicting in time. \n\nFor example, consider the files from the 2 programs managers:\n\nProgram Manager #1 \n\n 5\n 2 3 2\n 54 5 2\n 9 10 1\n 26 10 4\n 65 5 4\n\nProgram Manager #2\n\n 3\n 16 4 4\n 7 5 3\n 3 3 1\n\nThe channel does not want a silent period of transmission and select a program that is close to reduce the silent time.\n\nCompatible Programs \n\n 2 3 2\n 7 5 3\n 16 4 4\n 26 10 4\n 54 5 2\n 65 5 4\n\nConflicting programs\n\n 3 3 1\n 9 10 1\n\nYou need to maintain a BST for maintaining a set of compatible programs. The searching and\nfinding a conflict in program can be perform in log n time.", + "readme_title": "Data Structures Assignments", + "releases_count": 0, + "repo": "DS-Assignments", + "repo_name": "DS-Assignments", + "stars": 1, + "topics": [ + "data-structures", + "c", + "pathfinding", + "bfs", + "dfs", + "cpp", + "optimization", + "postfix", + "infix" + ], + "updated_at": "2020-11-14T19:08:06Z", + "url": "https://github.com/hassan11196/DS-Assignments", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/DS-Assignments" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/Django-QuerySet-Cheatsheet" + ], + 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"https://github.com/chrisdl/Django-QuerySet-Cheatsheet", + "primary_language": null, + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2020-04-17T08:29:35Z", + "readme_length": 21746, + "readme_text": "# Cheatsheet for Django QuerySets\nCurrent Django Version: [3.0](https://docs.djangoproject.com/en/3.0/ref/models/querysets/)\n\n## Methods that return new [QuerySets](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#methods-that-return-new-querysets)\n\n**Can be chained:**\n\n```python\nEntry.objects.filter(**kwargs).exclude(**kwargs).order_by(**kwargs)\n```\n\n * [filter](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#filter)\n * [exclude](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#exclude)\n * [annotate](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#annotate)\n * [order_by](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#order-by)\n * [reverse](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#reverse)\n * [distinct](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#distinct)\n * [values](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#values)\n * [values_list](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#values-list)\n * [dates](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#dates)\n * [datetimes](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#datetimes)\n * [none](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#none)\n * [all](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#all)\n * [union](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#union)\n * [intersection](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#intersection)\n * [difference](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#difference)\n * [select_related](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#select-related)\n * [prefetch_related](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#prefetch-related)\n * [extra](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#extra)\n * [defer](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#defer)\n * [only](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#only)\n * [using](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#using)\n * [select_for_update](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#select-for-update)\n * [raw](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#raw)\n\n## Operators that return new [QuerySets](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#operators-that-return-new-querysets)\n\n * [AND (&)](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#and)\n * [OR (|)](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#or)\n\n## Methods that do not return [QuerySets](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#methods-that-do-not-return-querysets)\n\n * [get](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#get)\n * [create](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#create)\n * [get_or_create](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#get-or-create)\n * [update_or_create](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#update-or-create)\n * [bulk_create](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#bulk-create)\n * [bulk_update](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#bulk-update)\n * [count](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#count)\n * [in_bulk](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#in-bulk)\n * [iterator](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#iterator)\n * [latest](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#latest)\n * [earliest](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#earliest)\n * [first](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#first)\n * [last](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#last)\n * [aggregate](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#aggregate)\n * [exists](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#exists)\n * [update](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#update)\n * [delete](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#delete)\n * [as_manager](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#as-manager)\n * [explain](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#explain)\n\n## Field lookups\n\n**Field lookups are how you specify the meat of an SQL WHERE clause. They’re specified as keyword arguments to the QuerySet methods *filter()*, *exclude()* and *get()*.**\n\n```python\nExample: Entry.objects.get(id__exact=14) # note double underscore.\n```\n\n * [exact](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#exact)\n * [iexact](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#iexact)\n * [contains](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#contains)\n * [icontains](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#icontains)\n * [in](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#in)\n * [gt](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#gt)\n * [gte](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#gte)\n * [lt](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#lt)\n * [lte](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#lte)\n * [startswith](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#startswith)\n * [istartswith](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#istartswith)\n * [endswith](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#endswith)\n * [iendswith](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#iendswith)\n * [range](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#range)\n * [date](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#date)\n * [year](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#year)\n * [iso_year](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#iso-year)\n * [month](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#month)\n * [day](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#day)\n * [week](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#week)\n * [week_day](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#week-day)\n * [quarter](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#quarter)\n * [time](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#time)\n * [hour](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#hour)\n * [minute](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#minute)\n * [second](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#second)\n * [isnull](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#isnull)\n * [regex](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#regex)\n * [iregex](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#iregex)\n\n**Protip: Use [in](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#in) to avoid chaining [filter()](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#filter) and [exclude()](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#exclude)**\n\n```python\nEntry.objects.filter(status__in=['Hung over', 'Sober', 'Drunk'])\n```\n\n## Aggregation functions ([link](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#aggregation-functions))\n\n * [expression](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#expression)\n * [output_field](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#output-field)\n * [filter](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#aggregate-filter)\n * [\\*\\*extra](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#id7)\n * [Avg](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#avg)\n * [Count](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#id8)\n * [Max](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#max)\n * [Min](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#min)\n * [StdDev](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#stddev)\n * [Sum](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#sum)\n * [Variance](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#variance)\n\n## Query-related tools ([link](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#query-related-tools))\n\n * [Q() objects](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#q-objects)\n * [Prefetch() objects](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#prefetch-objects)\n * [prefetch_related_objects()](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#prefetch-related-objects)\n * [FilteredRelation() objects](https://docs.djangoproject.com/en/3.0/ref/models/querysets/#filteredrelation-objects)\n\n- - -\n---\ntitle: Django - Queries - Cheat sheet\n---\n\n **Queryset** can be constructed, filtered, sliced, and generally passed around without actually hitting the database. No database activity actually occurs until you do something to evaluate the queryset.\n\nQuerysets are evaluated when\n\n1. iterated\n2. slice\n3. pickle\n4. repr\n5. len\n6. list\n7. bool\n\n* Filter:Returns a new QuerySet containing objects that match the given lookup parameters.\n* Exclude: Returns a new `QuerySet` containing objects that do _not_ match the given lookup parameters. In sql SELECT ...WHERE NOT..\n* Annotate\n\n```python\n >>> from django.db.models import Count\n >>> q = Blog.objects.annotate(Count('entry'))\n \\# The name of the first blog\n >>> q[0].name\n \\'Blogasaurus'\n \\# The number of entries on the first blog\n >>> q[0].entry__count\n 42\n```\n\n* order_by\n* reverse\n* distinct\n\n Entry.objects.order_by('pub_date').distinct('pub_date')\n* values\n\n Blog.objects.values()\n \n \n Blog.objects.values('id', 'name')\n \n \n Blog.objects.values(lower_name=Lower('name'))\n \n* value_list\n\n >>> Entry.objects.values_list('id', 'headline')\n \n >>> from django.db.models.functions import Lower\n >>> Entry.objects.values_list('id', Lower('headline'))\n \n* dates and datetimes\n\n >>> Entry.objects.dates('pub_date', 'year')\n [datetime.date(2005, 1, 1)]\n >>> Entry.objects.dates('pub_date', 'month')\n [datetime.date(2005, 2, 1), datetime.date(2005, 3, 1)]\n >>> Entry.objects.dates('pub_date', 'day')\n [datetime.date(2005, 2, 20), datetime.date(2005, 3, 20)]\n >>> Entry.objects.dates('pub_date', 'day', order='DESC')\n [datetime.date(2005, 3, 20), datetime.date(2005, 2, 20)]\n >>> Entry.objects.filter(headline__contains='Lennon').dates('pub_date', 'day')\n [datetime.date(2005, 3, 20)]\n* all\n* union, intersection, difference\n\n qs1.union(qs2, qs3)\n qs1.intersection(qs2, qs3)\n qs1.difference(qs2, qs3)\n* Select_related: Returns a `QuerySet` that will “follow” foreign-key relationships, selecting additional related-object data when it executes its query. This is a performance booster which results in a single more complex query but means later use of foreign-key relationships won’t require database queries\n\n # Hits the database.\n e = Entry.objects.select_related('blog').get(id=5)\n \n # Doesn't hit the database, because e.blog has been prepopulated\n # in the previous query.\n b = e.blog\n* Prefetch related: similar to select_realted\n\n `select_related` works by creating an SQL join and including the fields of the related object in the `SELECT` statement. For this reason, `select_related` gets the related objects in the same database query. However, to avoid the much larger result set that would result from joining across a ‘many’ relationship, `select_related` is limited to single-valued relationships - foreign key and one-to-one.\n\n `prefetch_related`, on the other hand, does a separate lookup for each relationship, and does the ‘joining’ in Python. This allows it to prefetch many-to-many and many-to-one objects, which cannot be done using `select_related`, in addition to the foreign key and one-to-one relationships that are supported by `select_related`.\n\n # This is all possible\n Restaurant.objects.prefetch_related('best_pizza__toppings')\n Restaurant.objects.select_related('best_pizza').prefetch_related('best_pizza__toppings')\n* Extra: Sometimes, the Django query syntax by itself can’t easily express a complex `WHERE` clause. For these edge cases, Django provides the `extra()` `QuerySet` modifier — a hook for injecting specific clauses into the SQL generated by a `QuerySet`.\n\n # Select example\n qs.extra(select={'val': \"select col from sometable where othercol = %s\"},\n select_params=(someparam,))\n \n # Where example\n Entry.objects.extra(where=[\"foo='a' OR bar = 'a'\", \"baz = 'a'\"])\n \n # order by\n q = q.extra(order_by = ['-is_recent'])\n* Defer: In some complex data-modeling situations, your models might contain a lot of fields, some of which could contain a lot of data (for example, text fields), or require expensive processing to convert them to Python objects. If you are using the results of a queryset in some situation where you don’t know if you need those particular fields when you initially fetch the data, you can tell Django not to retrieve them from the database\n\n # Simple defer\n Entry.objects.defer(\"headline\", \"body\")\n \n # complex defer\n Blog.objects.select_related().defer(\"entry__headline\", \"entry__body\")\n* Only: Opposite to defer\n* Using: when using multiple databases\n* Select___for__u_pda_te: selects rows and locks them for update\n* Raw: Takes a raw SQL query, executes it\n* Get\n* Create\n* update\n* delete\n* get _or create and update or create_\n* bulk_create\n* count\n* in_bulk: to get multiple entries in bulk\n* **OuterRef:** Use `OuterRef` when a queryset in a `Subquery` needs to refer to a field from the outer query. It acts like an `[F](https://docs.djangoproject.com/en/2.0/ref/models/expressions/#django.db.models.F \"django.db.models.F\")` expression except that the check to see if it refers to a valid field isn’t made until the outer queryset is resolved.\n* Latest\n\n Entry.objects.latest('pub_date', '-expire_date')\n* first and last\n* Aggregate:\n\n Book.objects.aggregate(Avg('price'), Max('price'), Min('price'))\n* Annotate\n\n q = Book.objects.annotate(Count('authors'))\n # Interrogate the first object in the queryset\n >>> q[0]\n \n >>> q[0].authors__count\n 2\n # Interrogate the second object in the queryset\n >>> q[1]\n \n >>> q[1].authors__count\n 1\n* Follow relationships backwards ( how we use `'book'` to specify the `Publisher` -> `Book` reverse foreign key hop))\n\n Publisher.objects.aggregate(oldest_pubdate=Min('book__pubdate'))\n* Filter on annotation\n\n Book.objects.annotate(num_authors=Count('authors')).filter(num_authors__gt=1)\n* Q objects (Logical operations)\n\n Q(question__startswith='Who') | Q(question__startswith='What')\n Poll.objects.get(\n Q(question__startswith='Who'),\n Q(pub_date=date(2005, 5, 2)) | Q(pub_date=date(2005, 5, 6))\n )\n* F expressions: act as a reference to a model field within a query\n\n >>> from django.db.models import F\n >>> Entry.objects.filter(n_comments__gt=F('n_pingbacks'))\n* `Subquery()` expressions\n\n >>> from django.db.models import OuterRef, Subquery\n >>> newest = Comment.objects.filter(post=OuterRef('pk')).order_by('-created_at')\n >>> Post.objects.annotate(newest_commenter_email=Subquery(newest.values('email')[:1]))\n\n##### Learn COMPLEX queries by examples:\n\n # Group by date\n Log.objects.order_by().annotate(date=TruncDate('created_at')).values('date').annotate(c=Count('id'))\n \n # Annotating Django querysets with ForeignKey Counts subject to conditions\n \n Airport.objects.filter(\n Q(origins__owner=user) | Q(destinations__owner=user)\n ).annotate(\n num_origins=Count(\n Case(When(Q(origin__owner=user), then=1, else=0)),\n ),\n num_destinations=Count(\n Case(When(Q(destination__owner=user), then=1, else=0)),\n )\n )\n \n # Aggrigate\t\n Feedback.objects.aggregate(avg_rating=Avg('rating'))\n \n Parent.objects\n .annotate(child_count=Count('child'))\n .annotate(\n grandchild_count_for_state_true=Subquery(\n GrandChild.objects.filter(\n state=True,\n child=OuterRef('pk')\n ).values('parent')\n .annotate(cnt=Sum('child__grandchild__num'))\n .values('cnt'),\n num=models.IntegerField()\n )\n )\n\nWINDOW: Window functions provide a way to apply functions on partitions. Unlike a normal aggregation function which computes a final result for each set defined by the group by, window functions operate on [frames](https://docs.djangoproject.com/en/2.0/ref/models/expressions/#window-frames) and partitions, and compute the result for each row.\n\n >>> from django.db.models import Avg, F, RowRange, Window\n >>> from django.db.models.functions import ExtractYear\n >>> Movie.objects.annotate(\n >>> avg_rating=Window(\n >>> expression=Avg('rating'),\n >>> partition_by=[F('studio'), F('genre')],\n >>> order_by=ExtractYear('released').asc(),\n >>> frame=RowRange(start=-2, end=2),\n >>> ),\n >>> )\n\n`**Exists:**`is a `Subquery` subclass that uses an SQL `EXISTS` statement. In many cases it will perform better than a subquery since the database is able to stop evaluation of the subquery when a first matching row is found.\n\nFor example, to annotate each post with whether or not it has a comment from within the last day:\n\n >>> from django.db.models import Exists, OuterRef\n >>> from datetime import timedelta\n >>> from django.utils import timezone\n >>> one_day_ago = timezone.now() - timedelta(days=1)\n >>> recent_comments = Comment.objects.filter(\n ... post=OuterRef('pk'),\n ... created_at__gte=one_day_ago,\n ... )\n >>> Post.objects.annotate(recent_comment=Exists(recent_comments))\n\n`Exists` is a `Subquery` subclass that uses an SQL `EXISTS` statement. In many cases it will perform better than a subquery since the database is able to stop evaluation of the subquery when a first matching row is found.\n\nFor example, to annotate each post with whether or not it has a comment from within the last day:\n\n >>> from django.db.models import Exists, OuterRef\n >>> from datetime import timedelta\n >>> from django.utils import timezone\n >>> one_day_ago = timezone.now() - timedelta(days=1)\n >>> recent_comments = Comment.objects.filter(\n ... post=OuterRef('pk'),\n ... created_at__gte=one_day_ago,\n ... )\n >>> Post.objects.annotate(recent_comment=Exists(recent_comments))\n \n\nmore examples\n\n from django.db.models.functions import Concat\n from django.db.models import F, Value\n \n qs = qs.annotate(fullname=Concat(F('name'), Value(' '), F('surname')))\\\n .filter(fullname__icontains=textquery)\n\nIf you want to write django queries try answering these questions in django ORM\n\n[http://a4academics.com/interview-questions/53-database-and-sql/397-top-100-database-sql-interview-questions-and-answers-examples-queries?showall=&start=2](http://a4academics.com/interview-questions/53-database-and-sql/397-top-100-database-sql-interview-questions-and-answers-examples-queries?showall=&start=2 \"http://a4academics.com/interview-questions/53-database-and-sql/397-top-100-database-sql-interview-questions-and-answers-examples-queries?showall=&start=2\")\n\nReferences:\n\nDjango documentaion\n\n[https://stackoverflow.com/questions/8746014/django-group-by-date-day-month-year](https://stackoverflow.com/questions/8746014/django-group-by-date-day-month-year \"https://stackoverflow.com/questions/8746014/django-group-by-date-day-month-year\")\n\n[https://stackoverflow.com/questions/48301339/group-by-each-date-in-django-2](https://stackoverflow.com/questions/48301339/group-by-each-date-in-django-2 \"https://stackoverflow.com/questions/48301339/group-by-each-date-in-django-2\")\n\n[https://stackoverflow.com/questions/tagged/django-queryset](https://stackoverflow.com/questions/tagged/django-queryset \"https://stackoverflow.com/questions/tagged/django-queryset\")\n\n[https://narengowda.github.io/django-queries-cheat-sheet/](https://narengowda.github.io/django-queries-cheat-sheet/)\n\n\n\n\"Creative
Django-QuerySet-Cheatsheet by @chrisdl and @briandant is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.
\n\nThe [contributors](https://github.com/chrisdl/Django-QuerySet-Cheatsheet/graphs/contributors) are as gods among ants and shall forever be remembered as such.\n\nThe Django web framework referenced in the Django-QuerySet-Cheatsheet is ​© 2005-2018 Django Software Foundation.\nDjango is a registered trademark of the Django Software Foundation.", + "readme_title": "Cheatsheet for Django QuerySets", + "releases_count": 0, + "repo": "Django-QuerySet-Cheatsheet", + "repo_name": "Django-QuerySet-Cheatsheet", + "stars": 1, + "topics": [], + "updated_at": "2020-05-06T04:38:54Z", + "url": "https://github.com/hassan11196/Django-QuerySet-Cheatsheet", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/Django-QuerySet-Cheatsheet" + 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英文词频统计程序\n\n[![last-commit](https://img.shields.io/github/last-commit/HollowMan6/English-Word-Frequency-Statistics-Program)](../../graphs/commit-activity)\n[![release-date](https://img.shields.io/github/release-date/HollowMan6/English-Word-Frequency-Statistics-Program)](../../releases)\n\n[![Followers](https://img.shields.io/github/followers/HollowMan6?style=social)](https://github.com/HollowMan6?tab=followers)\n[![watchers](https://img.shields.io/github/watchers/HollowMan6/English-Word-Frequency-Statistics-Program?style=social)](../../watchers)\n[![stars](https://img.shields.io/github/stars/HollowMan6/English-Word-Frequency-Statistics-Program?style=social)](../../stargazers)\n[![forks](https://img.shields.io/github/forks/HollowMan6/English-Word-Frequency-Statistics-Program?style=social)](../../network/members)\n\n[![Open Source Love](https://badges.frapsoft.com/os/v1/open-source.svg?v=103)](https://hollowman6.github.io/fund.html)\n[![Repo-Size](https://img.shields.io/github/repo-size/HollowMan6/English-Word-Frequency-Statistics-Program.svg)](../../archive/master.zip)\n[![GPL Licence](https://badges.frapsoft.com/os/gpl/gpl.svg?v=103)](https://opensource.org/licenses/GPL-3.0/)\n\n使用C语言实现。\n\n## 原理：\n\n使用改进的二叉查找树储存单词及其出现的次数（具体见[源代码](英文词频统计程序.c)）\n\n## 测试：\n\n使用莎士比亚戏剧文章进行了测试，这里附上[测试样例](shakespeare.txt)以及[测试结果](count.txt)\n\n# English Word Frequency Statistics Program\n\nEnglish Word Frequency Statistics Program realized by C\n\n## Principle:\n\nUse the improved two fork search tree to store words and the number of times they appear ([see source code](英文词频统计程序.c) for details).\n\n## Test:\n\nTest using Shakespeare's drama. 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'Tabdeal' تبدیل means exchange.\nTabdealIt provides a platform for communities and individuals to exchange their items using a Barter system. TabDealIt specially aims to reduce e-waste as its one of leading contributors to Pollution.\n\n## Tech:\n\nTabdealIt is built on Angular with Firebase as a Backend that provides Database and Hosting Solution.\n\n## Team:\n\nTabDealIt was Made Possible by support, guidance and dedication from the following DSC members.\n\n- Krinza Momin. (DSC Lead for FAST NUCES Karachi)\n- Shahmeer Ahmed. (Lead Web Development)\n- M. Hassan Ahmed. (Co-Lead Web Development)\n- Ahsan Ali. (Team Web Development)\n- Noman Anjum. (Team Web Development)\n- Rida. (Team Web Development)\n\n## Code:\n\nCheckout the code [here](https://github.com/hassan11196/FAST-Barter).\n\nClick here to go to [TabDealIt](https://www.tabdealit.ml) .\n\n\n## For Contributors:\n\n### References: \nIf you find article that helped you, you can add them here so that it may also help others working on the project.\n\n\n[https://medium.com/codingthesmartway-com-blog/firebase-cloud-storage-with-angular-394566fd529](https://medium.com/codingthesmartway-com-blog/firebase-cloud-storage-with-angular-394566fd529)\n\n[https://codesearchonline.com/index.php/angular-firebase-cheat-sheet/](https://codesearchonline.com/index.php/angular-firebase-cheat-sheet/)\n\n[https://alligator.io/angular/firebase-crud-operations/](https://alligator.io/angular/firebase-crud-operations/)\n\n[https://alligator.io/angular/cloud-firestore-angularfire/](https://alligator.io/angular/cloud-firestore-angularfire/)\n\n[https://getuikit.com/](https://getuikit.com/)\n\n\nThis project was generated with [Angular CLI](https://github.com/angular/angular-cli) version 8.3.19.\n\n### Development server\n\nRun `ng serve` for a dev server. Navigate to `http://localhost:4200/`. The app will automatically reload if you change any of the source files.\n\n### Code scaffolding\n\nRun `ng generate component component-name` to generate a new component. You can also use `ng generate directive|pipe|service|class|guard|interface|enum|module`.\n\n### Build\n\nRun `ng build` to build the project. The build artifacts will be stored in the `dist/` directory. Use the `--prod` flag for a production build.\n\n### Running unit tests\n\nRun `ng test` to execute the unit tests via [Karma](https://karma-runner.github.io).\n\n## Running end-to-end tests\n\nRun `ng e2e` to execute the end-to-end tests via [Protractor](http://www.protractortest.org/).\n\n### Further help\n\nTo get more help on the Angular CLI use `ng help` or go check out the [Angular CLI README](https://github.com/angular/angular-cli/blob/master/README.md).", + "readme_title": "## Solution Challenge 2020:", + "releases_count": 0, + "repo": "FAST-Barter", + "repo_name": "FAST-Barter", + "stars": 2, + "topics": [ + "angular", + "dsc", + "firebase", + "barter", + "fast", + "nuces" + ], + "updated_at": "2025-09-22T02:32:44Z", + "url": "https://github.com/hassan11196/FAST-Barter", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/FAST-Barter" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/GitHubGraduation-2021" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 873, + "contributors_count": 275, + "contributors_top": [ + { + "contributions": 275, + "html_url": "https://github.com/elisemoe", + "login": "elisemoe" + }, + { + "contributions": 158, + "html_url": null, + "login": "Adam Rensel" + }, + { + "contributions": 15, + "html_url": null, + "login": "Juan Pablo Flores" + }, + { + "contributions": 9, + "html_url": null, + "login": "manasasamaga" + }, + { + "contributions": 8, + "html_url": null, + "login": "[InnCreator]" + }, + { + "contributions": 7, + "html_url": "https://github.com/Tusker13-04", + "login": "Tusker13-04" + }, + { + "contributions": 6, + "html_url": "https://github.com/EvilGns", + "login": "EvilGns" + }, + { + "contributions": 6, + "html_url": "https://github.com/rcya1", + "login": "rcya1" + } + ], + "created_at": "2021-05-12T17:47:05Z", + "default_branch": "main", + "description": "Join the GitHub Graduation Yearbook and \"walk the stage\" on June 5.", + "dirs_total_count": 276, + "files_root_entries": [ + { + "path": ".github", + "size": 0, + "type": "dir" + }, + { + "path": "README.md", + "size": 6337, + "type": "file" + }, + { + "path": "_data", + "size": 0, + "type": "dir" + }, + { + "path": "assets", + "size": 0, + "type": "dir" + } + ], + "files_total_count": 286, + "first_commit_date_default_branch": "2021-04-22T19:11:10Z", + "forks": 0, + "has_ci": true, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/GitHubGraduation-2021", + "is_archived": false, + "is_fork": true, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "JavaScript", + "size": 18763 + } + ], + "last_commit_date_default_branch": "2021-05-12T17:59:30Z", + "last_commit_oid_default_branch": "5d70ff090f4534b8a646076e80926dd0c9d3b2ff", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/GitHubGraduation-2021", + "owner": "hassan11196", + "parent_repo": "github-education-resources/GitHubGraduation-2021", + "parent_url": "https://github.com/github-education-resources/GitHubGraduation-2021", + "primary_language": "JavaScript", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2021-05-12T17:59:30Z", + "readme_length": 6288, + "readme_text": "![2021-github-graduation-social-card-1](https://user-images.githubusercontent.com/6633808/117580730-3ed6e180-b0ae-11eb-86c9-2bd01c7aa8b6.png)\n\n\nThis repository contains the [yearbook](https://education.github.com/graduation/yearbook) for **GitHub Graduation 2021**. By issuing a pull request to this repository, you can request to be added to the Class of 2021. \n\nThe first 5,000 pull request successfully merged into the repository by May 27 will receive custom swag in the mail. \n\n\n## Privacy Notice 👀\nConsider that all the information that you add to this repository will be publicly available.\n\n- If you don't feel comfortable with displaying your full name, you can include a short name or nickname instead.\n\n# Who can apply 📝\nWe invite any student that has graduated, or plans to graduate, in 2021 to apply to the yearbook. This includes bootcamps, code camps, high school graduates, Master's graduates, Ph. D. Graduates, etc.\n\nThe eligibility criteria are -\n1. You have been verified as a student with the GitHub Student Developer Pack. Not yet a part of the Pack? [Apply here](https://education.github.com/discount_requests/student_application?utm_source=2021-06-05-GitHubGraduation).\n2. You have not participated in a past GitHub Graduation event.\n\n# How to join the Class of 2021\n\nHere are two steps to join graduation and receive swag in the mail. \n1. [Fill out the **swag shipment form**](https://airtable.com/shrM5IigBuRFaj33H). Note: the form needs to be done before creating your Pull Request and does not guarantee participation in the event. Your PR must be successfully merged to be included and only the first 5,000 merged PRs will receive swag.\n2. **Submit a pull request** with your profile information to join the Yearbook and be highlighted in the graduation event.\n3. (optional) Level up your swag and make an additional appearance at graduation by **sharing a story** of a teacher who has made an impact in your education. More info below!\n4. [**Save the date**](http://www.google.com/calendar/event?action=TEMPLATE&dates=20210605T160000Z%2F20210605T180000Z&text=GitHub%20Graduation%202021%20%F0%9F%8E%93&location=twitch.tv%2Fgithubeducation&details=Join%20us%20on%20GitHub%20Campus%20TV%20to%20%22walk%20the%20stage%22%20and%20celebrate%20the%20accomplishments%20of%20this%20year%E2%80%99s%20graduating%20class%20with%20your%20fellow%20developers.%20) (Google calendar invite) and attend graduation 🥳\n\n## 1. Fill out the swag shipment form.\nInformation submitted to [the swag shipment form](https://airtable.com/shrM5IigBuRFaj33H) will only be used to ship swag for graduation. Submitting the form does not guarantee you will receive swag in the mail. Only the first 5,000 graduates to successfully merge their pull request to the Yearbook will receive a shipment.\n\n## 2. Add yourself to Yearbook 🏫\n\nReplace `` with your GitHub username in this guide.\n\n### First, create the folder _data/YOUR-USERNAME/ \nFork this repository, create a new folder inside the `_data` folder, and name it with your username. It should look something like this `_data//`. Ex.\n\n```\n_data/MonaTheOctocat/\n```\n### Second, add your profile information\nCreate a markdown file in your folder following the convention `.md`. Ex.\n\n```\n_data/MonaTheOctocat/MonaTheOctocat.md\n```\nCopy the next template into your file, delete the boilerplate data and fill the information with yours.\n```\n---\nname: FULLNAME-OR-NICKNAME # No longer than 28 characters\ninstitution: INSTITUTION-NAME 🚩 # no longer than 58 characters\nquote: YOUR-SENIOR-QUOTE # no longer than 100 characters, avoid using quotes(\") to guarantee the format remains the same.\ngithub_user: YOUR-GITHUB-USERNAME\n---\n```\n\n_Do not use special characters in the template above._\n\n### Third, submit your Pull Request\n\nGo through the checklist on the pull request template to guarantee your submission is valid. The GitHub Education team will review your application, approve and merge your submission if everything is correct. Otherwise, you will get notified of the changes requested in the pull request comment section. \n\n# 3. Share your story of a teacher who made an impact 👩‍🏫👨‍🏫\nThis year, we want to hear stories of teachers or educators who made a difference on your journey in 2021. Similar to last year's [\"A Message to the Octograds\"](https://www.youtube.com/watch?v=w5HykygC43Y&t=7s) video, we're putting together a compilation of your stories. Every participant will level up their swag with an extra special design. To take part,\n\n1. [Fill out the video release form.](https://www.docusign.net/Member/PowerFormSigning.aspx?PowerFormId=d460686d-c036-48b4-a69b-2095735affe8&env=na1&acct=8536c841-0a45-4d7f-836a-1d9cd83fec06&v=2)\n2. [Record and submit your video message.](https://github.com/education/GitHubGraduation-2021/files/6448974/GitHub.Graduation.Teacher.Video.Project.pdf) (full instructions for recording)\n\nNot sure what to say? Even a \"thank-you teachers!\" in your local language is appreciated to help us in honoring educators around the world.\n\n[Share your message to teachers.](https://github.com/education/GitHubGraduation-2021/files/6448974/GitHub.Graduation.Teacher.Video.Project.pdf)\n\n# A note on swag 🛍\nThe first 5,000 successfully merged PRs will receive a custom holographic developer trading card with their GitHub status in the mail. \n\nWhat does this mean? We will use your public GitHub profile information to create a trading card. 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"pushed_at": "2020-12-27T03:31:19Z", + "readme_length": 5279, + "readme_text": "# Hacktober-2020\n## Share what you did in 2020!\n\n[![License: MIT](https://img.shields.io/badge/License-MIT-success.svg)](https://github.com/open-minds/Hacktober-fest-2020/blob/master/LICENSE)\n![first-temers](https://img.shields.io/badge/first--timers-friendly-blue)\n![PRs-welcome](https://img.shields.io/badge/Pull%20Requests-Welcome-success)\n![GitHub search hit counter](https://img.shields.io/github/search/open-minds/Hacktober-fest-2020/hacktoberfest?color=red&style=flat-square)\n![GitHub top language](https://img.shields.io/github/languages/top/open-minds/Hacktober-fest-2020?color=teal&style=flat-square)\n![GitHub language count](https://img.shields.io/github/languages/count/open-minds/Hacktober-fest-2020?style=flat-square)\n\n![GitHub closed pull requests](https://img.shields.io/github/issues-pr-closed/open-minds/Hacktober-fest-2020?style=flat-square)\n![GitHub pull requests](https://img.shields.io/github/issues-pr-raw/open-minds/Hacktober-fest-2020?color=green&style=flat-square)\n\n\nRepo for TOM members dedicated for the Hacktober fest of 2020 (7th edition), this repo is here to encourage open source learning and sharing and enhance git/github using abilities. \n\nUse this project to make your first contribution to an open source project on GitHub. Practice making your first pull request to a public repository before doing the real thing!\n\nCelebrate [Hacktoberfest](https://hacktoberfest.digitalocean.com/) by getting involved in the open source community by completing some simple tasks in this project.\n\nThis repository is open to all members of the GitHub community. Any member may contribute to this project without being a collaborator.\n\n## What is Hacktoberfest?\nA month-long celebration from October 1st - 31st sponsored by [Digital Ocean](https://www.digitalocean.com/), [GitHub](https://github.com/), and [Twilio](https://www.twilio.com/) to get people involved in [Open Source](https://github.com/open-source). Create your very first pull request to any public repository on GitHub and contribute to the open source developer community. The first 50,000 people to get 5 pull requests will recieve a free t-shirt!\n\n[https://hacktoberfest.digitalocean.com/](https://hacktoberfest.digitalocean.com/)\n\n## Getting Started - Easy\n* Fork this repository (Click the Fork button in the top right of this page, click your Profile Image)\n* Navigate to `CONTRIBUTORS.md`\n* Edit the file and insert your short profile following the instructions below, then click on Commit Changes.\n* Navigate to the `profiles` directory\n* Click `Create new file` and create a new profile `Your_Full_Name.md`, and commit your changes.\n* Add any piece of code (we recommand you add something in the `Hello World` folder).\n* Create a new pull request from your forked repository (Click the `New Pull Request` button located at the top of your repo)\n* Wait for your PR review and merge approval!\n* __Star this repository__ if you had fun!\n\n## Getting Started - Difficult\n* Fork this repository (Click the Fork button in the top right of this page, click your Profile Image)\n* Install git on your computer https://git-scm.com\n* Clone your fork down to your local machine\n\n```markdown\ngit clone https://github.com/open-minds/Hacktober-fest-2020.git\n```\n\n* Make your changes (choose from any task above)\n* Commit and push\n\n```markdown\ngit add .\ngit commit -m \"Commit message\"\ngit push origin master\n```\n* Create a new pull request from your forked repository (Click the `New Pull Request` button located at the top of your repo)\n* Wait for your PR review and merge approval!\n* __Star this repository__ if you had fun!\n\n\n## Choose from these tasks\n### 1. Add your name\nAdd your name to the `CONTRIBUTORS.md` file using the below convention:\n\n```markdown\n#### Name: [GitHib username](GitHub profile link)\n```\n\n### 2. Add a profile page\nAdd a `Your_Full_Name.md` file to the `profiles` directory. Use any combination of content and Markdown you'd like. Here is an example:\n\n```markdown\n# Your Full Name\n\n### Location\n\nYour City/Country\n\n### Academics\n\nYour School\n\n### Interests\n\n- Some Things You Like\n- Something Else\n\n### Development\n\n- web developper for example\n- game dev as another example\n\n### Profile Link\n\n[GitHib username](GitHub profile link)\n```\n### 3. Add a profile readMe: \n\nAdd a `Your_Full_Name.md` file to the `profiles` directory. Use any combination of content and Markdown you'd like. Here is an *empty* example:\n\n```markdown\n\"Username's\n\n```\n\nHere's a full working example:\n\n\"Oussama's\n\nRead the [Github stats docs](https://github.com/anuraghazra/github-readme-stats) to find out how to customize your card!\n\n## Up for more PRs? Check this quick list for more interesting ressources (first timers friendly):\n* [First Timers Only](https://www.firsttimersonly.com/) Friendly Open Source projects on the go!\n* [FreeCodeCamp](https://github.com/freeCodeCamp/freeCodeCamp) Open source codebase and curriculum. 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If you don't\nknow where to start, feel free to watch the videos linked below, and\nread the contribution rules. Happy hacking <3 !!\n\nP.S. Star and share this repository, if you had fun!\n\n## Videos\n\n- https://www.youtube.com/watch?v=c5SUB2fNW0w\n\n## Getting Started\n\n- Create a [Github](https://github.com/) account for free [here](https://github.com/).\n- Register at [Hactoberfest-2020](https://hacktoberfest.digitalocean.com/) using your github id.\n- Star this repo and share it with your friends.\n- Add your programs/code to respective folders of each programming language.\n\n## Ideas for contributions\n:sparkles:\n- Add your own projects to the 'Helpful Material Page'\n\n## FAQs\n:sparkles:\n- Who can contribute?\n - Anyone with a github account and who is signed up for [hacktoberfest](https://hacktoberfest.digitalocean.com/) :).\n\n- How many pull request (PR) must be made, if I can get an awesome shirt from Hacktoberfest 2020?\n - 5\n\n- How do I track my progress to get an awesome shirt from Hacktoberfest 2020?\n - go to : [https://hacktoberfest.digitalocean.com/stats/](https://hacktoberfest.digitalocean.com/stats/)\n\n- What is the duration of Hacktoberfest 2020?\n - It is from 1st october to 31st october 2020.\n\n- What is the event for?\n - For the open source community engagement.\n\n### *I will do my best to merge as much as possible from everyone. However, time is limited and the merge conflicts are horrible <3*", + "readme_title": "Hacktoberfest 2020 - Expert", + "releases_count": 0, + "repo": "Hacktoberfest2020-Expert", + "repo_name": "Hacktoberfest2020-Expert", + "stars": 0, + "topics": [], + "updated_at": "2020-10-02T19:50:08Z", + "url": "https://github.com/hassan11196/Hacktoberfest2020-Expert", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/Hacktoberfest2020-Expert" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/HassanGatsbyNetlify" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 528, + "contributors_count": 46, + "contributors_top": [ + { + "contributions": 108, + "html_url": "https://github.com/apps/dependabot-preview", + "login": "dependabot-preview[bot]" + }, + { + "contributions": 100, + "html_url": "https://github.com/AustinGreen", + "login": "AustinGreen" + }, + { + "contributions": 97, + "html_url": "https://github.com/ZoltanVeres", + "login": "ZoltanVeres" + }, + { + "contributions": 68, + "html_url": "https://github.com/renovate-bot", + "login": "renovate-bot" + }, + { + "contributions": 38, + "html_url": "https://github.com/erquhart", + "login": "erquhart" + }, + { + "contributions": 32, + "html_url": "https://github.com/apps/renovate", + "login": "renovate[bot]" + }, + { + "contributions": 21, + "html_url": "https://github.com/marcobiedermann", + "login": "marcobiedermann" + }, + { + "contributions": 6, + "html_url": "https://github.com/Benaiah", + "login": "Benaiah" + } + ], + "created_at": "2020-01-07T10:29:20Z", + "default_branch": "master", + "description": null, + "dirs_total_count": 20, + "files_root_entries": [ + { + "path": ".dependabot", + "size": 0, + "type": "dir" + }, + { + "path": ".github", + "size": 0, + "type": "dir" + }, + { + "path": ".gitignore", + "size": 212, + "type": "file" + }, + { + "path": "CODE_OF_CONDUCT.md", + "size": 3349, + "type": "file" + }, + { + "path": "CONTRIBUTING.md", + "size": 2981, + "type": "file" + }, + { + "path": "LICENSE", + "size": 1076, + "type": "file" + }, + { + "path": "PULL_REQUEST_TEMPLATE.md", + "size": 907, + "type": "file" + }, + { + "path": "README.md", + "size": 5995, + "type": "file" + }, + { + "path": "_headers", + "size": 87, + "type": "file" + }, + { + "path": "gatsby-config.js", + "size": 2273, + "type": "file" + }, + { + "path": "gatsby-node.js", + "size": 2090, + "type": "file" + }, + { + "path": "lambda", + "size": 0, + "type": "dir" + }, + { + "path": "netlify.toml", + "size": 163, + "type": "file" + }, + { + "path": "package.json", + "size": 1596, + "type": "file" + }, + { + "path": "renovate.json", + "size": 72, + "type": "file" + }, + { + "path": "src", + "size": 0, + "type": "dir" + }, + { + "path": "static", + "size": 0, + "type": "dir" + }, + { + "path": "yarn.lock", + "size": 648875, + "type": "file" + } + ], + "files_total_count": 79, + "first_commit_date_default_branch": "2017-10-18T21:43:20Z", + "forks": 0, + "has_ci": true, + "has_contributing": true, + "has_docker": false, + "has_docs": false, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/HassanGatsbyNetlify", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "JavaScript", + "size": 54758 + }, + { + "language": "CSS", + "size": 2877 + } + ], + "last_commit_date_default_branch": "2020-01-07T11:29:52Z", + "last_commit_oid_default_branch": "5cd5e40e28c9bc8861e06e70f45c3889b2944807", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "MIT License", + "license_spdx": "MIT", + "name_with_owner": "hassan11196/HassanGatsbyNetlify", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "JavaScript", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 2, + "pull_requests_total": 2, + "pushed_at": "2023-01-11T22:00:00Z", + "readme_length": 5988, + "readme_text": "# Gatsby + Netlify CMS Starter\n\n[![Netlify Status](https://api.netlify.com/api/v1/badges/b654c94e-08a6-4b79-b443-7837581b1d8d/deploy-status)](https://app.netlify.com/sites/gatsby-starter-netlify-cms-ci/deploys)\n\n**Note:** This starter uses [Gatsby v2](https://www.gatsbyjs.org/blog/2018-09-17-gatsby-v2/).\n\nThis repo contains an example business website that is built with [Gatsby](https://www.gatsbyjs.org/), and [Netlify CMS](https://www.netlifycms.org): **[Demo Link](https://gatsby-netlify-cms.netlify.com/)**.\n\nIt follows the [JAMstack architecture](https://jamstack.org) by using Git as a single source of truth, and [Netlify](https://www.netlify.com) for continuous deployment, and CDN distribution.\n\n## Features\n\n- A simple landing page with blog functionality built with Netlify CMS\n- Editabe Pages: Landing, About, Product, Blog-Collection and Contact page with Netlify Form support\n- Create Blog posts from Netlify CMS\n- Tags: Separate page for posts under each tag\n- Basic directory organization\n- Uses Bulma for styling, but size is reduced by `purge-css-plugin`\n- Blazing fast loading times thanks to pre-rendered HTML and automatic chunk loading of JS files\n- Uses `gatsby-image` with Netlify-CMS preview support\n- Separate components for everything\n- Netlify deploy configuration\n- Netlify function support, see `lambda` folder\n- Perfect score on Lighthouse for SEO, Accessibility and Performance (wip:PWA)\n- ..and more\n\n## Prerequisites\n\n- Node (I recommend using v8.2.0 or higher)\n- [Gatsby CLI](https://www.gatsbyjs.org/docs/)\n- [Netlify CLI](https://github.com/netlify/cli)\n\n## Getting Started (Recommended)\n\nNetlify CMS can run in any frontend web environment, but the quickest way to try it out is by running it on a pre-configured starter site with Netlify. The example here is the Kaldi coffee company template (adapted from [One Click Hugo CMS](https://github.com/netlify-templates/one-click-hugo-cms)). Use the button below to build and deploy your own copy of the repository:\n\n\"Deploy\n\nAfter clicking that button, you’ll authenticate with GitHub and choose a repository name. Netlify will then automatically create a repository in your GitHub account with a copy of the files from the template. Next, it will build and deploy the new site on Netlify, bringing you to the site dashboard when the build is complete. Next, you’ll need to set up Netlify’s Identity service to authorize users to log in to the CMS.\n\n### Access Locally\n\nPulldown a local copy of the Github repository Netlify created for you, with the name you specified in the previous step\n```\n$ git clone https://github.com/[GITHUB_USERNAME]/[REPO_NAME].git\n$ cd [REPO_NAME]\n$ yarn\n$ netlify dev # or ntl dev\n```\n\nThis uses the new [Netlify Dev](https://www.netlify.com/products/dev/?utm_source=blog&utm_medium=netlifycms&utm_campaign=devex) CLI feature to serve any functions you have in the `lambda` folder.\n\nTo test the CMS locally, you'll need run a production build of the site:\n\n```\n$ npm run build\n$ netlify dev # or ntl dev\n```\n\n### Media Libraries (installed, but optional)\n\nMedia Libraries have been included in this starter as a default. If you are not planning to use `Uploadcare` or `Cloudinary` in your project, you **can** remove them from module import and registration in `src/cms/cms.js`. Here is an example of the lines to comment or remove them your project.\n\n```javascript\nimport CMS from 'netlify-cms-app'\n// import uploadcare from 'netlify-cms-media-library-uploadcare'\n// import cloudinary from 'netlify-cms-media-library-cloudinary'\n\nimport AboutPagePreview from './preview-templates/AboutPagePreview'\nimport BlogPostPreview from './preview-templates/BlogPostPreview'\nimport ProductPagePreview from './preview-templates/ProductPagePreview'\nimport IndexPagePreview from './preview-templates/IndexPagePreview'\n\n// CMS.registerMediaLibrary(uploadcare);\n// CMS.registerMediaLibrary(cloudinary);\n\nCMS.registerPreviewTemplate('index', IndexPagePreview)\nCMS.registerPreviewTemplate('about', AboutPagePreview)\nCMS.registerPreviewTemplate('products', ProductPagePreview)\nCMS.registerPreviewTemplate('blog', BlogPostPreview)\n```\n\nNote: Don't forget to also remove them from `package.json` and `yarn.lock` / `package-lock.json` using `yarn` or `npm`. During the build netlify-cms-app will bundle the media libraries as well, having them removed will save you build time.\nExample:\n```\nyarn remove netlify-cms-media-library-uploadcare\n```\nOR\n```\nyarn remove netlify-cms-media-library-cloudinary\n```\n## Getting Started (Without Netlify)\n\n```\n$ gatsby new [SITE_DIRECTORY_NAME] https://github.com/netlify-templates/gatsby-starter-netlify-cms/\n$ cd [SITE_DIRECTORY_NAME]\n$ npm run build\n$ npm run serve\n```\n\n### Setting up the CMS\n\nFollow the [Netlify CMS Quick Start Guide](https://www.netlifycms.org/docs/quick-start/#authentication) to set up authentication, and hosting.\n\n## Debugging\n\nWindows users might encounter `node-gyp` errors when trying to npm install.\nTo resolve, make sure that you have both Python 2.7 and the Visual C++ build environment installed.\n\n```\nnpm config set python python2.7\nnpm install --global --production windows-build-tools\n```\n\n[Full details here](https://www.npmjs.com/package/node-gyp 'NPM node-gyp page')\n\nMacOS users might also encounter some errors, for more info check [node-gyp](https://github.com/nodejs/node-gyp). We recommend using the latest stable node version.\n\n## Purgecss\n\nThis plugin uses [gatsby-plugin-purgecss](https://www.gatsbyjs.org/packages/gatsby-plugin-purgecss/) and [bulma](https://bulma.io/). The bulma builds are usually ~170K but reduced 90% by purgecss.\n\n# CONTRIBUTING\n\nContributions are always welcome, no matter how large or small. Before contributing,\nplease read the [code of conduct](CODE_OF_CONDUCT.md).", + "readme_title": "Gatsby + Netlify CMS Starter", + "releases_count": 0, + "repo": "HassanGatsbyNetlify", + "repo_name": "HassanGatsbyNetlify", + "stars": 0, + "topics": [], + "updated_at": "2020-01-07T11:29:55Z", + "url": "https://github.com/hassan11196/HassanGatsbyNetlify", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/HassanGatsbyNetlify" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/HassanPortfolioGatsby" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 246, + "contributors_count": 11, + "contributors_top": [ + { + "contributions": 86, + "html_url": "https://github.com/apps/renovate", + "login": "renovate[bot]" + }, + { + "contributions": 77, + "html_url": "https://github.com/LekoArts", + "login": "LekoArts" + }, + { + "contributions": 70, + "html_url": null, + "login": "LekoArts" + }, + { + "contributions": 6, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + }, + { + "contributions": 1, + "html_url": "https://github.com/anmolbajajnet", + "login": "anmolbajajnet" + }, + { + "contributions": 1, + "html_url": "https://github.com/CanRau", + "login": "CanRau" + }, + { + "contributions": 1, + "html_url": "https://github.com/d2s", + "login": "d2s" + }, + { + "contributions": 1, + "html_url": "https://github.com/douglasjunior", + "login": "douglasjunior" + } + ], + "created_at": "2019-12-22T22:14:45Z", + "default_branch": "master", + "description": null, + "dirs_total_count": 9, + "files_root_entries": [ + { + "path": ".github", + "size": 0, + "type": "dir" + }, + { + "path": ".gitignore", + "size": 970, + "type": "file" + }, + { + "path": "CHANGELOG.md", + "size": 3233, + "type": "file" + }, + { + "path": "LICENSE", + "size": 1065, + "type": "file" + }, + { + "path": "README.md", + "size": 5622, + "type": "file" + }, + { + "path": "gatsby-config.js", + "size": 1796, + "type": "file" + }, + { + "path": "package-lock.json", + "size": 708840, + "type": "file" + }, + { + "path": "package.json", + "size": 1038, + "type": "file" + }, + { + "path": "src", + "size": 0, + "type": "dir" + }, + { + "path": "static", + "size": 0, + "type": "dir" + }, + { + "path": "yarn.lock", + "size": 532028, + "type": "file" + } + ], + "files_total_count": 26, + "first_commit_date_default_branch": "2018-06-02T19:41:22Z", + "forks": 1, + "has_ci": true, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": "https://hassangatsby.netlify.app/", + "input_url": "https://github.com/hassan11196/HassanPortfolioGatsby", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "JavaScript", + "size": 2915 + }, + { + "language": "HTML", + "size": 366 + } + ], + "last_commit_date_default_branch": "2020-01-12T16:42:53Z", + "last_commit_oid_default_branch": "6d8baeeb00deaf6ed92f3b2b15a1cffe32da915b", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "MIT License", + "license_spdx": "MIT", + "name_with_owner": "hassan11196/HassanPortfolioGatsby", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "JavaScript", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 1, + "pull_requests_total": 1, + "pushed_at": "2023-06-10T00:29:15Z", + "readme_length": 5604, + "readme_text": "

\n \n \"LekoArts\"\n \n

\n

\n Gatsby Starter Portfolio: Cara\n

\n\n

\n \n \"Gatsby\n \n \"PRs\n \n \"Follow\n \n \n \"Netlify\n \n

\n\nPlayful and Colorful One-Page portfolio featuring Parallax effects and animations. Using the Gatsby Theme [`@lekoarts/gatsby-theme-cara`](https://github.com/LekoArts/gatsby-themes/tree/master/themes/gatsby-theme-cara).\n\n[**Demo Website**](https://cara.lekoarts.de)\n\nAlso be sure to checkout other [Free & Open Source Gatsby Themes](https://themes.lekoarts.de)\n\n## ✨ Features\n\n- Theme UI-based theming\n- react-spring parallax effect\n- CSS Animations on Shapes\n\n## 🚀 Getting Started\n\n[![Deploy to Netlify](https://www.netlify.com/img/deploy/button.svg)](https://app.netlify.com/start/deploy?repository=https://github.com/LekoArts/gatsby-starter-portfolio-cara) [![Edit gatsby-starter-portfolio-cara](https://codesandbox.io/static/img/play-codesandbox.svg)](https://codesandbox.io/s/github/LekoArts/gatsby-starter-portfolio-cara/tree/master/)\n\n1. **Create a Gatsby site.**\n\nUse the Gatsby CLI to create a new site, specifying this project\n\n```sh\ngatsby new project-name https://github.com/LekoArts/gatsby-starter-portfolio-cara\n```\n\n2. **Start developing.**\n\nNavigate into your new site's directory and start it up.\n\n```sh\ncd project-name\ngatsby develop\n```\n\n3. **Open the code and start customizing!**\n\nYour site is now running at `http://localhost:8000`!\n\nIf you want to learn more about how you can use a Gatsby starter that is configured with a Gatsby theme, you can checkout this [shorter](https://www.gatsbyjs.org/docs/themes/using-a-gatsby-theme/) or [longer](https://www.gatsbyjs.org/tutorial/using-a-theme/) tutorial. The tutorials don't exactly apply to this starter however the concepts are the same.\n\n## 📝 Using and modifying this starter\n\n**Important Note:** Please read the guide [Shadowing in Gatsby Themes](https://www.gatsbyjs.org/docs/themes/shadowing/) to understand how to customize the underlying theme!\n\nThis starter creates a new Gatsby site that installs and configures the theme [`@lekoarts/gatsby-theme-cara`](https://github.com/LekoArts/gatsby-themes/tree/master/themes/gatsby-theme-cara).\n\nHave a look at the theme's README and files to see what options are available and how you can shadow the various components including Theme UI. Generally speaking you will want to place your files into `src/@lekoarts/gatsby-theme-cara/` to shadow/override files. The Theme UI config can be configured by shadowing its files in `src/gatsby-plugin-theme-ui/`.\n\n### Changing content\n\nThe content of this project is defined in four `.mdx` files inside the theme's `sections` folder. You can override the files `intro.mdx`, `projects.mdx`, `about.mdx` and `contact.mdx`. This starter has overriden the `intro.mdx` file as an example. Place the other files in the same `src/@lekoarts/gatsby-theme-cara/sections/` folder.\n\nYou have to use the `` component inside `projects.mdx` to display the cards. Example:\n\n```md\n## Projects\n\n\nThis project is my entry to Adobe's #ChallengeYourPerspective contest.\n\n```\n\n### Change your `static` folder\n\nThe `static` folder contains the icons, social media images and robots.txt. Don't forget to change these files, too!\n\n## 🤔 Questions or problems?\n\nPlease open up an issue on the main repository: [LekoArts/gatsby-themes](https://github.com/LekoArts/gatsby-themes). Thanks!\n\n## 🎓 Learning Gatsby\n\nLooking for more guidance? Full documentation for Gatsby lives [on Gatsby's website](https://www.gatsbyjs.org/).\n\n### Themes\n\n- To learn more about Gatsby themes specifically, we recommend checking out the [theme docs](https://www.gatsbyjs.org/docs/themes/).\n\n### General\n\n- **For most developers, I recommend starting with the [in-depth tutorial for creating a site with Gatsby](https://www.gatsbyjs.org/tutorial/).** It starts with zero assumptions about your level of ability and walks through every step of the process.\n\n- **To dive straight into code samples, head [to Gatsby's documentation](https://www.gatsbyjs.org/docs/).** In particular, check out the _Reference Guides_ and _Gatsby API_ sections in the sidebar.\n\n## 🌟 Supporting me\n\nThanks for using this project! I'm always interested in seeing what people do with my projects, so don't hesitate to tag me on [Twitter](https://twitter.com/lekoarts_de) and share the project with me.\n\nPlease star this project, share it on Social Media or consider supporting me on [Patreon](https://www.patreon.com/lekoarts)!\n\nIf you want to hire me for **contract/freelance work**, you can do so! [Get in touch with me!](https://www.lekoarts.de/en/contact)", + "readme_title": "

", + "releases_count": 0, + "repo": "HassanPortfolioGatsby", + "repo_name": "HassanPortfolioGatsby", + "stars": 0, + "topics": [], + "updated_at": "2021-09-19T11:28:40Z", + "url": "https://github.com/hassan11196/HassanPortfolioGatsby", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/HassanPortfolioGatsby" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/House-Prices-Advanced-Regression-Techniques" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 5, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 5, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + } + ], + "created_at": "2020-07-26T12:13:46Z", + "default_branch": "master", + "description": "House Prices: Advanced Regression Techniques Predict sales prices and practice feature engineering, RFs, and gradient boosting.", + "dirs_total_count": 3, + "files_root_entries": [ + { + "path": ".gitignore", + "size": 9, + "type": "file" + }, + { + "path": ".ipynb_checkpoints", + "size": 0, + "type": "dir" + }, + { + "path": "PlayGround-1.ipynb", + "size": 154090, + "type": "file" + }, + { + "path": "README.md", + "size": 295, + "type": "file" + }, + { + "path": "data", + "size": 0, + "type": "dir" + }, + { + "path": "submissions", + "size": 0, + "type": "dir" + } + ], + "files_total_count": 37, + "first_commit_date_default_branch": "2020-07-26T12:13:48Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": true, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/House-Prices-Advanced-Regression-Techniques", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Jupyter Notebook", + "size": 211937 + } + ], + "last_commit_date_default_branch": "2020-08-03T20:55:20Z", + "last_commit_oid_default_branch": "17f39a9f607199b9055f2a2b278ba5a3a23fd484", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/House-Prices-Advanced-Regression-Techniques", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "Jupyter Notebook", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2020-08-03T20:55:35Z", + "readme_length": 294, + "readme_text": "# House Prices: Advanced Regression Techniques\n\nPredict sales prices and practice feature engineering, RFs, and gradient boosting.\n\nNotebook : [PlayGround-1.ipynb](./PlayGround-1.ipynb)\n\n## References: \n[Kaggle Competition](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/)", + "readme_title": "House Prices: Advanced Regression Techniques", + "releases_count": 0, + "repo": "House-Prices-Advanced-Regression-Techniques", + "repo_name": "House-Prices-Advanced-Regression-Techniques", + "stars": 0, + "topics": [], + "updated_at": "2021-09-11T17:17:35Z", + "url": "https://github.com/hassan11196/House-Prices-Advanced-Regression-Techniques", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/House-Prices-Advanced-Regression-Techniques" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/IR-A1" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 26, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 26, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + } + ], + "created_at": "2020-02-19T13:29:26Z", + "default_branch": "master", + "description": "A Django Application to serve as back-end and Jupyter Notebook Prototype for Information Retrieval - Assignment 1 - CS317 - FAST NUCES (Karachi)", + "dirs_total_count": 34, + "files_root_entries": [ + { + "path": ".gitignore", + "size": 1681, + "type": "file" + }, + { + "path": ".ipynb_checkpoints", + "size": 0, + "type": "dir" + }, + { + "path": ".vscode", + "size": 0, + "type": "dir" + }, + { + "path": "CS317-IR-A1-Spring 2020.pdf", + "size": 277815, + "type": "file" + }, + { + "path": "DjangoApp", + "size": 0, + "type": "dir" + }, + { + "path": "IR-A1-K173654.ipynb", + "size": 55609, + "type": "file" + }, + { + "path": "IR-A1-K173654.zip", + "size": 18437588, + "type": "file" + }, + { + "path": "IR-A1-Notebook.ipynb", + "size": 74902, + "type": "file" + }, + { + "path": "IR-A1-Notebook.py", + "size": 13022, + "type": "file" + }, + { + "path": "IR-SYS-A1", + "size": 0, + "type": "file" + }, + { + "path": "IR-WEB-A1", + "size": 0, + "type": "dir" + }, + { + "path": "Pipfile", + "size": 252, + "type": "file" + }, + { + "path": "Procfile", + "size": 62, + "type": "file" + }, + { + "path": "Querry List New.txt", + "size": 3259, + "type": "file" + }, + { + "path": "Querry List.txt", + "size": 2131, + "type": "file" + }, + { + "path": "README.md", + "size": 2711, + "type": "file" + }, + { + "path": "Stopword-List.txt", + "size": 101, + "type": "file" + }, + { + "path": "Submission", + "size": 0, + "type": "dir" + }, + { + "path": "Test.ipynb", + "size": 27755, + "type": "file" + }, + { + "path": "data", + "size": 0, + "type": "dir" + }, + { + "path": "internals", + "size": 0, + "type": "dir" + }, + { + "path": "pickled", + "size": 0, + "type": "dir" + }, + { + "path": "query_parser.py", + "size": 9166, + "type": "file" + } + ], + "files_total_count": 360, + "first_commit_date_default_branch": "2020-02-19T13:30:19Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": true, + "has_readme_file": true, + "has_tests": false, + "homepage_url": "https://ira1.netlify.com/#/", + "input_url": "https://github.com/hassan11196/IR-A1", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Jupyter Notebook", + "size": 428385 + }, + { + "language": "Python", + "size": 83854 + }, + { + "language": "JavaScript", + "size": 4728 + } + ], + "last_commit_date_default_branch": "2020-06-16T19:17:31Z", + "last_commit_oid_default_branch": "03562f3d2c9f270f125ba7afa43cd4855ab2b314", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/IR-A1", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "Jupyter Notebook", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2020-06-16T19:17:44Z", + "readme_length": 2708, + "readme_text": "# IR-Assignment # 1 \n## Implementation:\n- Indexed Given Documents of Trump Speeches using a Inverted Index. \n- Designed a Query Engine using a Abstract Syntax Tree that can parse complex Boolean queries that have nested brackets.\n- Pickled the inverted index for quick retrieval for query purposes.\n## Files:\n### Jupyter Notebook:\nThe Notebook Contains a Inverted Index Implementation with options to query Boolean, Phrasal, Proximity Queries.\n\n- Check Notebook : IR-A1-K173654.ipynb\n\n### Desktop System:\nA GUI Based Executable is present that provides a Desktop Interface to the Django based web server which holds the implementation of Inverted Index hosted on Heroku. It can evaluate and return results for queries.\n\n- Check Zip File For Desktop GUI Code : IR-SYS-A1\n - Repo Link: [Click Here](https://github.com/hassan11196/IR-SYS-A1)\n- Check Folder For Desktop GUI Executable : IR-APP-A1\n - Exe File: [Click Here](http://www.mediafire.com/file/u3zqzu1lvfpeedw/IR-SYS-A1-K173654_Setup_1.0.0.exe/file)\n- Check Folder For Python Django Web Server : IR-SERVER-A1\n - Repo Link: [Click Here](https://github.com/hassan11196/IR-A1/tree/master/DjangoApp/IRA1)\n\n### Web System:\nA Web Application is hosted at [https://ira1.netlify.com/#/](https://ira1.netlify.com/#/) .\n\n- Check Zip File for Web Interface Code : IR-WEB-A1\n - Repo Link : [Click Here](https://github.com/hassan11196/IR-WEB-A1)\n \n### ScreenShots:\n\n- ![](Submission/Screenshots/Annotation 2020-03-10 205835.png)\n- ![](Submission/Screenshots/Annotation 2020-03-10 205849.png)\n- ![](Submission/Screenshots/Annotation 2020-03-10 205854.png)\n\n### Built Using:\n\n

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\n\n### Template:\n- [electron react boilerplate](https://github.com/electron-react-boilerplate/electron-react-boilerplate)", + "readme_title": "IR-Assignment # 1", + "releases_count": 0, + "repo": "IR-A1", + "repo_name": "IR-A1", + "stars": 1, + "topics": [], + "updated_at": "2020-06-19T04:49:49Z", + "url": "https://github.com/hassan11196/IR-A1", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/IR-A1" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/IR-A2" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 8, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 8, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + } + ], + "created_at": "2020-04-10T16:37:53Z", + "default_branch": "master", + "description": "A Django Application to serve as back-end and Jupyter Notebook Prototype for Information Retrieval - Assignment 2 - CS317 - FAST NUCES (Karachi)", + "dirs_total_count": 20, + "files_root_entries": [ + { + "path": ".gitignore", + "size": 1716, + "type": "file" + }, + { + "path": ".ipynb_checkpoints", + "size": 0, + "type": "dir" + }, + { + "path": ".vscode", + "size": 0, + "type": "dir" + }, + { + "path": "CS317-IR-A2-Spring 2020.pdf", + "size": 289635, + "type": "file" + }, + { + "path": "IR-A2-K173654-sciket.ipynb", + "size": 103629, + "type": "file" + }, + { + "path": "IR-A2-K173654.ipynb", + "size": 52840, + "type": "file" + }, + { + "path": "IR-K173654-A2-V2.zip", + "size": 46196439, + "type": "file" + }, + { + "path": "IR-SERVER-A2", + "size": 0, + "type": "dir" + }, + { + "path": "Pipfile", + "size": 287, + "type": "file" + }, + { + "path": "Procfile", + "size": 65, + "type": "file" + }, + { + "path": "Query List VSM.txt", + "size": 3078, + "type": "file" + }, + { + "path": "README.md", + "size": 2469, + "type": "file" + }, + { + "path": "Screenshots", + "size": 0, + "type": "dir" + }, + { + "path": "Stopword-List.txt", + "size": 101, + "type": "file" + }, + { + "path": "data", + "size": 0, + "type": "dir" + }, + { + "path": "internals", + "size": 0, + "type": "dir" + }, + { + "path": "pickled", + "size": 0, + "type": "dir" + } + ], + "files_total_count": 208, + "first_commit_date_default_branch": "2020-04-10T16:40:13Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": true, + "has_readme_file": true, + "has_tests": false, + "homepage_url": "https://ira2.netlify.com/#/", + "input_url": "https://github.com/hassan11196/IR-A2", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Jupyter Notebook", + "size": 310214 + }, + { + "language": "Python", + "size": 51092 + }, + { + "language": "JavaScript", + "size": 4728 + } + ], + "last_commit_date_default_branch": "2020-06-16T19:22:06Z", + "last_commit_oid_default_branch": "d253780f14d8e94bedf266ea4d5fe08a707bd843", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/IR-A2", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "Jupyter Notebook", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2020-06-16T19:22:21Z", + "readme_length": 2466, + "readme_text": "# IR-Assignment # 2 \n## Implementation:\n- Indexed Given Documents of Trump Speeches using a Vector Space Model. \n- Pickled the inverted index for quick retrieval for query purposes.\n## Files:\n### Jupyter Notebook:\nThe Notebook Contains a Vector Space Model Implementation.\n\n- Check Notebook : IR-A2-K173654.ipynb\n\n### Desktop System:\nA GUI Based Executable is present that provides a Desktop Interface to the Django based web server which holds the implementation of Inverted Index hosted on Heroku. It can evaluate and return results for queries.\n\n- Check Zip File For Desktop GUI Code : IR-SYS-A2\n - Repo Link: [Click Here](https://github.com/hassan11196/IR-SYS-A2)\n- Check Folder For Desktop GUI Executable : IR-APP-A2\n - Exe File: [Click Here](https://www.mediafire.com/file/asb4p9y0i00iy7n/IR-SYS-A2-K173654_Setup_1.0.0.exe/file)\n- Check Folder For Python Django Web Server : IR-SERVER-A2\n - Repo Link: [Click Here](https://github.com/hassan11196/IR-A2/tree/master/IR-SERVER-A2/IRA2)\n\n### Web System:\nA Web Application is hosted at [https://ira2.netlify.com/#/](https://ira2.netlify.com/#/) .\n\n- Check Zip File for Web Interface Code : IR-WEB-A2\n - Repo Link : [Click Here](https://github.com/hassan11196/IR-WEB-A2)\n \n### ScreenShots:\n- ![](Screenshots/Annotation%202020-04-10%20234906.png)\n- ![](Screenshots/ss1.png)\n- ![](Screenshots/ss2.png)\n- ![](Screenshots/ss3.png)\n\n\n\n### Built Using:\n\n
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\n\n### Template:\n- [electron react boilerplate](https://github.com/electron-react-boilerplate/electron-react-boilerplate)", + "readme_title": "IR-Assignment # 2", + "releases_count": 0, + "repo": "IR-A2", + "repo_name": "IR-A2", + "stars": 0, + "topics": [], + "updated_at": "2020-06-16T19:24:14Z", + "url": "https://github.com/hassan11196/IR-A2", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/IR-A2" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/IR-A3" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 16, + "contributors_count": 2, + "contributors_top": [ + { + "contributions": 14, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + }, + { + "contributions": 2, + "html_url": "https://github.com/EishaMazhar", + "login": "EishaMazhar" + } + ], + "created_at": "2020-05-11T06:20:00Z", + "default_branch": "master", + "description": "A Django Application to serve as back-end and Jupyter Notebook Prototype for Information Retrieval - Assignment 3 - CS317 - FAST NUCES (Karachi)", + "dirs_total_count": 33, + "files_root_entries": [ + { + "path": ".gitignore", + "size": 1790, + "type": "file" + }, + { + "path": ".ipynb_checkpoints", + "size": 0, + "type": "dir" + }, + { + "path": "CS317-IR-A3-Spring 2020.pdf", + "size": 201589, + "type": "file" + }, + { + "path": "IR-A3-K173654-RoughBook.ipynb", + "size": 764167, + "type": "file" + }, + { + "path": "IR-A3-K173654.ipynb", + "size": 151740, + "type": "file" + }, + { + "path": "IR-K173654-A3.zip", + "size": 9928335, + "type": "file" + }, + { + "path": "IR-SERVER-A3", + "size": 0, + "type": "dir" + }, + { + "path": "KMeans.sav", + "size": 50227816, + "type": "file" + }, + { + "path": "KNN.sav", + "size": 39932761, + "type": "file" + }, + { + "path": "Pipfile", + "size": 315, + "type": "file" + }, + { + "path": "Procfile", + "size": 126, + "type": "file" + }, + { + "path": "README.md", + "size": 3265, + "type": "file" + }, + { + "path": "Screenshots", + "size": 0, + "type": "dir" + }, + { + "path": "Stopword-List.txt", + "size": 1780, + "type": "file" + }, + { + "path": "VectorSpace", + "size": 81827574, + "type": "file" + }, + { + "path": "bbcsport-fulltext.zip", + "size": 869685, + "type": "file" + }, + { + "path": "data", + "size": 0, + "type": "dir" + }, + { + "path": "internals", + "size": 0, + "type": "dir" + } + ], + "files_total_count": 1641, + "first_commit_date_default_branch": "2020-05-11T06:28:00Z", + "forks": 1, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": true, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/IR-A3", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 1, + "issues_open": 0, + "issues_total": 1, + "languages_top": [ + { + "language": "Jupyter Notebook", + "size": 1831606 + }, + { + "language": "Python", + "size": 77875 + }, + { + "language": "JavaScript", + "size": 4728 + } + ], + "last_commit_date_default_branch": "2021-10-31T15:50:14Z", + "last_commit_oid_default_branch": "dd1a6b0a3dade63a359c6dd99ab4e7bb080e6552", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/IR-A3", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "Jupyter Notebook", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 1, + "pull_requests_open": 0, + "pull_requests_total": 1, + "pushed_at": "2021-10-31T15:50:14Z", + "readme_length": 3262, + "readme_text": "# IR-Assignment # 3 \n## Implementation:\n- Classify and Cluster Given Documents of BBC Sport using a Knn and K-means. \n\n## Files:\n### Jupyter Notebook:\nThe Notebook Contains a KNN and K-means Implementation.\n\n- Check Notebook : IR-A3-K173654.ipynb\n\n#### KNN Algorithm \n**Approach:** Supervised\n\nA k-nearest-neighbor is a data classification algorithm that attempts to determine what group a data point is in by looking at the data points around it.\n\nAn algorithm, looking at one point on a grid, trying to determine if a point is in group A or B, looks at the states of the points that are near it. The range is arbitrarily determined, but the point is to take a sample of the data. If the majority of the points are in group A, then it is likely that the data point in question will be A rather than B, and vice versa.\n\n#### K-Mean Algorithm\n**Approach:** Unsupervised\n\nK-means clustering is a method used for clustering analysis, especially in data mining and statistics. It aims to partition a set of observations into a number of clusters (k), resulting in the partitioning of the data into Voronoi cells. It can be considered a method of finding out which group a certain object really belongs to.\n\n\n### Desktop System:\nA GUI Based Executable is present that provides a Desktop Interface to the Django based web server which holds the implementation of KNN and Kmeans hosted on Heroku. It can classify and cluster documents and return results.\n\n- Check Zip File For Desktop GUI Code : IR-SYS-A3\n - Repo Link: [Click Here](https://github.com/hassan11196/IR-SYS-A3)\n- Check Folder For Desktop GUI Executable : IR-APP-A3\n\n- Check Folder For Python Django Web Server : IR-SERVER-A3\n - Repo Link: [Click Here](https://github.com/hassan11196/IR-A3/tree/master/IR-SERVER-A3/IRA3)\n\n\n### Web System:\nA Web Application is hosted at [https://ira3.netlify.com/#/](https://ira3.netlify.com/#/) .\n\n- Check Folder for Web Interface Code : IR-WEB-A3 \n - Repo Link : [Click Here](https://github.com/hassan11196/IR-WEB-A3)\n\n\n### ScreenShots:\n\n- ![](Screenshots/Annotation%202020-05-18%20080928.png)\n\n\n### References:\n[http://mlg.ucd.ie/datasets/bbc.html](http://mlg.ucd.ie/datasets/bbc.html)\n\n\n### Built Using:\n\n
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\n\n### Template:\n- [electron react boilerplate](https://github.com/electron-react-boilerplate/electron-react-boilerplate)", + "readme_title": "IR-Assignment # 3", + "releases_count": 0, + "repo": "IR-A3", + "repo_name": "IR-A3", + "stars": 0, + "topics": [ + "hacktober", + "hacktoberfest" + ], + "updated_at": "2021-11-01T06:29:14Z", + "url": "https://github.com/hassan11196/IR-A3", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/IR-A3" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/IR-CA" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 2, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 2, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + } + ], + "created_at": "2020-05-02T09:55:15Z", + "default_branch": "master", + "description": "Information Retrieval - Class Activities - CS317 - FAST NUCES (Karachi)", + "dirs_total_count": 2, + "files_root_entries": [ + { + "path": "CA1", + "size": 0, + "type": "dir" + }, + { + "path": "CA2", + "size": 0, + "type": "dir" + }, + { + "path": "README.md", + "size": 55, + "type": "file" + } + ], + "files_total_count": 5, + "first_commit_date_default_branch": "2020-05-02T09:55:37Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/IR-CA", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [], + "last_commit_date_default_branch": "2020-05-02T09:57:14Z", + "last_commit_oid_default_branch": "a054e4bcf9e2c758c1329504352aad472573cb47", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/IR-CA", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": null, + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2020-05-02T09:57:23Z", + "readme_length": 55, + "readme_text": "# Information Retrieval\n\n\nClass Activities by Sir Rafi.", + "readme_title": "Information Retrieval", + "releases_count": 0, + "repo": "IR-CA", + "repo_name": "IR-CA", + "stars": 0, + "topics": [], + "updated_at": "2021-04-18T18:02:43Z", + "url": "https://github.com/hassan11196/IR-CA", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/IR-CA" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/IR-SYS-A1" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 4, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 4, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + } + ], + "created_at": "2020-03-08T18:05:57Z", + "default_branch": "master", + "description": "An Electron based Front-end Desktop Application For Information Retrieval - Assignment 1 - FAST NUCES (Karachi)", + "dirs_total_count": 30, + "files_root_entries": [ + { + "path": ".dockerignore", + "size": 762, + "type": "file" + }, + { + "path": ".editorconfig", + "size": 188, + "type": "file" + }, + { + "path": ".eslintignore", + "size": 838, + "type": "file" + }, + { + "path": ".eslintrc.js", + "size": 461, + "type": "file" + }, + { + "path": ".gitattributes", + "size": 81, + "type": "file" + }, + { + "path": ".gitignore", + "size": 783, + "type": "file" + }, + { + "path": ".prettierrc.json", + "size": 185, + "type": "file" + }, + { + "path": ".stylelintrc", + "size": 76, + "type": "file" + }, + { + "path": ".travis.yml", + "size": 1665, + "type": "file" + }, + { + "path": ".vscode", + "size": 0, + "type": "dir" + }, + { + "path": "CHANGELOG.md", + "size": 15300, + "type": "file" + }, + { + "path": "CODE_OF_CONDUCT.md", + "size": 3366, + "type": "file" + }, + { + "path": "LICENSE", + "size": 1102, + "type": "file" + }, + { + "path": "README.md", + "size": 2670, + "type": "file" + }, + { + "path": "Screenshots", + "size": 0, + "type": "dir" + }, + { + "path": "app", + "size": 0, + "type": "dir" + }, + { + "path": "appveyor.yml", + "size": 493, + "type": "file" + }, + { + "path": "azure-pipelines.yml", + "size": 1306, + "type": "file" + }, + { + "path": "babel.config.js", + "size": 2146, + "type": "file" + }, + { + "path": "configs", + "size": 0, + "type": "dir" + }, + { + "path": "internals", + "size": 0, + "type": "dir" + }, + { + "path": "package.json", + "size": 11579, + "type": "file" + }, + { + "path": "renovate.json", + "size": 27, + "type": "file" + }, + { + "path": "resources", + "size": 0, + "type": "dir" + }, + { + "path": "test", + "size": 0, + "type": "dir" + }, + { + "path": "tsconfig.json", + "size": 889, + "type": "file" + }, + { + "path": "yarn.lock", + "size": 673450, + "type": "file" + } + ], + "files_total_count": 189, + "first_commit_date_default_branch": "2020-03-08T18:08:24Z", + "forks": 0, + "has_ci": true, + "has_contributing": false, + "has_docker": true, + "has_docs": false, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": true, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/IR-SYS-A1", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "TypeScript", + "size": 48158 + }, + { + "language": "JavaScript", + "size": 25471 + }, + { + "language": "CSS", + "size": 1924 + }, + { + "language": "HTML", + "size": 1491 + } + ], + "last_commit_date_default_branch": "2020-06-16T19:19:12Z", + "last_commit_oid_default_branch": "607a0be82a9cc479fcb872e5672bf5072ec378ef", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "MIT License", + "license_spdx": "MIT", + "name_with_owner": "hassan11196/IR-SYS-A1", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "TypeScript", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2020-06-16T19:20:02Z", + "readme_length": 2666, + "readme_text": "# IR-Assignment # 1 \n## Implementation:\n- Indexed Given Documents of Trump Speeches using a Inverted Index. \n- Designed a Query Engine using a Abstract Syntax Tree that can parse complex Boolean queries that have nested brackets.\n- Pickled the inverted index for quick retrieval for query purposes.\n## Files:\n### Jupyter Notebook:\nThe Notebook Contains a Inverted Index Implementation with options to query Boolean, Phrasal, Proximity Queries.\n\n- Check Notebook : IR-A1-K173654.ipynb\n\n### Desktop System:\nA GUI Based Executable is present that provides a Desktop Interface to the Django based web server which holds the implementation of Inverted Index hosted on Heroku. It can evaluate and return results for queries.\n\n- Check Zip File For Desktop GUI Code : IR-SYS-A1\n - Repo Link: [Click Here](https://github.com/hassan11196/IR-SYS-A1)\n- Check Folder For Desktop GUI Executable : IR-APP-A1\n - Exe File: [Click Here](http://www.mediafire.com/file/u3zqzu1lvfpeedw/IR-SYS-A1-K173654_Setup_1.0.0.exe/file)\n- Check Folder For Python Django Web Server : IR-SERVER-A1\n - Repo Link: [Click Here](https://github.com/hassan11196/IR-A1/tree/master/DjangoApp/IRA1)\n\n### Web System:\nA Web Application is hosted at [https://ira1.netlify.com/#/](https://ira1.netlify.com/#/) .\n\n- Check Zip File for Web Interface Code : IR-WEB-A1\n - Repo Link : [Click Here](https://github.com/hassan11196/IR-WEB-A1)\n### ScreenShots:\n\n![](Screenshots/Annotation 2020-03-10 205835.png)\n![](Screenshots/Annotation 2020-03-10 205849.png)\n![](Screenshots/Annotation 2020-03-10 205854.png)\n\n### Built Using:\n\n
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\n\n### Template:\n- [electron react boilerplate](https://github.com/electron-react-boilerplate/electron-react-boilerplate)", + "readme_title": "IR-Assignment # 1", + "releases_count": 0, + "repo": "IR-SYS-A1", + "repo_name": "IR-SYS-A1", + "stars": 0, + "topics": [], + "updated_at": "2020-06-16T19:36:42Z", + "url": "https://github.com/hassan11196/IR-SYS-A1", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/IR-SYS-A1" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/IR-SYS-A3" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 13, + "contributors_count": 3, + "contributors_top": [ + { + "contributions": 11, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + }, + { + "contributions": 1, + "html_url": "https://github.com/EishaMazhar", + "login": "EishaMazhar" + }, + { + "contributions": 1, + "html_url": "https://github.com/apps/dependabot", + "login": "dependabot[bot]" + } + ], + "created_at": "2020-05-20T23:21:52Z", + "default_branch": "master", + "description": "A Electron React-Redux based Front-end Desktop Application For Information Retrieval - Assignment 3 - CS317 - FAST NUCES (Karachi)", + "dirs_total_count": 34, + "files_root_entries": [ + { + "path": ".dockerignore", + "size": 762, + "type": "file" + }, + { + "path": ".editorconfig", + "size": 188, + "type": "file" + }, + { + "path": ".eslintignore", + "size": 838, + "type": "file" + }, + { + "path": ".eslintrc.js", + "size": 461, + "type": "file" + }, + { + "path": ".gitattributes", + "size": 81, + "type": "file" + }, + { + "path": ".gitignore", + "size": 783, + "type": "file" + }, + { + "path": ".prettierrc.json", + "size": 185, + "type": "file" + }, + { + "path": ".stylelintrc", + "size": 76, + "type": "file" + }, + { + "path": ".travis.yml", + "size": 1665, + "type": "file" + }, + { + "path": ".vscode", + "size": 0, + "type": "dir" + }, + { + "path": "CHANGELOG.md", + "size": 15300, + "type": "file" + }, + { + "path": "CODE_OF_CONDUCT.md", + "size": 3366, + "type": "file" + }, + { + "path": "CONTRIBUTING.md", + "size": 4218, + "type": "file" + }, + { + "path": "LICENSE", + "size": 1102, + "type": "file" + }, + { + "path": "README.md", + "size": 3264, + "type": "file" + }, + { + "path": "Screenshots", + "size": 0, + "type": "dir" + }, + { + "path": "app", + "size": 0, + "type": "dir" + }, + { + "path": "appveyor.yml", + "size": 493, + "type": "file" + }, + { + "path": "azure-pipelines.yml", + "size": 1306, + "type": "file" + }, + { + "path": "babel.config.js", + "size": 2146, + "type": "file" + }, + { + "path": "configs", + "size": 0, + "type": "dir" + }, + { + "path": "internals", + "size": 0, + "type": "dir" + }, + { + "path": "package.json", + "size": 11586, + "type": "file" + }, + { + "path": "renovate.json", + "size": 27, + "type": "file" + }, + { + "path": "resources", + "size": 0, + "type": "dir" + }, + { + "path": "test", + "size": 0, + "type": "dir" + }, + { + "path": "tsconfig.json", + "size": 889, + "type": "file" + }, + { + "path": "yarn.lock", + "size": 681025, + "type": "file" + } + ], + "files_total_count": 204, + "first_commit_date_default_branch": "2020-03-08T18:08:24Z", + "forks": 1, + "has_ci": true, + "has_contributing": true, + "has_docker": true, + "has_docs": false, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": true, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/IR-SYS-A3", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 1, + "issues_open": 0, + "issues_total": 1, + "languages_top": [ + { + "language": "TypeScript", + "size": 102899 + }, + { + "language": "JavaScript", + "size": 25470 + }, + { + "language": "CSS", + "size": 2552 + }, + { + "language": "HTML", + "size": 1491 + } + ], + "last_commit_date_default_branch": "2021-10-31T15:25:16Z", + "last_commit_oid_default_branch": "4b8dc6dcc67398c4e00a3fc457ad84d81373d018", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "MIT License", + "license_spdx": "MIT", + "name_with_owner": "hassan11196/IR-SYS-A3", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "TypeScript", + "project_foreign_key": null, + "pull_requests_closed": 5, + "pull_requests_merged": 2, + "pull_requests_open": 3, + "pull_requests_total": 10, + "pushed_at": "2022-11-10T17:29:05Z", + "readme_length": 3261, + "readme_text": "# IR-Assignment # 2 \n## Implementation:\n- Classify and Cluster Given Documents of BBC Sport using a Knn and K-means. \n\n## Files:\n### Jupyter Notebook:\nThe Notebook Contains a KNN and K-means Implementation.\n\n- Check Notebook : IR-A3-K173654.ipynb\n\n#### KNN Algorithm \n**Approach:** Supervised\n\nA k-nearest-neighbor is a data classification algorithm that attempts to determine what group a data point is in by looking at the data points around it.\n\nAn algorithm, looking at one point on a grid, trying to determine if a point is in group A or B, looks at the states of the points that are near it. The range is arbitrarily determined, but the point is to take a sample of the data. If the majority of the points are in group A, then it is likely that the data point in question will be A rather than B, and vice versa.\n\n#### K-Mean Algorithm\n**Approach:** Unsupervised\nK-means clustering is a method used for clustering analysis, especially in data mining and statistics. It aims to partition a set of observations into a number of clusters (k), resulting in the partitioning of the data into Voronoi cells. It can be considered a method of finding out which group a certain object really belongs to.\n\n\n### Desktop System:\nA GUI Based Executable is present that provides a Desktop Interface to the Django based web server which holds the implementation of KNN and Kmeans hosted on Heroku. It can classify and cluster documents and return results.\n\n- Check Zip File For Desktop GUI Code : IR-SYS-A3\n - Repo Link: [Click Here](https://github.com/hassan11196/IR-SYS-A3)\n- Check Folder For Desktop GUI Executable : IR-APP-A3\n\n- Check Folder For Python Django Web Server : IR-SERVER-A3\n - Repo Link: [Click Here](https://github.com/hassan11196/IR-A3/tree/master/IR-SERVER-A3/IRA3)\n\n\n### Web System:\nA Web Application is hosted at [https://ira3.netlify.com/#/](https://ira3.netlify.com/#/) .\n\n- Check Folder for Web Interface Code : IR-WEB-A3 \n - Repo Link : [Click Here](https://github.com/hassan11196/IR-WEB-A3)\n\n\n### ScreenShots:\n\n- ![](Screenshots/Annotation%202020-05-18%20080928.png)\n\n\n### References:\n[http://mlg.ucd.ie/datasets/bbc.html](http://mlg.ucd.ie/datasets/bbc.html)\n\n\n### Built Using:\n\n
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\n\n### Template:\n- [electron react boilerplate](https://github.com/electron-react-boilerplate/electron-react-boilerplate)", + "readme_title": "IR-Assignment # 2", + "releases_count": 0, + "repo": "IR-SYS-A3", + "repo_name": "IR-SYS-A3", + "stars": 0, + "topics": [ + "hacktoberfest" + ], + "updated_at": "2021-10-31T15:25:19Z", + "url": "https://github.com/hassan11196/IR-SYS-A3", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/IR-SYS-A3" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/IR-WEB-A1" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 11, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 11, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + } + ], + "created_at": "2020-03-08T20:30:30Z", + "default_branch": "master", + "description": "A React-Redux based Front-end Web Application For Information Retrieval - Assignment 1 - CS317 - FAST NUCES (Karachi)", + "dirs_total_count": 23, + "files_root_entries": [ + { + "path": ".eslintrc", + "size": 90, + "type": "file" + }, + { + "path": ".gitignore", + "size": 310, + "type": "file" + }, + { + "path": "README.md", + "size": 2670, + "type": "file" + }, + { + "path": "Screenshots", + "size": 0, + "type": "dir" + }, + { + "path": "internals", + "size": 0, + "type": "dir" + }, + { + "path": "netlify.toml", + "size": 130, + "type": "file" + }, + { + "path": "package.json", + "size": 1750, + "type": "file" + }, + { + "path": "public", + "size": 0, + "type": "dir" + }, + { + "path": "src", + "size": 0, + "type": "dir" + }, + { + "path": "tsconfig.json", + "size": 593, + "type": "file" + }, + { + "path": "yarn.lock", + "size": 481595, + "type": "file" + } + ], + "files_total_count": 185, + "first_commit_date_default_branch": "2020-03-08T19:07:14Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/IR-WEB-A1", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "TypeScript", + "size": 33664 + }, + { + "language": "JavaScript", + "size": 5970 + }, + { + "language": "HTML", + "size": 3269 + }, + { + "language": "CSS", + "size": 2800 + } + ], + "last_commit_date_default_branch": "2020-06-16T19:20:04Z", + "last_commit_oid_default_branch": "e8ede50299e0d4491f1d1cb3654086bb07d3a5f1", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/IR-WEB-A1", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "TypeScript", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2020-06-16T19:20:37Z", + "readme_length": 2666, + "readme_text": "# IR-Assignment # 1 \n## Implementation:\n- Indexed Given Documents of Trump Speeches using a Inverted Index. \n- Designed a Query Engine using a Abstract Syntax Tree that can parse complex Boolean queries that have nested brackets.\n- Pickled the inverted index for quick retrieval for query purposes.\n## Files:\n### Jupyter Notebook:\nThe Notebook Contains a Inverted Index Implementation with options to query Boolean, Phrasal, Proximity Queries.\n\n- Check Notebook : IR-A1-K173654.ipynb\n\n### Desktop System:\nA GUI Based Executable is present that provides a Desktop Interface to the Django based web server which holds the implementation of Inverted Index hosted on Heroku. It can evaluate and return results for queries.\n\n- Check Zip File For Desktop GUI Code : IR-SYS-A1\n - Repo Link: [Click Here](https://github.com/hassan11196/IR-SYS-A1)\n- Check Folder For Desktop GUI Executable : IR-APP-A1\n - Exe File: [Click Here](http://www.mediafire.com/file/u3zqzu1lvfpeedw/IR-SYS-A1-K173654_Setup_1.0.0.exe/file)\n- Check Folder For Python Django Web Server : IR-SERVER-A1\n - Repo Link: [Click Here](https://github.com/hassan11196/IR-A1/tree/master/DjangoApp/IRA1)\n\n### Web System:\nA Web Application is hosted at [https://ira1.netlify.com/#/](https://ira1.netlify.com/#/) .\n\n- Check Zip File for Web Interface Code : IR-WEB-A1\n - Repo Link : [Click Here](https://github.com/hassan11196/IR-WEB-A1)\n### ScreenShots:\n\n![](Screenshots/Annotation 2020-03-10 205835.png)\n![](Screenshots/Annotation 2020-03-10 205849.png)\n![](Screenshots/Annotation 2020-03-10 205854.png)\n\n### Built Using:\n\n
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\n\n### Template:\n- [electron react boilerplate](https://github.com/electron-react-boilerplate/electron-react-boilerplate)", + "readme_title": "IR-Assignment # 1", + "releases_count": 0, + "repo": "IR-WEB-A1", + "repo_name": "IR-WEB-A1", + "stars": 0, + "topics": [], + "updated_at": "2020-06-16T19:36:28Z", + "url": "https://github.com/hassan11196/IR-WEB-A1", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/IR-WEB-A1" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/IR-WEB-A2" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 12, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 12, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + } + ], + "created_at": "2020-04-09T20:41:32Z", + "default_branch": "master", + "description": "A React-Redux based Front-end Web Application For Information Retrieval - Assignment 2 - CS317 - FAST NUCES (Karachi)", + "dirs_total_count": 25, + "files_root_entries": [ + { + "path": ".eslintrc", + "size": 90, + "type": "file" + }, + { + "path": ".gitignore", + "size": 310, + "type": "file" + }, + { + "path": "README.md", + "size": 2468, + "type": "file" + }, + { + "path": "Screenshots", + "size": 0, + "type": "dir" + }, + { + "path": "internals", + "size": 0, + "type": "dir" + }, + { + "path": "netlify.toml", + "size": 130, + "type": "file" + }, + { + "path": "package.json", + "size": 1781, + "type": "file" + }, + { + "path": "public", + "size": 0, + "type": "dir" + }, + { + "path": "src", + "size": 0, + "type": "dir" + }, + { + "path": "tsconfig.json", + "size": 593, + "type": "file" + }, + { + "path": "yarn.lock", + "size": 489072, + "type": "file" + } + ], + "files_total_count": 190, + "first_commit_date_default_branch": "2020-03-08T19:07:14Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/IR-WEB-A2", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "TypeScript", + "size": 47176 + }, + { + "language": "JavaScript", + "size": 5798 + }, + { + "language": "HTML", + "size": 3269 + }, + { + "language": "CSS", + "size": 2957 + } + ], + "last_commit_date_default_branch": "2020-12-27T03:33:05Z", + "last_commit_oid_default_branch": "3ef711737c3d541ec6043cf6d6383c83b043ef77", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/IR-WEB-A2", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "TypeScript", + "project_foreign_key": null, + "pull_requests_closed": 4, + "pull_requests_merged": 0, + "pull_requests_open": 20, + "pull_requests_total": 24, + "pushed_at": "2023-01-05T13:13:46Z", + "readme_length": 2466, + "readme_text": "# IR-Assignment # 2 \n## Implementation:\n- Indexed Given Documents of Trump Speeches using a Vector Space Model. \n- Pickled the inverted index for quick retrieval for query purposes.\n## Files:\n### Jupyter Notebook:\nThe Notebook Contains a Vector Space Model Implementation.\n\n- Check Notebook : IR-A2-K173654.ipynb\n\n### Desktop System:\nA GUI Based Executable is present that provides a Desktop Interface to the Django based web server which holds the implementation of Inverted Index hosted on Heroku. It can evaluate and return results for queries.\n\n- Check Zip File For Desktop GUI Code : IR-SYS-A2\n - Repo Link: [Click Here](https://github.com/hassan11196/IR-SYS-A2)\n- Check Folder For Desktop GUI Executable : IR-APP-A2\n - Exe File: [Click Here](https://www.mediafire.com/file/asb4p9y0i00iy7n/IR-SYS-A2-K173654_Setup_1.0.0.exe/file)\n- Check Folder For Python Django Web Server : IR-SERVER-A2\n - Repo Link: [Click Here](https://github.com/hassan11196/IR-A2/tree/master/IR-SERVER-A2/IRA2)\n\n### Web System:\nA Web Application is hosted at [https://ira2.netlify.com/#/](https://ira2.netlify.com/#/) .\n\n- Check Zip File for Web Interface Code : IR-WEB-A2\n - Repo Link : [Click Here](https://github.com/hassan11196/IR-WEB-A2)\n \n### ScreenShots:\n- ![](Screenshots/Annotation%202020-04-10%20234906.png)\n- ![](Screenshots/ss1.png)\n- ![](Screenshots/ss2.png)\n- ![](Screenshots/ss3.png)\n\n\n\n### Built Using:\n\n
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\n\n### Template:\n- [electron react boilerplate](https://github.com/electron-react-boilerplate/electron-react-boilerplate)", + "readme_title": "IR-Assignment # 2", + "releases_count": 0, + "repo": "IR-WEB-A2", + "repo_name": "IR-WEB-A2", + "stars": 0, + "topics": [ + "react", + "redux", + "information-retrieval", + "jupyter-notebook", + "hacktoberfest" + ], + "updated_at": "2023-10-22T15:22:04Z", + "url": "https://github.com/hassan11196/IR-WEB-A2", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/IR-WEB-A2" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/IR-WEB-A3" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 19, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 19, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + } + ], + "created_at": "2020-05-17T19:46:40Z", + "default_branch": "master", + "description": "A React-Redux based Front-end Web Application For Information Retrieval - Assignment 3 - CS317 - FAST NUCES (Karachi)", + "dirs_total_count": 27, + "files_root_entries": [ + { + "path": ".eslintrc", + "size": 90, + "type": "file" + }, + { + "path": ".gitignore", + "size": 310, + "type": "file" + }, + { + "path": "CODE_OF_CONDUCT.md", + "size": 5225, + "type": "file" + }, + { + "path": "CONTRIBUTING.md", + "size": 3831, + "type": "file" + }, + { + "path": "README.md", + "size": 2309, + "type": "file" + }, + { + "path": "Screenshots", + "size": 0, + "type": "dir" + }, + { + "path": "internals", + "size": 0, + "type": "dir" + }, + { + "path": "netlify.toml", + "size": 130, + "type": "file" + }, + { + "path": "package.json", + "size": 1781, + "type": "file" + }, + { + "path": "public", + "size": 0, + "type": "dir" + }, + { + "path": "src", + "size": 0, + "type": "dir" + }, + { + "path": "tsconfig.json", + "size": 593, + "type": "file" + }, + { + "path": "yarn.lock", + "size": 489072, + "type": "file" + } + ], + "files_total_count": 199, + "first_commit_date_default_branch": "2020-03-08T19:07:14Z", + "forks": 0, + "has_ci": false, + "has_contributing": true, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/IR-WEB-A3", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "TypeScript", + "size": 77800 + }, + { + "language": "JavaScript", + "size": 5886 + }, + { + "language": "CSS", + "size": 3271 + }, + { + "language": "HTML", + "size": 3269 + } + ], + "last_commit_date_default_branch": "2021-10-31T09:43:06Z", + "last_commit_oid_default_branch": "95af94749fba0f59e2d668d5cf8c344635ae80f2", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/IR-WEB-A3", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "TypeScript", + "project_foreign_key": null, + "pull_requests_closed": 4, + "pull_requests_merged": 1, + "pull_requests_open": 22, + "pull_requests_total": 27, + "pushed_at": "2023-01-05T12:19:43Z", + "readme_length": 2306, + "readme_text": "# IR-Assignment # 2 \n## Implementation:\n- Classify and Cluster Given Documents of BBC Sport using a Knn and K-means. \n\n## Files:\n### Jupyter Notebook:\nThe Notebook Contains a KNN and K-means Implementation.\n\n- Check Notebook : IR-A3-K173654.ipynb\n\n### Desktop System:\nA GUI Based Executable is present that provides a Desktop Interface to the Django based web server which holds the implementation of KNN and Kmeans hosted on Heroku. It can classify and cluster documents and return results.\n\n- Check Zip File For Desktop GUI Code : IR-SYS-A3\n - Repo Link: [Click Here](https://github.com/hassan11196/IR-SYS-A3)\n- Check Folder For Desktop GUI Executable : IR-APP-A3\n\n- Check Folder For Python Django Web Server : IR-SERVER-A3\n - Repo Link: [Click Here](https://github.com/hassan11196/IR-A3/tree/master/IR-SERVER-A3/IRA3)\n\n\n### Web System:\nA Web Application is hosted at [https://ira3.netlify.com/#/](https://ira3.netlify.com/#/) .\n\n- Check Folder for Web Interface Code : IR-WEB-A3 \n - Repo Link : [Click Here](https://github.com/hassan11196/IR-WEB-A3)\n\n\n### ScreenShots:\n\n- ![](Screenshots/Annotation%202020-05-18%20080928.png)\n\n\n### References:\n[http://mlg.ucd.ie/datasets/bbc.html](http://mlg.ucd.ie/datasets/bbc.html)\n\n\n### Built Using:\n\n
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\n\n### Template:\n- [electron react boilerplate](https://github.com/electron-react-boilerplate/electron-react-boilerplate)", + "readme_title": "IR-Assignment # 2", + "releases_count": 0, + "repo": "IR-WEB-A3", + "repo_name": "IR-WEB-A3", + "stars": 0, + "topics": [ + "hacktoberfest" + ], + "updated_at": "2021-10-31T09:43:09Z", + "url": "https://github.com/hassan11196/IR-WEB-A3", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/IR-WEB-A3" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/IR-whats-cooking" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 22, + "contributors_count": 2, + "contributors_top": [ + { + "contributions": 16, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + }, + { + "contributions": 6, + "html_url": "https://github.com/NomanAnjum09", + "login": "NomanAnjum09" + } + ], + "created_at": "2020-05-05T16:53:46Z", + "default_branch": "master", + "description": "Semester Project - Information Retrieval and Artificial Intelligence - CS317 & CS401 - FAST NUCES (Karachi)", + "dirs_total_count": 26, + "files_root_entries": [ + { + "path": ".gitignore", + "size": 76, + "type": "file" + }, + { + "path": ".ipynb_checkpoints", + "size": 0, + "type": "dir" + }, + { + "path": ".vscode", + "size": 0, + "type": "dir" + }, + { + "path": "Classifier.ipynb", + "size": 12992, + "type": "file" + }, + { + "path": "DV", + "size": 6738016, + "type": "file" + }, + { + "path": "Data", + "size": 0, + "type": "dir" + }, + { + "path": "IR-whats-cooking-WEB", + "size": 0, + "type": "dir" + }, + { + "path": "IR-whats-cooking-proto-2.ipynb", + "size": 2251633, + "type": "file" + }, + { + "path": "IR-whats-cooking-server", + "size": 0, + "type": "dir" + }, + { + "path": "LogisticRegression.csv", + "size": 141531, + "type": "file" + }, + { + "path": "Pipfile", + "size": 383, + "type": "file" + }, + { + "path": "Pipfile.lock", + "size": 51528, + "type": "file" + }, + { + "path": "Procfile", + "size": 84, + "type": "file" + }, + { + "path": "README.md", + "size": 3289, + "type": "file" + }, + { + "path": "Stopword-List.txt", + "size": 1780, + "type": "file" + }, + { + "path": "VectorSpaceX", + "size": 6568800, + "type": "file" + }, + { + "path": "Vector_Machine.py", + "size": 13518, + "type": "file" + }, + { + "path": "Whats Cooking.pdf", + "size": 114046, + "type": "file" + }, + { + "path": "internals", + "size": 0, + "type": "dir" + }, + { + "path": "screenshots", + "size": 0, + "type": "dir" + }, + { + "path": "sparse_matrix.npz", + "size": 6683071, + "type": "file" + }, + { + "path": "whats cooking.ipynb", + "size": 38466, + "type": "file" + }, + { + "path": "whats-cooking-out.csv", + "size": 132808, + "type": "file" + } + ], + "files_total_count": 132, + "first_commit_date_default_branch": "2020-05-05T16:55:33Z", + "forks": 1, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": true, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/IR-whats-cooking", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Jupyter Notebook", + "size": 2601228 + }, + { + "language": "Python", + "size": 111730 + }, + { + "language": "JavaScript", + "size": 4728 + } + ], + "last_commit_date_default_branch": "2020-07-23T03:44:29Z", + "last_commit_oid_default_branch": "ada78d9843d4c910b94259f0776851fbd5ba4ea6", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/IR-whats-cooking", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "Jupyter Notebook", + "project_foreign_key": null, + "pull_requests_closed": 6, + "pull_requests_merged": 0, + "pull_requests_open": 10, + "pull_requests_total": 16, + "pushed_at": "2022-09-30T20:13:40Z", + "readme_length": 3272, + "readme_text": "# What’s Cooking? Predicting Cuisines from Recipe Ingredients\n\nKaggle is an online platform for hosting data science competitions. On September\n9, Kaggle opened a contest called “What’s Cooking?”. The goal of this contest\nis to predict the type of a cuisine (e.g. “Chinese” or “Mexican”) from the list of\ningredients in a recipe.\nIn this [paper](Whats%20Cooking.pdf), Kevin K. Do and his team describes their methodology for training an\nalgorithm to make such predictions, We implemented the paper in a Jupyter Notebook and Developed a Web Application that can predict cuisine types given the ingredients list.\n\n## Files:\n\n### Jupyter Notebook:\n\nThe Notebook Contains a Implementation of the [paper](./Whats%20Cooking.pdf).\n\n- Check Notebook : [IR-A3-K173654.ipynb](./IR-whats-cooking-proto-2.ipynb)\n\n### Web System:\n\nA Web Application is hosted at [https://whats-cooking-ir.netlify.app/](https://whats-cooking-ir.netlify.app/) .\n\n- Check Folder for Web Interface Code : IR-WEB-A3\n - Repo Link : [Click Here](https://github.com/hassan11196/IR-whats-cooking-WEB)\n\n### ScreenShots:\n\n- ![](screenshots/Annotation%202020-07-23%20082642.png)\n- ![](screenshots/Annotation%202020-07-23%20082556.png)\n\n### Team:\n\n| [![Noman Anjum](https://github.com/NomanAnjum09.png?size=100)](https://github.com/NomanAnjum09) | [![Hassan Ahmed](https://github.com/hassan11196.png?size=100)](https://github.com/hassan11196) | [![Ahsan Ali](https://github.com/MuhammadAichee.png?size=100)](https://github.com/MuhammadAichee) |\n| :---------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------------------------: |\n| [Noman Anjum](https://github.com/NomanAnjum09) | [Hassan Ahmed](https://github.com/hassan11196) | [Ahsan Ali](https://github.com/MuhammadAichee) |\n\n### References:\n\n[https://www.kaggle.com/c/whats-cooking/](https://www.kaggle.com/c/whats-cooking/)\n\n### Built Using:\n\n
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\n\n### Template:\n\n- [electron react boilerplate](https://github.com/electron-react-boilerplate/electron-react-boilerplate)", + "readme_title": "What’s Cooking? Predicting Cuisines from Recipe Ingredients", + "releases_count": 0, + "repo": "IR-whats-cooking", + "repo_name": "IR-whats-cooking", + "stars": 0, + "topics": [], + "updated_at": "2020-07-23T03:44:39Z", + "url": "https://github.com/hassan11196/IR-whats-cooking", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/IR-whats-cooking" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/IR-whats-cooking-WEB" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 13, + "contributors_count": 3, + "contributors_top": [ + { + "contributions": 6, + "html_url": "https://github.com/MuhammadAichee", + "login": "MuhammadAichee" + }, + { + "contributions": 6, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + }, + { + "contributions": 1, + "html_url": "https://github.com/apps/dependabot", + "login": "dependabot[bot]" + } + ], + "created_at": "2020-06-05T04:32:58Z", + "default_branch": "master", + "description": "Semester Project - Frontend for Information Retrieval and Artificial Intelligence - CS317 & CS401 - FAST NUCES (Karachi)", + "dirs_total_count": 20, + "files_root_entries": [ + { + "path": ".eslintrc", + "size": 90, + "type": "file" + }, + { + "path": ".gitignore", + "size": 310, + "type": "file" + }, + { + "path": "README.md", + "size": 4206, + "type": "file" + }, + { + "path": "netlify.toml", + "size": 130, + "type": "file" + }, + { + "path": "package-lock.json", + "size": 643439, + "type": "file" + }, + { + "path": "package.json", + "size": 2151, + "type": "file" + }, + { + "path": "public", + "size": 0, + "type": "dir" + }, + { + "path": "src", + "size": 0, + "type": "dir" + }, + { + "path": "tsconfig.json", + "size": 593, + "type": "file" + }, + { + "path": "yarn.lock", + "size": 519096, + "type": "file" + } + ], + "files_total_count": 106, + "first_commit_date_default_branch": "2020-06-06T18:42:44Z", + "forks": 1, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/IR-whats-cooking-WEB", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "TypeScript", + "size": 102144 + }, + { + "language": "CSS", + "size": 3644 + }, + { + "language": "HTML", + "size": 3290 + }, + { + "language": "JavaScript", + "size": 1158 + } + ], + "last_commit_date_default_branch": "2020-09-13T15:17:54Z", + "last_commit_oid_default_branch": "86415869391cbfbe75e090747f03114076c3ac8a", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/IR-whats-cooking-WEB", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "TypeScript", + "project_foreign_key": null, + "pull_requests_closed": 4, + "pull_requests_merged": 1, + "pull_requests_open": 19, + "pull_requests_total": 24, + "pushed_at": "2022-12-13T08:56:13Z", + "readme_length": 4189, + "readme_text": "# What’s Cooking? Predicting Cuisines from Recipe Ingredients\n\nKaggle is an online platform for hosting data science competitions. On September\n9, Kaggle opened a contest called “What’s Cooking?”. The goal of this contest\nis to predict the type of a cuisine (e.g. “Chinese” or “Mexican”) from the list of\ningredients in a recipe.\nIn this [paper](https://github.com/hassan11196/IR-whats-cooking/blob/master/Whats%20Cooking.pdf), Kevin K. Do and his team describes their methodology for training an\nalgorithm to make such predictions, We implemented the paper in a Jupyter Notebook and Developed a Web Application that can predict cuisine types given the ingredients list.\n\n## Files:\n\n### Jupyter Notebook:\n\nThe Notebook Contains a Implementation of the [paper](https://github.com/hassan11196/IR-whats-cooking/blob/master/Whats%20Cooking.pdf).\n\n- Check Notebook : [IR-A3-K173654.ipynb](https://github.com/hassan11196/IR-whats-cooking/blob/master/IR-whats-cooking-proto-2.ipynb)\n\n### Web System:\n\nA Web Application is hosted at [https://whats-cooking-ir.netlify.app/](https://whats-cooking-ir.netlify.app/) .\n\n- Check Folder for Web Interface Code : IR-WEB-A3\n - Repo Link : [Click Here](https://github.com/hassan11196/IR-whats-cooking-WEB)\n\n### ScreenShots:\n\n- ![](https://github.com/hassan11196/IR-whats-cooking/blob/master/screenshots/Annotation%202020-07-23%20082642.png?raw=true)\n- ![](https://github.com/hassan11196/IR-whats-cooking/blob/master/screenshots/Annotation%202020-07-23%20082556.png?raw=true)\n\n### Team:\n\n| [![Noman Anjum](https://github.com/NomanAnjum09.png?size=100)](https://github.com/NomanAnjum09) | [![Hassan Ahmed](https://github.com/hassan11196.png?size=100)](https://github.com/hassan11196) | [![Ahsan Ali](https://github.com/MuhammadAichee.png?size=100)](https://github.com/MuhammadAichee) |\n| :---------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------------------------: |\n| [Noman Anjum](https://github.com/NomanAnjum09) | [Hassan Ahmed](https://github.com/hassan11196) | [Ahsan Ali](https://github.com/MuhammadAichee) |\n\n### References:\n\n[https://www.kaggle.com/c/whats-cooking/](https://www.kaggle.com/c/whats-cooking/)\n\n### Built Using:\n\n
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\n\n### Template:\n\n- [electron react boilerplate](https://github.com/electron-react-boilerplate/electron-react-boilerplate)", + "readme_title": "What’s Cooking? 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Create the virtual environment.\n```\nvirtualenv /path/to/venv --python=/path/to/python3\n```\nYou can find out the path to your `python3` interpreter with the command `which python3`.\n\n2. Activate the environment and install dependencies.\n```\nsource /path/to/venv/bin/activate\npip install -r requirements.txt\n```\n\n3. Launch the service\n```\nuvicorn api.main:app\n```\n\n## Posting requests locally\nWhen the service is running, try\n```\n127.0.0.1/docs\n```\nor \n```\ncurl\n```\n\n## Deployment with Docker\n1. Build the Docker image\n```\ndocker build --file Dockerfile --tag fastapi-ml-quickstart .\n```\n\n2. Running the Docker image\n```\ndocker run -p 8000:8000 fastapi-ml-quickstart\n```\n\n3. Entering into the Docker image\n```\ndocker run -it --entrypoint /bin/bash fastapi-ml-quickstart\n```\n\n## docker-compose\n1. Launching the service\n```\ndocker-compose up\n```\nThis command looks for the `docker-compose.yaml` configuration file. If you want to use another configuration file,\nit can be specified with the `-f` switch. For example \n\n2. Testing\n```\ndocker-compose -f docker-compose.test.yaml up --abort-on-container-exit --exit-code-from fastapi-ml-quickstart\n```\n\nReference: \nhttps://towardsdatascience.com/how-to-properly-ship-and-deploy-your-machine-learning-model-8a8664b763c4", + "readme_title": "How to properly ship and deploy your machine learning model", + "releases_count": 0, + "repo": "ML-model-ship-and-deploy", + "repo_name": "ML-model-ship-and-deploy", + "stars": 9, + "topics": [ + "python", + "fastapi", + "ml", + "machine-learning", + "docker", + "uvicorn", + "docker-image", + "hacktoberfest" + ], + "updated_at": "2025-07-20T15:52:26Z", + "url": "https://github.com/hassan11196/ML-model-ship-and-deploy", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/ML-model-ship-and-deploy" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/MachineLearningExercises" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 24, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 24, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + } + ], + "created_at": "2020-01-18T15:53:25Z", + "default_branch": "master", + "description": "Machine Learning by Stanford University. Taught by: Andrew Ng", + "dirs_total_count": 35, + "files_root_entries": [ + { + "path": ".gitignore", + "size": 16, + "type": "file" + }, + { + "path": ".vscode", + "size": 0, + "type": "dir" + }, + { + "path": "Lectures", + "size": 0, + "type": "dir" + }, + { + "path": "README.md", + "size": 4393, + "type": "file" + }, + { + "path": "_config.yml", + "size": 25, + "type": "file" + }, + { + "path": "machine-learning-ex1", + "size": 0, + "type": "dir" + }, + { + "path": "machine-learning-ex2", + "size": 0, + "type": "dir" + }, + { + "path": "machine-learning-ex3", + "size": 0, + "type": "dir" + }, + { + "path": "machine-learning-ex4", + "size": 0, + "type": "dir" + }, + { + "path": "machine-learning-ex5", + "size": 0, + "type": "dir" + }, + { + "path": "machine-learning-ex6", + "size": 0, + "type": "dir" + }, + { + "path": "machine-learning-ex7", + "size": 0, + "type": "dir" + }, + { + "path": "machine-learning-ex8", + "size": 0, + "type": "dir" + } + ], + "files_total_count": 262, + "first_commit_date_default_branch": "2020-01-18T15:51:37Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": "https://hassan11196.github.io/MachineLearningExercises/", + "input_url": "https://github.com/hassan11196/MachineLearningExercises", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "MATLAB", + "size": 833763 + } + ], + "last_commit_date_default_branch": "2020-07-02T05:04:12Z", + "last_commit_oid_default_branch": "c307e0482203beaadcd2e52a836c7270db843a28", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/MachineLearningExercises", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "MATLAB", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2020-07-02T05:04:12Z", + "readme_length": 4392, + "readme_text": "# Machine Learning Exercises\n## Coursera Course : Machine Learning by Stanford University\n\n- [Exercise 1 - Linear Regression](https://github.com/hassan11196/MachineLearningExercises/tree/master/machine-learning-ex1)\n - [Linear Regression Cost Function](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex1/ex1/computeCost.m) \n - [Gradient Descent](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex1/ex1/gradientDescent.m)\n - [Normal Equations](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex1/ex1/normalEqn.m)\n\n- [Exercise 2 - Logistic Regression](https://github.com/hassan11196/MachineLearningExercises/tree/master/machine-learning-ex2)\n - [Logistic Regression Cost Function](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex2/ex2/costFunction.m)\n - [Logistic Regression Regularized Cost Function](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex2/ex2/costFunctionReg.m)\n \n- [Exercise 3 - Multi-class Classification and Neural Networks](https://github.com/hassan11196/MachineLearningExercises/tree/master/machine-learning-ex3)\n - [Multi-class Classification](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex3/ex3/ex3.m)\n - [Vectorizing Logistic Regression](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex3/ex3/lrCostFunction.m)\n - [One-vs-all Classification](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex3/ex3/oneVsAll.m)\n - [Neural Network Prediction Function](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex3/ex3/ex3_nn.m)\n \n- [Exercise 4 - Neural Networks Learning](https://github.com/hassan11196/MachineLearningExercises/tree/master/machine-learning-ex4)\n - [NN Feedforward and Cost Function](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex4/ex4/nnCostFunction.m)\n - [Sigmoid Gradient](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex4/ex4/sigmoidGradient.m)\n - [Backpropagation](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex4/ex4/nnCostFunction.m)\n \n- [Exercise 5 - Regularized Linear Regression and Bias vs Variance](https://github.com/hassan11196/MachineLearningExercises/tree/master/machine-learning-ex5)\n - [Regularized Linear Regression](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex5/ex5/linearRegCostFunction.m)\n - [Polynomial Regression](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex5/ex5/polyFeatures.m)\n \n- [Exercise 6 - Support Vector Machines](https://github.com/hassan11196/MachineLearningExercises/tree/master/machine-learning-ex6)\n - [SVM with Gaussian Kernels](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex6/ex6/svmTrain.m)\n - [Spam Email Classification](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex6/ex6/processEmail.m)\n \n- [Exercise 7 - K-means Clustering and Principal Component Analysis](https://github.com/hassan11196/MachineLearningExercises/tree/master/machine-learning-ex7)\n - [K-means Clustering](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex7/ex7/computeCentroids.m)\n - [Dimensionality Reduction with PCA](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex7/ex7/pca.m)\n \n- [Exercise 8 - Anomaly Detection and Recommender Systems](https://github.com/hassan11196/MachineLearningExercises/tree/master/machine-learning-ex8)\n - [Anomaly detection](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex8/ex8/selectThreshold.m)\n - [Collaborative filltering learning algorithm](https://github.com/hassan11196/MachineLearningExercises/blob/master/machine-learning-ex8/ex8/cofiCostFunc.m)\n\n\n\n### Help Material : \n[what makes the gaussian kernel so magical for pca](https://stats.stackexchange.com/questions/131138/what-makes-the-gaussian-kernel-so-magical-for-pca-and-also-in-general)\n\n[The Gaussian Kernel](https://mccormickml.com/2013/08/15/the-gaussian-kernel/)\n\nTaught by: Andrew Ng,", + "readme_title": "Machine Learning Exercises", + "releases_count": 0, + "repo": "MachineLearningExercises", + "repo_name": "MachineLearningExercises", + "stars": 0, + "topics": [ + "ml", + "machine-learning", + "andrew-ng-machine-learning", + "andrew-ng", + "coursera-machine-learning", + "octave", + "matlab" + ], + "updated_at": "2020-07-02T05:04:46Z", + "url": "https://github.com/hassan11196/MachineLearningExercises", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/MachineLearningExercises" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/MoViNet-pytorch" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 2, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 2, + "html_url": null, + "login": "hassan" + } + ], + "created_at": "2022-03-14T11:51:32Z", + "default_branch": "main", + "description": null, + "dirs_total_count": 3, + "files_root_entries": [ + { + "path": "LICENSE", + "size": 1063, + "type": "file" + }, + { + "path": "README.md", + "size": 6057, + "type": "file" + }, + { + "path": "figures", + "size": 0, + "type": "dir" + }, + { + "path": "movinet_tutorial.ipynb", + "size": 70794, + "type": "file" + }, + { + "path": "movinets", + "size": 0, + "type": "dir" + }, + { + "path": "setup.py", + "size": 631, + "type": "file" + }, + { + "path": "tests", + "size": 0, + "type": "dir" + } + ], + "files_total_count": 11, + "first_commit_date_default_branch": "2022-03-14T11:49:27Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": true, + "has_notebooks": true, + "has_readme_file": true, + "has_tests": true, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/MoViNet-pytorch", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Jupyter Notebook", + "size": 70794 + }, + { + "language": "Python", + "size": 62922 + } + ], + "last_commit_date_default_branch": "2022-03-14T11:53:40Z", + "last_commit_oid_default_branch": "f2b0a14ba72223b2b883052987f30b8394479ba5", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "MIT License", + "license_spdx": "MIT", + "name_with_owner": "hassan11196/MoViNet-pytorch", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "Jupyter Notebook", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2022-03-14T11:53:45Z", + "readme_length": 6055, + "readme_text": "# MoViNet-pytorch\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Atze00/MoViNet-pytorch/blob/main/movinet_tutorial.ipynb) [![Paper](http://img.shields.io/badge/Paper-arXiv.2103.11511-B3181B?logo=arXiv)](https://arxiv.org/abs/2103.11511)

\nPytorch unofficial implementation of [MoViNets: Mobile Video Networks for Efficient Video Recognition](https://arxiv.org/pdf/2103.11511.pdf).
\nAuthors: Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, Boqing Gong (Google Research)
\n[MoviNet-Pytorch Main repo](https://github.com/Atze00/MoViNet-pytorch)\nThis is a fork to reduce installation install time. \n[[Authors' Implementation]](https://github.com/tensorflow/models/tree/master/official/vision/beta/projects/movinet)
\n\n## Stream Buffer\n![stream buffer](https://github.com/Atze00/MoViNet-pytorch/blob/main/figures/Stream_buffer.png)\n\n#### Clean stream buffer\nIt is required to clean the buffer after all the clips of the same video have been processed.\n```python\nmodel.clean_activation_buffers()\n```\n## Usage\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Atze00/MoViNet-pytorch/blob/main/movinet_tutorial.ipynb)
\nClick on \"Open in Colab\" to open an example of training on HMDB-51
\n### installation \n```pip install git+https://github.com/hassan11196/MoViNet-pytorch.git```\n\n#### How to build a model\nUse ```causal = True``` to use the model with stream buffer, causal = False will use standard convolutions
\n```python\nfrom movinets import MoViNet\nfrom movinets.config import _C\n\nMoViNetA0 = MoViNet(_C.MODEL.MoViNetA0, causal = True, pretrained = True )\nMoViNetA1 = MoViNet(_C.MODEL.MoViNetA1, causal = True, pretrained = True )\n...\n```\n##### Load weights\nUse ```pretrained = True``` to use the model with pretrained weights
\n\n```python\n \"\"\"\n If pretrained is True:\n num_classes is set to 600,\n conv_type is set to \"3d\" if causal is False, \"2plus1d\" if causal is True\n tf_like is set to True\n \"\"\"\nmodel = MoViNet(_C.MODEL.MoViNetA0, causal = True, pretrained = True )\nmodel = MoViNet(_C.MODEL.MoViNetA0, causal = False, pretrained = True )\n```\n\n\n#### Training loop examples\nTraining loop with stream buffer\n```python\ndef train_iter(model, optimz, data_load, n_clips = 5, n_clip_frames=8):\n \"\"\"\n In causal mode with stream buffer a single video is fed to the network\n using subclips of lenght n_clip_frames. \n n_clips*n_clip_frames should be equal to the total number of frames presents\n in the video.\n \n n_clips : number of clips that are used\n n_clip_frames : number of frame contained in each clip\n \"\"\"\n \n #clean the buffer of activations\n model.clean_activation_buffers()\n optimz.zero_grad()\n for i, data, target in enumerate(data_load):\n #backward pass for each clip\n for j in range(n_clips):\n out = F.log_softmax(model(data[:,:,(n_clip_frames)*(j):(n_clip_frames)*(j+1)]), dim=1)\n loss = F.nll_loss(out, target)/n_clips\n loss.backward()\n optimz.step()\n optimz.zero_grad()\n \n #clean the buffer of activations\n model.clean_activation_buffers()\n```\nTraining loop with standard convolutions\n```python\ndef train_iter(model, optimz, data_load):\n\n optimz.zero_grad()\n for i, (data,_ , target) in enumerate(data_load):\n out = F.log_softmax(model(data), dim=1)\n loss = F.nll_loss(out, target)\n loss.backward()\n optimz.step()\n optimz.zero_grad()\n```\n\n## Pretrained models\n#### Weights\nThe weights are loaded from the tensorflow models released by the authors, trained on kinetics.\n\n#### Base Models\n\nBase models implement standard 3D convolutions without stream buffers.\n\n| Model Name | Top-1 Accuracy* | Top-5 Accuracy* | Input Shape |\n|------------|----------------|----------------|-------------|\n| MoViNet-A0-Base | 72.28 | 90.92 | 50 x 172 x 172 | \n| MoViNet-A1-Base | 76.69 | 93.40 | 50 x 172 x 172 | \n| MoViNet-A2-Base | 78.62 | 94.17 | 50 x 224 x 224 | \n| MoViNet-A3-Base | 81.79 | 95.67 | 120 x 256 x 256 | \n| MoViNet-A4-Base | 83.48 | 96.16 | 80 x 290 x 290 | \n| MoViNet-A5-Base | 84.27 | 96.39 | 120 x 320 x 320 | \n\n\n| Model Name | Top-1 Accuracy* | Top-5 Accuracy* | Input Shape\\*\\* |\n|------------|----------------|----------------|---------------|\n| MoViNet-A0-Stream | 72.05 | 90.63 | 50 x 172 x 172 | \n| MoViNet-A1-Stream | 76.45 | 93.25 | 50 x 172 x 172 |\n| MoViNet-A2-Stream | 78.40 | 94.05 | 50 x 224 x 224 |\n\n\n\\*\\*In streaming mode, the number of frames correspond to the total accumulated\nduration of the 10-second clip.\n\n*Accuracy reported on the official repository for the dataset kinetics 600, It has not been tested by me. It should be the same since the tf models and the reimplemented pytorch models output the same results [[Test]](https://github.com/Atze00/MoViNet-pytorch/blob/main/tests/test_pretrained_models.py).\n\nI currently haven't tested the speed of the streaming models, feel free to test and contribute.\n\n#### Status\nCurrently are available the pretrained models for the following architectures:\n- [x] MoViNetA1-BASE\n- [x] MoViNetA1-STREAM\n- [x] MoViNetA2-BASE\n- [x] MoViNetA2-STREAM\n- [x] MoViNetA3-BASE\n- [ ] MoViNetA3-STREAM\n- [x] MoViNetA4-BASE\n- [ ] MoViNetA4-STREAM\n- [x] MoViNetA5-BASE\n- [ ] MoViNetA5-STREAM\n\nI currently have no plans to include streaming version of A3,A4,A5. Those models are too slow for most mobile applications.\n\n### Testing\nI recommend to create a new environment for testing and run the following command to install all the required packages:
\n ```pip install -r tests/test_requirements.txt```\n \n### Citations\n```bibtex\n@article{kondratyuk2021movinets,\n title={MoViNets: Mobile Video Networks for Efficient Video Recognition},\n author={Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Matthew Brown, and Boqing Gong},\n journal={arXiv preprint arXiv:2103.11511},\n year={2021}\n}\n```", + "readme_title": "MoViNet-pytorch", + "releases_count": 0, + "repo": "MoViNet-pytorch", + "repo_name": "MoViNet-pytorch", + "stars": 0, + "topics": [], + "updated_at": "2022-03-14T11:52:00Z", + "url": "https://github.com/hassan11196/MoViNet-pytorch", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/MoViNet-pytorch" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/ORA" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 8, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 8, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + } + ], + "created_at": "2019-06-10T00:02:59Z", + "default_branch": "master", + "description": "Minimum Spanning Tree - 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MT303 - Minimum Spanning Tree\n\n#### Execute\n\n\n python application.py\n\n\n\n#### References :\n\n##### https://www.sanfoundry.com/python-program-find-minimum-spanning-tree-using-krusals-algorithm/\n##### https://visualgo.net/en/mst?slide=1", + "readme_title": "ORA", + "releases_count": 0, + "repo": "ORA", + "repo_name": "ORA", + "stars": 0, + "topics": [], + "updated_at": "2020-03-15T22:05:29Z", + "url": "https://github.com/hassan11196/ORA", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/ORA" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/OS-LAB" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 8, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 8, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + } + ], + "created_at": "2019-03-18T14:17:41Z", + "default_branch": "master", + "description": "OS Lab Tasks - 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Operating Systems(CS205) - Miss Tania Erum - Spring 2019 - FAST(NUCES)\n\nReference Links :\n
    \n
  • https://linuxconfig.org/check-your-gmail-inbox-for-new-emails-with-bash-script
    • \n
  • https://stackoverflow.com/questions/9590935/change-extension-of-file-using-shell-script
    • \n
  • http://programscode.blogspot.com/2005/08/unix-shell-script-for-sum-of-cubes.html
    • \n
  • https://unix.stackexchange.com/questions/413449/does-bash-provide-support-for-using-pointers
    • \n
  • https://tyson332.wordpress.com/2015/04/19/shell-script-to-check-given-string-is-palindrome-or-not/
    • \n
  • https://support.rackspace.com/how-to/checking-linux-file-permissions-with-ls/
    • \n
  • https://stackoverflow.com/questions/29244070/check-if-different-user-has-read-wrte-permissions-to-a-file-on-linux
    • \n
  • https://bash.cyberciti.biz/file-management/find-file-permission-script/
    • \n
  • https://www.tecmint.com/find-top-large-directories-and-files-sizes-in-linux/
    • \n
  • https://stackoverflow.com/questions/8390799/arent-pipes-in-unix-supposed-to-be-uni-directional
    • \n
  • https://www.thecrazyprogrammer.com/2014/08/c-program-for-shortest-job-first-sjf.html
    • \n
  • https://www.thecrazyprogrammer.com/2014/11/c-cpp-program-for-first-come-first-served-fcfs.html
    • \n
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SQLite and PostgreSQL are used in Development and Production, respectively. \n\n## Team\n\n| [![Noman Anjum](https://github.com/NomanAnjum09.png?size=100)](https://github.com/NomanAnjum09) | [![Hassan Ahmed](https://github.com/hassan11196.png?size=100)](https://github.com/hassan11196) | [![Ahsan Ali](https://github.com/MuhammadAichee.png?size=100)](https://github.com/MuhammadAichee) |\n| :---------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------------------------: |\n| [Noman Anjum](https://github.com/NomanAnjum09) | [Hassan Ahmed](https://github.com/hassan11196) | [Ahsan Ali](https://github.com/MuhammadAichee) |\n\n\n## QRSMS Teacher Portal: \n[![Netlify 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Three Portals. One for Teachers, One for Students and One for Faculty. A Flutter App where Students can view and mark their attendance using QR-Code option.\n\nQRSMS is built on Django Web Framework which provides a extensible environment for Future Extension and Up-gradation.\n\nA Restful Architecture allows for portability and future scalability. Python Environment is managed by Pipenv. The Front-end is powered by the Leading Web Library, React. Bootstrap alongside with Semantic-UI allow for a robust and Well-Polished Web interface. SQLite and PostgreSQL are used in Development and Production, respectively. \n\n## Team\n\n| [![Noman Anjum](https://github.com/NomanAnjum09.png?size=100)](https://github.com/NomanAnjum09) | [![Hassan Ahmed](https://github.com/hassan11196.png?size=100)](https://github.com/hassan11196) | [![Ahsan Ali](https://github.com/MuhammadAichee.png?size=100)](https://github.com/MuhammadAichee) |\n| :---------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------------------------: |\n| [Noman Anjum](https://github.com/NomanAnjum09) | [Hassan Ahmed](https://github.com/hassan11196) | [Ahsan Ali](https://github.com/MuhammadAichee) |\n\n\n## QRSMS Teacher Portal: \n[![Netlify 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Three Portals. One for Teachers, One for Students and One for Faculty. A Flutter App where Students can view and mark their attendance using QR-Code option.\n\nQRSMS is built on Django Web Framework which provides a extensible environment for Future Extension and Up-gradation.\n\nA Restful Architecture allows for portability and future scalability. Python Environment is managed by Pipenv. The Front-end is powered by the Leading Web Library, React. Bootstrap alongside with Semantic-UI allow for a robust and Well-Polished Web interface. SQLite and PostgreSQL are used in Development and Production, respectively. \n\n## Team\n\n| [![Noman Anjum](https://github.com/NomanAnjum09.png?size=100)](https://github.com/NomanAnjum09) | [![Hassan Ahmed](https://github.com/hassan11196.png?size=100)](https://github.com/hassan11196) | [![Ahsan Ali](https://github.com/MuhammadAichee.png?size=100)](https://github.com/MuhammadAichee) |\n| :---------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------------------------: |\n| [Noman Anjum](https://github.com/NomanAnjum09) | [Hassan Ahmed](https://github.com/hassan11196) | [Ahsan Ali](https://github.com/MuhammadAichee) |\n\n\n## QRSMS Teacher Portal: \n[![Netlify Status](https://api.netlify.com/api/v1/badges/0772398c-04c6-442e-b5e8-2b0a50b99e31/deploy-status)](https://app.netlify.com/sites/qrsms-teacher/deploys)\n\n### Link: [qrsms-teacher.netlify.app](qrsms-teacher.netlify.app)\n\n### Repo Link: [https://github.com/hassan11196/QRSMS-Teacher](https://github.com/hassan11196/QRSMS-Teacher)\n\n### Images:\n- ![](images/teacher1.png)\n- ![](images/teacher2.png)\n- ![](images/teacher3.png)\n\n## QRSMS Student Portal: \n[![Netlify Status](https://api.netlify.com/api/v1/badges/c3041597-8544-48af-a987-d16a63ac9933/deploy-status)](https://app.netlify.com/sites/qrsms-student/deploys)\n\n### Link: [qrsms-student.netlify.app](qrsms-student.netlify.app)\n\n### Repo Link: [https://github.com/hassan11196/QRSMS-Student](https://github.com/hassan11196/QRSMS-Student)\n\n### Images:\n- ![](images/student1.png)\n- ![](images/student2.png)\n- ![](images/student3.png)\n\n\n\n## QRSMS Admin Portal:\nCustomized Django Admin Portal for Faculty of the University to manage QRSMS.\n### Link: [Click here](https://qrsms-v1.herokuapp.com/admin)\n\n### Images:\n- ![](images/admin1.png)\n- ![](images/admin2.png)\n- ![](images/admin3.png)\n\n\n## QRSMS Flutter Application: \n[![Build Status](https://travis-ci.com/hassan11196/QRSMSApp-1.svg?token=52F5hBqAVyxvA6TSn1jQ&branch=master)](https://travis-ci.com/hassan11196/QRSMSApp-1)\n\n### Download Link: [Click here](https://github.com/hassan11196/QRSMS-Flutter-App/releases)\n\n### Repo Link: [https://github.com/hassan11196/QRSMSApp](https://github.com/hassan11196/QRSMS-Flutter-App)\n\nMade Possible with help of following additional contributors and mentors💓:\n\n|[![Mustafa Memon](https://github.com/Mustafamemon.png?size=75)](https://github.com/Mustafamemon)|\n|[![Huzaifa Abid](https://github.com/huzaifaa926.png?size=75)](https://github.com/huzaifaa926)|\n\n### Images:\n| ![](images/flutter1.jpeg) | ![](images/flutter2.jpeg) | ![](images/flutter3.jpeg) | ![](images/flutter4.jpeg) |\n| :------------------------ | :-----------------------: | ------------------------: | ------------------------: |", + "readme_title": "QRSMS", + "releases_count": 0, + "repo": "QRSMS-Teacher", + "repo_name": "QRSMS-Teacher", + "stars": 0, + "topics": [ + "python", + "django", + "django-rest-framework", + "react", + "reactjs", + "redux", + "student-information-system" + ], + "updated_at": "2020-11-30T15:23:59Z", + "url": "https://github.com/hassan11196/QRSMS-Teacher", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/QRSMS-Teacher" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/QRSMS-V1" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 333, + "contributors_count": 10, + "contributors_top": [ + { + "contributions": 221, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + }, + { + "contributions": 52, + "html_url": "https://github.com/NomanAnjum09", + "login": "NomanAnjum09" + }, + { + "contributions": 30, + "html_url": "https://github.com/apps/dependabot", + "login": "dependabot[bot]" + }, + { + "contributions": 14, + "html_url": null, + "login": "Hassan11196 G3" + }, + { + "contributions": 6, + "html_url": null, + "login": "hassan11196" + }, + { + "contributions": 5, + "html_url": null, + "login": "root" + }, + { + "contributions": 2, + "html_url": "https://github.com/MuhammadAichee", + "login": "MuhammadAichee" + }, + { + "contributions": 1, + "html_url": "https://github.com/EishaMazhar", + "login": "EishaMazhar" + } + ], + "created_at": "2019-09-07T17:41:33Z", + "default_branch": "master", + "description": "QRSMS - 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QR based Student Management System. built using React, Python Django, PostgreSQL. Three Portals. One for Teachers, One for Students and One for Faculty. A Flutter App where Students can view and mark their attendance using QR-Code option.\n\nQRSMS is built on Django Web Framework which provides a extensible environment for Future Extension and Up-gradation.\n\nA Restful Architecture allows for portability and future scalability. Python Environment is managed by Pipenv. The Front-end is powered by the Leading Web Library, React. Bootstrap alongside with Semantic-UI allow for a robust and Well-Polished Web interface. SQLite and PostgreSQL are used in Development and Production, respectively. \n\n## Team\n\n| [![Noman Anjum](https://github.com/NomanAnjum09.png?size=100)](https://github.com/NomanAnjum09) | [![Hassan Ahmed](https://github.com/hassan11196.png?size=100)](https://github.com/hassan11196) | [![Ahsan Ali](https://github.com/MuhammadAichee.png?size=100)](https://github.com/MuhammadAichee) |\n| :---------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------------------------: |\n| [Noman Anjum](https://github.com/NomanAnjum09) | [Hassan Ahmed](https://github.com/hassan11196) | [Ahsan Ali](https://github.com/MuhammadAichee) |\n\n\n## QRSMS Teacher Portal: \n[![Netlify Status](https://api.netlify.com/api/v1/badges/0772398c-04c6-442e-b5e8-2b0a50b99e31/deploy-status)](https://app.netlify.com/sites/qrsms-teacher/deploys)\n\n### Link: [qrsms-teacher.netlify.app](qrsms-teacher.netlify.app)\n\n### Repo Link: [https://github.com/hassan11196/QRSMS-Teacher](https://github.com/hassan11196/QRSMS-Teacher)\n\n### Images:\n- ![](images/teacher1.png)\n- ![](images/teacher2.png)\n- ![](images/teacher3.png)\n\n## QRSMS Student Portal: \n[![Netlify Status](https://api.netlify.com/api/v1/badges/c3041597-8544-48af-a987-d16a63ac9933/deploy-status)](https://app.netlify.com/sites/qrsms-student/deploys)\n\n### Link: [qrsms-student.netlify.app](qrsms-student.netlify.app)\n\n### Repo Link: [https://github.com/hassan11196/QRSMS-Student](https://github.com/hassan11196/QRSMS-Student)\n\n### Images:\n- ![](images/student1.png)\n- ![](images/student2.png)\n- ![](images/student3.png)\n\n\n\n## QRSMS Admin Portal:\nCustomized Django Admin Portal for Faculty of the University to manage QRSMS.\n### Link: [Click here](https://qrsms-v1.herokuapp.com/admin)\n\n### Images:\n- ![](images/admin1.png)\n- ![](images/admin2.png)\n- ![](images/admin3.png)\n\n\n## QRSMS Flutter Application: \n[![Build Status](https://travis-ci.com/hassan11196/QRSMSApp-1.svg?token=52F5hBqAVyxvA6TSn1jQ&branch=master)](https://travis-ci.com/hassan11196/QRSMSApp-1)\n\n### Download Link: [Click here](https://github.com/hassan11196/QRSMS-Flutter-App/releases)\n\n### Repo Link: [https://github.com/hassan11196/QRSMSApp](https://github.com/hassan11196/QRSMS-Flutter-App)\n\nMade Possible with help of following additional contributors and mentors💓:\n\n|[![Mustafa Memon](https://github.com/Mustafamemon.png?size=75)](https://github.com/Mustafamemon)|\n|[![Huzaifa Abid](https://github.com/huzaifaa926.png?size=75)](https://github.com/huzaifaa926)|\n\n### Images:\n| ![](images/flutter1.jpeg) | ![](images/flutter2.jpeg) | ![](images/flutter3.jpeg) | ![](images/flutter4.jpeg) |\n| :------------------------ 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"last_commit_date_default_branch": "2023-10-22T21:09:00Z", + "last_commit_oid_default_branch": "58be561b8721482c8b347b1e0ee95069aea09e07", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "MIT License", + "license_spdx": "MIT", + "name_with_owner": "hassan11196/RecognizeAndRecommend", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "TypeScript", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 2, + "pull_requests_open": 0, + "pull_requests_total": 2, + "pushed_at": "2023-10-22T21:09:00Z", + "readme_length": 4920, + "readme_text": "# Recognize and Recommend\n![logo](src/assets/Recognize%20&%20Recommend.png)\n\nA cutting-edge AI application designed for integration with superstores and outlets to recognize customers through facial recognition and recommend relevant products based on their interests and purchase history.\n\n## Table of Contents\n\n- [Introduction](#introduction)\n- [Methodology](#methodology)\n - [Recognition](#recognition)\n - [Recommendation](#recommendation)\n- [Development Phases](#development-phases)\n - [Data Collection](#data-collection)\n - [Data Cleaning and Categorization](#data-cleaning-and-categorization)\n - [Face Detection and Recognition](#face-detection-and-recognition)\n - [Product Recommendation](#product-recommendation)\n- [Getting Started](#getting-started)\n - [Prerequisites](#prerequisites)\n - [Installation](#installation)\n \n- [Machine Learning and Deep Learning Models](#machine-learning-and-deep-learning-models)\n- [Acknowledgements](#acknowledgements)\n\n## Introduction\n\nRecognize and Recommend is an innovative AI application that enhances the shopping experience by seamlessly integrating with superstores and outlets. Using state-of-the-art facial recognition technology, the application identifies customers upon arrival and recommends products tailored to their interests. Built using Django for the backend and React.js for the frontend, Recognize and Recommend employs a facial recognition system and collaborative filtering recommendation system to deliver personalized shopping experiences.\n\n## Methodology\n\n![methodology](public/image8.png)\n\n### Recognition\n\nThe application uses a high-quality camera to detect users' faces, which are then sent to the backend. The backend features an efficient image detector and recognizer that detects and encodes facial features, saving them to a database with a name or system-generated label.\n\n![recognition](public/image2.png)\n\n### Recommendation\n\nOnce a user has been successfully identified, the system checks their purchase history and product ratings. Using collaborative filtering, the application recommends products that have been highly rated by other users with similar interests.\n\n![products](public/image1.png)\n![products2](public/image5.png)\n![products3](public/image6.png)\n\n## Development Phases\n\n### Data Collection\n\nThe primary data source is the Amazon Product reviews dataset, which provides extensive information about products, customer ratings, and purchase history.\n\n### Data Cleaning and Categorization\n\nTwo primary data points are used:\n- Presenting recommended products to customers\n- Training the model to generate recommendations\n\nThe raw data is cleaned and categorized accordingly, with only product IDs, customer IDs, and customer ratings used for the recommendation system.\n\n### Face Detection and Recognition\n\nA face detection API on the frontend detects faces from video and sends them to the backend, which deploys separate modules for face recognition, such as Haar cascade, LBPH, and the face recognition API.\n\n### Product Recommendation\n\nCollaborative filtering is used to recommend products to users based on their purchase history and ratings from other customers with similar interests.\n\n## Machine Learning and Deep Learning Models\n\n- **SSD Mobilenet V1**: A Single-Shot Multibox Detection (SSD) network implemented using the Caffe* framework, used on the frontend to detect faces from the camera video.\n- **Haar cascade**: A machine learning-based approach for object detection, used on the backend to generate features after receiving the image from the frontend.\n- **LBPH**: Local Binary Pattern (LBP) is an efficient texture operator that labels image pixels by thresholding the neighborhood of each pixel, considering the result as a binary number.\n- **Collaborative Filtering Recommendation**: A recommendation model that filters or evaluates items based on the opinions of other users, helping to recommend products to customers.\n\n## Getting Started\n\n### Prerequisites\n - Python 3.6+\n - Node.js\n - NPM or Yarn\n\n### Installation\n 1. Clone the frontend repository:\n \n git clone https://github.com/hassan11196/RecognizeAndRecommend.git`\n\n 2. Clone the backend repository:\n \n git clone https://github.com/hassan11196/RecognizeAndRecommend-Backend.git\n\n 3. Install Python dependencies for the backend:\n \n cd RecognizeAndRecommend-Backend\n pip install -r requirements.txt\n\n 4. Install frontend dependencies:\n \n cd ../RecognizeAndRecommend/frontend\n npm install\n\n 5. Run the backend development server:\n \n cd ../RecognizeAndRecommend-Backend\n python manage.py runserver\n\n 6. Launch the frontend:\n \n cd ../RecognizeAndRecommend/frontend\n npm start\n\n\n\n## Acknowledgements\n\nThis project was a Semester Project for the course \"Human Computer Interaction\" by \"Dr. Rauf Shams Malick\" FALL 2020. 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+ "language": "Shell", + "size": 1796 + }, + { + "language": "Dockerfile", + "size": 425 + }, + { + "language": "HTML", + "size": 230 + } + ], + "last_commit_date_default_branch": "2020-12-25T04:07:59Z", + "last_commit_oid_default_branch": "30320bb69ccf273b99a2817f0111ca1a82826df8", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/RecognizeAndRecommend-Backend", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "Jupyter Notebook", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 3, + "pull_requests_open": 0, + "pull_requests_total": 3, + "pushed_at": "2020-12-25T04:08:00Z", + "readme_length": 4464, + "readme_text": "# Recognize And Recommend\n## Send Base64 of Face to \n\n`POST /recognition/recognize-face`\nRequest Body:\n```\n{\n \"face\":\"data:image/png;base64,iVB..\"\n}\n```\nif it returns a username then user is registered.\n\nelse if it returns unknown. prompt user for a username and send a base64 Face along with username to the following endpoint\n\n`POST localhost:8000/api/v1/users/`\n\nRequest Body:\n``` \n{\n \"username\":\"mustafa\",\n \"profile_photo\":\"data:image/png;base64,iVBOR...\"\n}\n```\nThis endpoint returns a `auth_token`, save it. it will be used to authenticate the user with the server.\n\nTo Save a user face for training\nsend Base64 face along with `auth_token` in header.\nCall this api in a loop and send 30 different Base64 Faces.\n\n`POST localhost:8000/recognition/save-face`\n\nRequest Body:\n```\n{\n \"face\":\"data:image/png;base64,iVB..\"\n}\n```\nHeaders:\n```\nAuthorization : Token auth_token\n```\n\nAfter you have saved a users face, hit this api to start training.\n\n`POST localhost:8000/recognition/train-face`\n\n\n# FACE RECOGNITION 2\n\n#### To Get CSRFMIDDLEWARETOKEN\nget: http://127.0.0.1:8000/facerecognition/get_csrf\n\n#### To Train On Individual Image\npost: http://127.0.0.1:8000/facerecognition/add_person/\n\nmultipart form data: \n\ncsrfmiddlewaretoken = \"\"\nname = \"\"\nimage = \"\" //Image File\n\n#### To Store Person Data Temporily for later training\npost: http://127.0.0.1:8000/facerecognition/add_temp_person/\n\nmultipart form data: \n\ncsrfmiddlewaretoken = \"\"\nname = \"\"\nimage = \"\" //Image File\n\n#### To Train All Temporily Stored Images\nget: http://127.0.0.1:8000/facerecognition/batch_train/\n\n#### To Get Face Label\npost: http://127.0.0.1:8000/facerecognition/get_label/\n\nmultipart form data: \n\ncsrfmiddlewaretoken = \"\"\nimage = \"\" //Image File\n\n\n\n## Python - Django Rest Framework boilerplate\n\nThis is boilerplate for starting fresh new DRF projects. It's built using [cookiecutter-django-rest](https://github.com/agconti/cookiecutter-django-rest).\n\n## Highlights\n\n- Modern Python development with Python 3.6+\n- Bleeding edge Django 3.0+\n- Fully dockerized, local development via docker-compose.\n- MySQL\n- Full test coverage, continuous integration, and continuous deployment.\n- Celery tasks\n\n### Features built-in\n\n- Token-based Auth system\n- Social (FB + G+) signup/sigin\n- API Throttling enabled\n- Password reset endpoints\n- User model with profile picture field using Easy Thumbnails\n- Sentry setup\n- Swagger API docs out-of-the-box\n- CodeLinter (flake8) and CodeFormatter (yapf)\n- Tests (with mocking and factories) with code-coverage support\n\n## API Docs\n\nAPI documentation is automatically generated using Swagger. You can view documention by visiting this [link](http://localhost:8000/swagger).\n\n## Prerequisites\n\nIf you are familiar with Docker, then you just need [Docker](https://docs.docker.com/docker-for-mac/install/). If you don't want to use Docker, then you just need Python3 and MySQL installed.\n\n## Local Development with Docker\n\nStart the dev server for local development:\n\n```bash\ncp .env.dist .env\ndocker-compose up\n```\n\nRun a command inside the docker container:\n\n```bash\ndocker-compose run --rm web [command]\n```\n\n## Local Development without Docker\n\n### Install\n\n```bash\npython3 -m venv env && source env/bin/activate # activate venv\ncp .env.dist .env # create .env file and fill-in DB info\npip install -r requirements.txt # install py requirements\n./manage.py migrate # run migrations\n./manage.py collectstatic --noinput # collect static files\nredis-server # run redis locally for celery\ncelery -A src.config worker --beat --loglevel=debug\n --pidfile=\"./celerybeat.pid\"\n --scheduler django_celery_beat.schedulers:DatabaseScheduler # run celery beat and worker\n```\n\n### Run dev server\n\nThis will run server on [http://localhost:8000](http://localhost:8000)\n\n```bash\n./manage.py runserver\n```\n\n### Create superuser\n\nIf you want, you can create initial super-user with next commad:\n\n```bash\n./manage.py createsuperuser\n```\n\n### Running Tests\n\nTo run all tests with code-coverate report, simple run:\n\n```bash\n./manage.py test\n```\n\n### MySQL - privileges issue\n\nIf you experience any problems with DB user privileges during migrations run query below:\n\n`GRANT ALL PRIVILEGES ON [mydb].* TO '[user]'@'localhost';`\n\nYou're now ready to ROCK! ✨ 💅 🛳", + "readme_title": "Recognize And Recommend", + "releases_count": 0, + "repo": "RecognizeAndRecommend-Backend", + "repo_name": "RecognizeAndRecommend-Backend", + "stars": 0, + "topics": [ + "collaborative-filtering", + "deep-learning", + "python", + "recognition", + "recommender-system", + "ssd" + ], + "updated_at": "2023-03-05T01:20:54Z", + "url": "https://github.com/hassan11196/RecognizeAndRecommend-Backend", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/RecognizeAndRecommend-Backend" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/Record-Linkage-Comparison-Patterns" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 2, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 2, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + } + ], + "created_at": "2020-07-24T17:50:57Z", + "default_branch": "master", + "description": null, + "dirs_total_count": 2, + "files_root_entries": [ + { + "path": ".gitignore", + "size": 0, + "type": "file" + }, + { + "path": ".ipynb_checkpoints", + "size": 0, + "type": "dir" + }, + { + "path": "PlayGround.ipynb", + "size": 108911, + "type": "file" + }, + { + "path": "Readme.md", + "size": 823, + "type": "file" + }, + { + "path": "donation.zip", + "size": 56448373, + "type": "file" + }, + { + "path": "donation", + "size": 0, + "type": "dir" + } + ], + "files_total_count": 17, + "first_commit_date_default_branch": "2020-07-24T17:51:05Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": true, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/Record-Linkage-Comparison-Patterns", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Jupyter Notebook", + "size": 217822 + } + ], + "last_commit_date_default_branch": "2020-07-26T18:21:52Z", + "last_commit_oid_default_branch": "6729be238441a60180e5cc64fd6566d1df67d96e", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/Record-Linkage-Comparison-Patterns", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "Jupyter Notebook", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2020-07-26T18:22:11Z", + "readme_length": 822, + "readme_text": "# Record Linkage Comparison Patterns\n\nPlaying with dataset for Exploratory Data Analysis and Binary Classification.\n\nCheck [Notebook](PlayGround.ipynb).\n\n# Source:\n\nThe underlying records stem from the epidemiological cancer registry of the\nGerman state of North Rhine-Westphalia (Epidemiologisches Krebsregister NRW,\nhttp://www.krebsregister.nrw.de). Creation of comparison patterns and\nassignment of matching status were undertaken by staff members of\nthe Institute for Medical Biostatistics, Epidemiology and Informatics (IMBEI)\nat the University Medical Center of the Johannes Gutenberg University in Mainz,\nGermany (http://www.imbei.uni-mainz.de).\n\n# References :\n\n[https://archive.ics.uci.edu/ml/datasets/Record+Linkage+Comparison+Patterns](https://archive.ics.uci.edu/ml/datasets/Record+Linkage+Comparison+Patterns)", + "readme_title": "Record Linkage Comparison Patterns", + "releases_count": 0, + "repo": "Record-Linkage-Comparison-Patterns", + "repo_name": "Record-Linkage-Comparison-Patterns", + "stars": 0, + "topics": [], + "updated_at": "2020-07-26T18:22:14Z", + "url": "https://github.com/hassan11196/Record-Linkage-Comparison-Patterns", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/Record-Linkage-Comparison-Patterns" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/Tickets-API" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 58, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 58, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + } + ], + "created_at": "2020-04-19T17:46:54Z", + "default_branch": "master", + "description": null, + "dirs_total_count": 7, + "files_root_entries": [ + { + "path": ".env", + "size": 196, + "type": "file" + }, + { + "path": ".gitignore", + "size": 3422, + "type": "file" + }, + { + "path": ".travis.yml", + "size": 2805, + "type": "file" + }, + { + "path": ".vscode", + "size": 0, + "type": "dir" + }, + { + "path": "Dockerfile", + "size": 391, + "type": "file" + }, + { + "path": "LICENSE", + "size": 1078, + "type": "file" + }, + { + "path": "Pipfile", + "size": 337, + "type": "file" + }, + { + "path": "Pipfile.lock", + "size": 33156, + "type": "file" + }, + { + "path": "Procfile", + "size": 41, + "type": "file" + }, + { + "path": "README.md", + "size": 1790, + "type": "file" + }, + { + "path": "app", + "size": 0, + "type": "dir" + }, + { + "path": "docker-compose.test.yaml", + "size": 113, + "type": "file" + }, + { + "path": "docker-compose.yaml", + "size": 521, + "type": "file" + }, + { + "path": "heroku.yml", + "size": 112, + "type": "file" + }, + { + "path": "init-mongo.js", + "size": 197, + "type": "file" + }, + { + "path": "requirements.txt", + "size": 1098, + "type": "file" + } + ], + "files_total_count": 27, + "first_commit_date_default_branch": "2020-02-01T09:27:57Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": true, + "has_docs": false, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/Tickets-API", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Python", + "size": 13494 + }, + { + "language": "Dockerfile", + "size": 391 + }, + { + "language": "JavaScript", + "size": 197 + } + ], + "last_commit_date_default_branch": "2020-04-23T18:01:11Z", + "last_commit_oid_default_branch": "5694ca7e71479ae8d8e9ade3734b0ae77df120ef", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "MIT License", + "license_spdx": "MIT", + "name_with_owner": "hassan11196/Tickets-API", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "Python", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 3, + "pull_requests_open": 0, + "pull_requests_total": 3, + "pushed_at": "2020-04-23T18:01:12Z", + "readme_length": 1787, + "readme_text": "# Tickets API\n\n \n\nYou are working in a large Telecommunication company as a developer and you got the task to build a new API for their soon to be popular ticket management system. API should support following use case scenarios: create a new ticket, update ticket data, find ticket by id, find ticket by filter criteria.\n \n## Project Structue:\n\nmodels - pydantic models that are used in api\n\ndb - db specific utils\n\ncore - some general components (configuration for db url)\n\napi - handlers for routes\n\nmain.py - FastAPI application instance and api router including\n\n## Project setup - without docker\n\n1. Create the virtual environment.\n\n```\n\nvirtualenv /path/to/venv --python=/path/to/python3\n\n```\n\nYou can find out the path to your `python3` interpreter with the command `which python3`.\n\n \n\n2. Activate the environment and install dependencies.\n\n```\n\nsource /path/to/venv/bin/activate\n\npip install -r requirements.txt\n\n```\n\n \n\n3. Launch the service\n\n```\n\nuvicorn app.main:app\n\n```\n\n \n\n## Posting requests locally\n\nWhen the service is running, try\n\n```\n\n127.0.0.1/docs\n\n```\n\n\n## Project setup with Docker\n\n1. 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Incompatible with\n\n --abort-on-container-exit.\n\n --force-recreate Recreate containers even if their configuration\n\n and image haven't changed.\n\n --build Build images before starting containers.\n\n --no-deps Don't start linked services.\n```", + "readme_title": "Tickets API", + "releases_count": 0, + "repo": "Tickets-API", + "repo_name": "Tickets-API", + "stars": 1, + "topics": [], + "updated_at": "2024-01-15T01:19:44Z", + "url": "https://github.com/hassan11196/Tickets-API", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/Tickets-API" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/Torch-TensorRT" + ], + 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PyTorch/TorchScript, targeting NVIDIA GPUs via NVIDIA's TensorRT Deep Learning Optimizer and Runtime. Unlike PyTorch's Just-In-Time (JIT) compiler, Torch-TensorRT is an Ahead-of-Time (AOT) compiler, meaning that before you deploy your TorchScript code, you go through an explicit compile step to convert a standard TorchScript program into an module targeting a TensorRT engine. Torch-TensorRT operates as a PyTorch extention and compiles modules that integrate into the JIT runtime seamlessly. After compilation using the optimized graph should feel no different than running a TorchScript module. You also have access to TensorRT's suite of configurations at compile time, so you are able to specify operating precision (FP32/FP16/INT8) and other settings for your module.\n\nResources:\n- [Documentation](https://nvidia.github.io/Torch-TensorRT/)\n- [Torch-TensorRT Explained in 2 minutes!](https://www.youtube.com/watch?v=TU5BMU6iYZ0&ab_channel=NVIDIADeveloper)\n- [Comprehensive Discusion (GTC Event)](https://www.nvidia.com/en-us/on-demand/session/gtcfall21-a31107/)\n- [Pre-built Docker Container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch). To use this container, make an NGC account and sign in to NVIDIA's registry with an API key. Refer to [this guide](https://docs.nvidia.com/ngc/ngc-catalog-user-guide/index.html#registering-activating-ngc-account) for the same.\n\n## NVIDIA NGC Container\nTorch-TensorRT is distributed in the ready-to-run NVIDIA [NGC PyTorch Container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch) starting with 21.11. We recommend using this prebuilt container to experiment & develop with Torch-TensorRT; it has all dependencies with the proper versions as well as example notebooks included.\n\n## Building a docker container for Torch-TensorRT\n\nWe provide a `Dockerfile` in `docker/` directory. It expects a PyTorch NGC container as a base but can easily be modified to build on top of any container that provides, PyTorch, CUDA, cuDNN and TensorRT. The dependency libraries in the container can be found in the release notes.\n\nPlease follow this instruction to build a Docker container.\n\n```bash\ndocker build --build-arg BASE= -f docker/Dockerfile -t torch_tensorrt:latest .\n```\n\nIn the case of building on top of a custom base container, you first must determine the\nversion of the PyTorch C++ ABI. If your source of PyTorch is pytorch.org, likely this is the pre-cxx11-abi in which case you must modify `//docker/dist-build.sh` to not build the\nC++11 ABI version of Torch-TensorRT.\n\nYou can then build the container using:\n\n\n```bash\ndocker build --build-arg BASE_IMG= -f docker/Dockerfile -t torch_tensorrt:latest .\n```\n\nIf you would like to build outside a docker container, please follow the section [Compiling Torch-TensorRT](#compiling-torch-tensorrt)\n\n## Example Usage\n\n### C++\n\n```c++\n#include \"torch/script.h\"\n#include \"torch_tensorrt/torch_tensorrt.h\"\n\n...\n// Set input datatypes. Allowerd options torch::{kFloat, kHalf, kChar, kInt32, kBool}\n// Size of input_dtypes should match number of inputs to the network.\n// If input_dtypes is not set, default precision follows traditional PyT / TRT rules\nauto input = torch_tensorrt::Input(dims, torch::kHalf);\nauto compile_settings = torch_tensorrt::ts::CompileSpec({input});\n// FP16 execution\ncompile_settings.enabled_precisions = {torch::kHalf};\n// Compile module\nauto trt_mod = torch_tensorrt::ts::compile(ts_mod, compile_settings);\n// Run like normal\nauto results = trt_mod.forward({in_tensor});\n// Save module for later\ntrt_mod.save(\"trt_torchscript_module.ts\");\n...\n```\n\n### Python\n\n```py\nimport torch_tensorrt\n\n...\n\ntrt_ts_module = torch_tensorrt.compile(torch_script_module,\n inputs = [example_tensor, # Provide example tensor for input shape or...\n torch_tensorrt.Input( # Specify input object with shape and dtype\n min_shape=[1, 3, 224, 224],\n opt_shape=[1, 3, 512, 512],\n max_shape=[1, 3, 1024, 1024],\n # For static size shape=[1, 3, 224, 224]\n dtype=torch.half) # Datatype of input tensor. Allowed options torch.(float|half|int8|int32|bool)\n ],\n enabled_precisions = {torch.half}, # Run with FP16)\n\nresult = trt_ts_module(input_data) # run inference\ntorch.jit.save(trt_ts_module, \"trt_torchscript_module.ts\") # save the TRT embedded Torchscript\n```\n\n> Notes on running in lower precisions:\n>\n> - Enabled lower precisions with compile_spec.enabled_precisions\n> - The module should be left in FP32 before compilation (FP16 can support half tensor models)\n> - Provided input tensors dtype should be the same as module before compilation, regardless of `enabled_precisions`. This can be overrided by setting `Input::dtype`\n\n## Platform Support\n\n| Platform | Support |\n| ------------------- | ------------------------------------------------ |\n| Linux AMD64 / GPU | **Supported** |\n| Linux aarch64 / GPU | **Native Compilation Supported on JetPack-4.4+ (use v1.0.0 for the time being)** |\n| Linux aarch64 / DLA | **Native Compilation Supported on JetPack-4.4+ (use v1.0.0 for the time being)** |\n| Windows / GPU | **Unofficial Support** |\n| Linux ppc64le / GPU | - |\n| NGC Containers | **Included in PyTorch NGC Containers 21.11+** |\n\n> Torch-TensorRT will be included in NVIDIA NGC containers (https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) starting in 21.11.\n\n> Note: Refer NVIDIA NGC container(https://ngc.nvidia.com/catalog/containers/nvidia:l4t-pytorch) for PyTorch libraries on JetPack.\n\n### Dependencies\n\nThese are the following dependencies used to verify the testcases. Torch-TensorRT can work with other versions, but the tests are not guaranteed to pass.\n\n- Bazel 5.1.1\n- Libtorch 1.11.0 (built with CUDA 11.3)\n- CUDA 11.3\n- cuDNN 8.2.1\n- TensorRT 8.2.4.2\n\n## Prebuilt Binaries and Wheel files\n\nReleases: https://github.com/NVIDIA/Torch-TensorRT/releases\n\n## Compiling Torch-TensorRT\n\n### Installing Dependencies\n\n#### 0. Install Bazel\n\nIf you don't have bazel installed, the easiest way is to install bazelisk using the method of you choosing https://github.com/bazelbuild/bazelisk\n\nOtherwise you can use the following instructions to install binaries https://docs.bazel.build/versions/master/install.html\n\nFinally if you need to compile from source (e.g. aarch64 until bazel distributes binaries for the architecture) you can use these instructions\n\n```sh\nexport BAZEL_VERSION=\nmkdir bazel\ncd bazel\ncurl -fSsL -O https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-dist.zip\nunzip bazel-$BAZEL_VERSION-dist.zip\nbash ./compile.sh\n```\n\nYou need to start by having CUDA installed on the system, LibTorch will automatically be pulled for you by bazel,\nthen you have two options.\n\n#### 1. Building using cuDNN & TensorRT tarball distributions\n\n> This is recommended so as to build Torch-TensorRT hermetically and insures any bugs are not caused by version issues\n\n> Make sure when running Torch-TensorRT that these versions of the libraries are prioritized in your `$LD_LIBRARY_PATH`\n\n1. You need to download the tarball distributions of TensorRT and cuDNN from the NVIDIA website.\n - https://developer.nvidia.com/cudnn\n - https://developer.nvidia.com/tensorrt\n2. Place these files in a directory (the directories `third_party/dist_dir/[x86_64-linux-gnu | aarch64-linux-gnu]` exist for this purpose)\n3. Compile using:\n\n``` shell\nbazel build //:libtorchtrt --compilation_mode opt --distdir third_party/dist_dir/[x86_64-linux-gnu | aarch64-linux-gnu]\n```\n\n#### 2. Building using locally installed cuDNN & TensorRT\n\n> If you find bugs and you compiled using this method please disclose you used this method in the issue\n> (an `ldd` dump would be nice too)\n\n1. Install TensorRT, CUDA and cuDNN on the system before starting to compile.\n2. In `WORKSPACE` comment out\n\n```py\n# Downloaded distributions to use with --distdir\nhttp_archive(\n name = \"cudnn\",\n urls = [\"\",],\n\n build_file = \"@//third_party/cudnn/archive:BUILD\",\n sha256 = \"\",\n strip_prefix = \"cuda\"\n)\n\nhttp_archive(\n name = \"tensorrt\",\n urls = [\"\",],\n\n build_file = \"@//third_party/tensorrt/archive:BUILD\",\n sha256 = \"\",\n strip_prefix = \"TensorRT-\"\n)\n```\n\nand uncomment\n\n```py\n# Locally installed dependencies\nnew_local_repository(\n name = \"cudnn\",\n path = \"/usr/\",\n build_file = \"@//third_party/cudnn/local:BUILD\"\n)\n\nnew_local_repository(\n name = \"tensorrt\",\n path = \"/usr/\",\n build_file = \"@//third_party/tensorrt/local:BUILD\"\n)\n```\n\n3. Compile using:\n\n``` shell\nbazel build //:libtorchtrt --compilation_mode opt\n```\n\n### Debug build\n\n``` shell\nbazel build //:libtorchtrt --compilation_mode=dbg\n```\n\n### Native compilation on NVIDIA Jetson AGX\nWe performed end to end testing on Jetson platform using Jetpack SDK 4.6.\n\n``` shell\nbazel build //:libtorchtrt --platforms //toolchains:jetpack_4.6\n```\n\n> Note: Please refer [installation](docs/tutorials/installation.html) instructions for Pre-requisites\n\nA tarball with the include files and library can then be found in bazel-bin\n\n### Running Torch-TensorRT on a JIT Graph\n\n> Make sure to add LibTorch to your LD_LIBRARY_PATH
\n> `export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$(pwd)/bazel-Torch-TensorRT/external/libtorch/lib`\n\n``` shell\nbazel run //cpp/bin/torchtrtc -- $(realpath ) out.ts \n```\n\n## Compiling the Python Package\n\nTo compile the python package for your local machine, just run `python3 setup.py install` in the `//py` directory.\nTo build wheel files for different python versions, first build the Dockerfile in ``//py`` then run the following\ncommand\n\n```\ndocker run -it -v$(pwd)/..:/workspace/Torch-TensorRT build_torch_tensorrt_wheel /bin/bash /workspace/Torch-TensorRT/py/build_whl.sh\n```\n\nPython compilation expects using the tarball based compilation strategy from above.\n\n\n## Testing using Python backend\n\nTorch-TensorRT supports testing in Python using [nox](https://nox.thea.codes/en/stable)\n\nTo install the nox using python-pip\n\n```\npython3 -m pip install --upgrade nox\n```\n\nTo list supported nox sessions:\n\n```\nnox --session -l\n```\n\nEnvironment variables supported by nox\n\n```\nPYT_PATH - To use different PYTHONPATH than system installed Python packages\nTOP_DIR - To set the root directory of the noxfile\nUSE_CXX11 - To use cxx11_abi (Defaults to 0)\nUSE_HOST_DEPS - To use host dependencies for tests (Defaults to 0)\n```\n\nUsage example\n\n```\nnox --session l0_api_tests\n```\n\nSupported Python versions:\n```\n[\"3.7\", \"3.8\", \"3.9\", \"3.10\"]\n```\n\n## How do I add support for a new op...\n\n### In Torch-TensorRT?\n\nThanks for wanting to contribute! There are two main ways to handle supporting a new op. Either you can write a converter for the op from scratch and register it in the NodeConverterRegistry or if you can map the op to a set of ops that already have converters you can write a graph rewrite pass which will replace your new op with an equivalent subgraph of supported ops. Its preferred to use graph rewriting because then we do not need to maintain a large library of op converters. Also do look at the various op support trackers in the [issues](https://github.com/NVIDIA/Torch-TensorRT/issues) for information on the support status of various operators.\n\n### In my application?\n\n> The Node Converter Registry is not exposed in the top level API but in the internal headers shipped with the tarball.\n\nYou can register a converter for your op using the `NodeConverterRegistry` inside your application.\n\n## Structure of the repo\n\n| Component | Description |\n| ------------------------ | ------------------------------------------------------------ |\n| [**core**](core) | Main JIT ingest, lowering, conversion and runtime implementations |\n| [**cpp**](cpp) | C++ API and CLI source |\n| [**examples**](examples) | Example applications to show different features of Torch-TensorRT |\n| [**py**](py) | Python API for Torch-TensorRT |\n| [**tests**](tests) | Unit tests for Torch-TensorRT |\n\n## Contributing\n\nTake a look at the [CONTRIBUTING.md](CONTRIBUTING.md)\n\n\n## License\n\nThe Torch-TensorRT license can be found in the LICENSE file. It is licensed with a BSD Style licence", + "readme_title": "Torch-TensorRT", + "releases_count": 0, + "repo": "Torch-TensorRT", + "repo_name": "Torch-TensorRT", + "stars": 0, + "topics": [], + "updated_at": "2022-06-17T06:01:10Z", + "url": "https://github.com/hassan11196/Torch-TensorRT", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/Torch-TensorRT" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/Toxic-Comment-Classification" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 1, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 1, + "html_url": 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So use this one line command to export them::\n\n export PYTHONPATH=$PYTHONPATH:/usr/lib64/python2.7/site-packages:/usr/lib/python2.7/site-packages\n\nLoading WMAgent Environment\n~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nSome of the scripts need WMAgent libraries, which at the moment are only installed on WMAgent machines.\n\nLog in to the machine and type::\n\n source /data/srv/wmagent/current/apps/wmagent/etc/profile.d/init.sh\n\nScripts that interact with the Request Manager\n----------------------------------------------\n\nassign.py\n~~~~~~~~~\n\n.. automodule:: WmAgentScripts.assign\n\n.. program-output:: python ../WmAgentScripts/assign.py -h\n\n.. Note::\n\n - You can provided the -l LFN parameter, be careful to assign the proper one MergeLFN. By default the script will assign the lfn from que original request asociated, in case of ACDCs.\n - You can use this script to assign any kind of workflow.\n - You can use a text file to assign multiple workflows at the same time.\n - You may use additional options to:\n * enforce disk replica subscription\n * change dashboard activity\n * change processing version\n * fix a processing string or acquisition era\n * You can also provide a list of sites separated by commas (no spaces) T1_US_FNAL,T2_US_UCSD,...\n * You can use -s acdc: It will assign to the sites taken from the ACDC server.\n * You can use -s all: It will assign to all sites available (Works for any taskchain acdc).\n * You can skip -s option: It will assign to the \"good site\" list (Works for any clone you need).\n\n\nchangePriorityWorkflow.py\n~~~~~~~~~\n\n.. automodule:: WmAgentScripts.changePriorityWorkflow\n\n.. program-output:: python ../WmAgentScripts/changePriorityWorkflow.py -h\n\n\nchangeSplittingWorkflow.py\n~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nThis script allows to change the splitting of a request, on a given task name\n\n.. automodule:: WmAgentScripts.changeSplittingWorkflow\n\n.. program-output:: python ../WmAgentScripts/changeSplittingWorkflow.py -h\n\n.. 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During our research, we found out that there are a wide variety of ambulances that provide different kinds of services so we included a service selection page for people to pick the best ambulance that suits their need. In addition, we also add a section to accommodate the COVID-19 situation to notify the driver whether the user needs special care of COVID-19 and remind them to wear a facial cover.\n\nDuring the process of hacking, we all challenge ourselves and try to collaborate as efficiently as possible. There is only 1 person in our team that has experience in app development. Therefore, we all have to step out of our comfort zone to learn new technical skills such as UI design and Backend Integration. Also, our team is an international gathering with people from different places of the world and different backgrounds. That said, we need to manage our tasks well so that people can work and cooperate in different time zone. Nevertheless, we all developed new skills and finish this product at the end. Teamwork is the best way to overcome any barriers!", + "readme_title": "MedHelp", + "releases_count": 0, + "repo": "ambulance-app", + "repo_name": "ambulance-app", + "stars": 0, + "topics": [], + "updated_at": "2020-12-04T03:33:34Z", + "url": "https://github.com/hassan11196/ambulance-app", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/ambulance-app" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/angular" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 2, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 2, + "html_url": "https://github.com/shamir456", + "login": "shamir456" + } + ], + "created_at": "2019-11-30T11:20:33Z", + "default_branch": "master", + "description": null, + "dirs_total_count": 7, + "files_root_entries": [ + { + "path": ".editorconfig", + "size": 246, + "type": "file" + }, + { + "path": ".gitignore", + "size": 631, + "type": "file" + }, + { + "path": "README.md", + "size": 1022, + "type": "file" + }, + { + "path": "angular.json", + "size": 3585, + "type": "file" + }, + { + "path": "browserslist", + "size": 429, + "type": "file" + }, + { + "path": "e2e", + "size": 0, + "type": "dir" + }, + { + "path": "karma.conf.js", + "size": 1017, + "type": "file" + }, + { + "path": "package-lock.json", + "size": 476561, + "type": "file" + }, + { + "path": "package.json", + "size": 1279, + "type": "file" + }, + { + "path": "src", + "size": 0, + "type": "dir" + }, + { + "path": "tsconfig.app.json", + "size": 270, + "type": "file" + }, + { + "path": "tsconfig.json", + "size": 543, + "type": "file" + }, + { + "path": "tsconfig.spec.json", + "size": 270, + "type": "file" + }, + { + "path": "tslint.json", + "size": 1953, + "type": "file" + } + ], + "files_total_count": 35, + "first_commit_date_default_branch": "2019-11-21T14:07:59Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/angular", + "is_archived": false, + "is_fork": true, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "HTML", + "size": 25841 + }, + { + "language": "TypeScript", + "size": 8372 + }, + { + "language": "JavaScript", + "size": 1827 + }, + { + "language": "CSS", + "size": 80 + } + ], + "last_commit_date_default_branch": "2019-11-23T18:34:09Z", + "last_commit_oid_default_branch": "8f6dfd07073ba959364d13c87db687b3100568e6", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/angular", + "owner": "hassan11196", + "parent_repo": "Developer-Student-Club-NUCES/angular", + "parent_url": "https://github.com/Developer-Student-Club-NUCES/angular", + "primary_language": "HTML", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2019-11-23T18:34:33Z", + "readme_length": 1021, + "readme_text": "# Myapp\n\nThis project was generated with [Angular CLI](https://github.com/angular/angular-cli) version 8.3.9.\n\n## Development server\n\nRun `ng serve` for a dev server. Navigate to `http://localhost:4200/`. The app will automatically reload if you change any of the source files.\n\n## Code scaffolding\n\nRun `ng generate component component-name` to generate a new component. You can also use `ng generate directive|pipe|service|class|guard|interface|enum|module`.\n\n## Build\n\nRun `ng build` to build the project. The build artifacts will be stored in the `dist/` directory. 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\n \n

\n\n# _archi_ - AI Augmented Research Chat Intelligence\n\n[![CI](https://github.com/archi-physics/archi/actions/workflows/pr-preview.yml/badge.svg)](https://github.com/archi-physics/archi/actions/workflows/pr-preview.yml)\n[![Docs](https://img.shields.io/badge/docs-online-blue)](https://archi-physics.github.io/archi/)\n\n_archi_ is a retrieval-augmented generation framework for research and education teams who need a low-barrier to entry, configurable, private, and extensible assistant. The system was first developed at MIT for the SubMIT computing project, and now powers chat, ticketing, and course-support workflows across academia and research organizations.\n\n## Key Capabilities\n\n_archi_ provides:\n- Customizable AI pipelines that combine data retrieval and LLMs (and more tools to come!).\n- Data ingestion connectors: web links, git repositories, local files, JIRA, and more.\n- Interfaces: chat app, ticketing assistant, email bot, and more.\n- Support for running or interacting with local and API-based LLMs.\n- Modular design that allows custom data sources, LLM backends, and deployment targets.\n- Containerized services and CLI utilities for repeatable deployments.\n\n## Documentation\n\nThe [docs](https://archi-physics.github.io/archi/) are organized as follows:\n\n- [Install](https://archi-physics.github.io/archi/install/) — system requirements and installation.\n- [Quickstart](https://archi-physics.github.io/archi/quickstart/) — deploy your first Archi instance in minutes.\n- [User Guide](https://archi-physics.github.io/archi/user_guide/) — overview of all capabilities.\n- [Data Sources](https://archi-physics.github.io/archi/data_sources/) — configure web links, git, JIRA, Redmine, and more.\n- [Services](https://archi-physics.github.io/archi/services/) — chat, uploader, data manager, Piazza, and other interfaces.\n- [Models & Providers](https://archi-physics.github.io/archi/models_providers/) — LLM providers, embeddings, and BYOK.\n- [Agents & Tools](https://archi-physics.github.io/archi/agents_tools/) — agent specs, tools, MCP integration.\n- [Configuration](https://archi-physics.github.io/archi/configuration/) — full YAML config schema reference.\n- [CLI Reference](https://archi-physics.github.io/archi/cli_reference/) — all CLI commands and options.\n- [API Reference](https://archi-physics.github.io/archi/api_reference/) — REST API endpoints.\n- [Benchmarking](https://archi-physics.github.io/archi/benchmarking/) — evaluate retrieval and response quality.\n- [Advanced Setup](https://archi-physics.github.io/archi/advanced_setup_deploy/) — GPU setup and production deployment.\n- [Developer Guide](https://archi-physics.github.io/archi/developer_guide/) — architecture, contributing, and extension patterns.\n- [Troubleshooting](https://archi-physics.github.io/archi/troubleshooting/) — common issues and fixes.\n\n## Getting Started\n\nFollow the [Install](https://archi-physics.github.io/archi/install/) and [Quickstart](https://archi-physics.github.io/archi/quickstart/) guide to set up prerequisites, configure data sources, and launch an instance.\n\n## Contributing\n\nWe welcome fixes and new integrations—see the [Developer Guide](https://archi-physics.github.io/archi/developer_guide/) for coding standards, testing instructions, and contribution tips. Please open issues or pull requests on the [GitHub repository](https://github.com/archi-physics/archi).\n\n## License and Support\n\n_archi_ is released under the [MIT License](LICENSE). 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The service watches a configurable data directory, performs incremental ingestion into ChromaDB and PostgreSQL, and exposes a FastAPI surface for querying indexed context.\n\n## Prerequisites\n\n- Docker and Docker Compose\n- Copy `.env.example` to `.env` and fill in secrets (PostgreSQL credentials, Chroma token, etc.)\n- Ensure the data source path (`DATA_SOURCE_PATH`) points to the repository of documents (default `../Material_GreenHorizon`).\n\n## Getting Started\n\n```bash\ncd argrag\ncp .env.example .env\n# edit .env as needed\ndocker compose up --build\n```\n\nThe FastAPI server will be available at `http://localhost:8000`. A ChromaDB service runs on `8001`, and PostgreSQL on `5432`.\n\n## Key Endpoints\n\n- `GET /healthz` – readiness probe.\n- `POST /ingest/run` – trigger a full rescan and re-index of the data source.\n- `POST /ingest/clear-and-reindex` – clear all documents from ChromaDB and PostgreSQL, then re-index everything from scratch.\n- `POST /query` – submit a retrieval query. Supports `top_k`, score thresholds, metadata filters, and MMR toggles.\n- `GET /documents` – list indexed documents and their current digests.\n- `GET /documents/content?path=` – retrieve the full content of any document by its path.\n- `GET /documents/stats` – get statistics about indexed documents.\n- `GET /chroma/stats` – get ChromaDB collection statistics.\n- `POST /metadata/generate` – manually trigger metadata generation.\n- `GET /metadata/summary` – get corpus metadata summary.\n\n\n## Configuration\n\nRuntime knobs live in `config/settings.yaml` (chunk size, glob patterns, etc.). Secrets and environment overrides belong in `.env`. 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Starter Template \n\nThis is the Starter template for Artist Use Cases\n\n\n## 🚀 Quick start\n\n```\n$ gatsby new my-project https://github.com/salemaziel/artist-1page\n$ cd my-project\n$ gatsby develop\n```\n\n## What's included\n\nThis starter is based on [gatsby-starter-default](https://github.com/gatsbyjs/gatsby-starter-default), and modified to include:\n\n* [bootstrap@4](https://www.npmjs.com/package/bootstrap)\n* [react-bootstrap@1](https://www.npmjs.com/package/bootstrap)\n* [bootswatch@4](https://www.npmjs.com/package/bootswatch) (optional)\n\n## Using themes (optional)\n\nYou easily use a theme from [Bootswatch](https://www.bootswatch.com) by uncommenting a few lines in `layout.scss`\n\nExample:\n\n```\n@import \"~bootswatch/dist/superhero/variables\";\n@import \"~bootstrap/scss/bootstrap\";\n@import \"~bootswatch/dist/superhero/bootswatch\";\n```", + "readme_title": "Artist UseCase - Starter Template", + "releases_count": 0, + "repo": "artist-1page", + "repo_name": "artist-1page", + "stars": 0, + "topics": [], + "updated_at": "2020-10-15T07:26:16Z", + "url": "https://github.com/hassan11196/artist-1page", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/artist-1page" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/awesome-nlp" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 386, + "contributors_count": 89, + "contributors_top": [ + { + "contributions": 123, + "html_url": "https://github.com/NirantK", + "login": "NirantK" + }, + { + "contributions": 97, + "html_url": "https://github.com/keon", + "login": "keon" + }, + { + "contributions": 35, + "html_url": "https://github.com/dhruvapte26", + "login": "dhruvapte26" + }, + { + "contributions": 7, + "html_url": null, + "login": "Daniel Khashabi" + }, + { + "contributions": 6, + "html_url": "https://github.com/ronrest", + "login": "ronrest" + }, + { + "contributions": 5, + "html_url": "https://github.com/jd7h", + "login": "jd7h" + }, + { + "contributions": 5, + "html_url": "https://github.com/outpark", + "login": "outpark" + }, + { + "contributions": 3, + "html_url": "https://github.com/panaali", + "login": "panaali" + } + ], + "created_at": "2019-11-15T17:29:06Z", + "default_branch": "master", + "description": ":book: A curated list of resources dedicated to Natural Language Processing (NLP)", + "dirs_total_count": 1, + "files_root_entries": [ + { + "path": "CREDITS.md", + "size": 727, + "type": "file" + }, + { + "path": "LICENSE", + "size": 7047, + "type": "file" + }, + { + "path": "PULL_REQUEST_TEMPLATE.md", + "size": 917, + "type": "file" + }, + { + "path": "README-ZH-TW.md", + "size": 32423, + "type": "file" + }, + { + "path": "README.md", + "size": 36909, + "type": "file" + }, + { + "path": "contributing.md", + "size": 2404, + "type": "file" + }, + { + "path": "images", + "size": 0, + "type": "dir" + } + ], + "files_total_count": 7, + "first_commit_date_default_branch": "2015-12-01T11:11:33Z", + "forks": 0, + "has_ci": false, + "has_contributing": true, + "has_docker": false, + "has_docs": false, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/awesome-nlp", + "is_archived": false, + "is_fork": true, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [], + "last_commit_date_default_branch": "2019-10-24T14:45:07Z", + "last_commit_oid_default_branch": "7446bca8971477b8c003ea87a863d7f2d885a150", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "Creative Commons Zero v1.0 Universal", + "license_spdx": "CC0-1.0", + "name_with_owner": "hassan11196/awesome-nlp", + "owner": "hassan11196", + "parent_repo": "keon/awesome-nlp", + "parent_url": "https://github.com/keon/awesome-nlp", + "primary_language": null, + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2019-11-15T13:16:42Z", + "readme_length": 36897, + "readme_text": "# awesome-nlp \n\n[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)\n\nA curated list of resources dedicated to Natural Language Processing\n\n![Awesome NLP Logo](/images/logo.jpg)\n\nRead this in [English](./README.md), [Traditional Chinese](./README-ZH-TW.md) \n\n_Please read the [contribution guidelines](contributing.md) before contributing. Please add your favourite NLP resource by raising a [pull request](https://github.com/keonkim/awesome-nlp/pulls)_\n\n## Contents\n\n* [Research Summaries and Trends](#research-summaries-and-trends)\n* [Prominent NLP Research Labs](#prominent-nlp-research-labs)\n* [Tutorials](#tutorials)\n * [Reading Content](#reading-content)\n * [Videos and Courses](#videos-and-online-courses)\n * [Books](#books)\n* [Libraries](#libraries)\n * [Node.js](#user-content-node-js)\n * [Python](#user-content-python)\n * [C++](#user-content-c++)\n * [Java](#user-content-java)\n * [Kotlin](#user-content-kotlin)\n * [Scala](#user-content-scala)\n * [R](#user-content-r)\n * [Clojure](#user-content-clojure)\n * [Ruby](#user-content-ruby)\n * [Rust](#user-content-rust)\n* [Services](#services)\n* [Annotation Tools](#annotation-tools)\n* [Datasets](#datasets)\n* [NLP in Korean](#nlp-in-korean)\n* [NLP in Arabic](#nlp-in-arabic)\n* [NLP in Chinese](#nlp-in-chinese)\n* [NLP in German](#nlp-in-german)\n* [NLP in Spanish](#nlp-in-spanish)\n* [NLP in Indic Languages](#nlp-in-indic-languages)\n* [NLP in Thai](#nlp-in-thai)\n* [NLP in Danish](#nlp-in-danish)\n* [NLP in Vietnamese](#nlp-in-vietnamese)\n* [NLP for Dutch](#nlp-for-dutch)\n* [NLP in Indonesian](#nlp-in-indonesian)\n* [Other Languages](#other-languages)\n* [Credits](#credits)\n\n## Research Summaries and Trends\n\n* [NLP-Overview](https://nlpoverview.com/) is an up-to-date overview of deep learning techniques applied to NLP, including theory, implementations, applications, and state-of-the-art results. This is a great Deep NLP Introduction for researchers. \n* [NLP-Progress](https://nlpprogress.com/) tracks the progress in Natural Language Processing, including the datasets and the current state-of-the-art for the most common NLP tasks\n* [NLP's ImageNet moment has arrived](https://thegradient.pub/nlp-imagenet/)\n* [ACL 2018 Highlights: Understanding Representation and Evaluation in More Challenging Settings](http://ruder.io/acl-2018-highlights/)\n* [Four deep learning trends from ACL 2017. Part One: Linguistic Structure and Word Embeddings](https://www.abigailsee.com/2017/08/30/four-deep-learning-trends-from-acl-2017-part-1.html)\n* [Four deep learning trends from ACL 2017. Part Two: Interpretability and Attention](https://www.abigailsee.com/2017/08/30/four-deep-learning-trends-from-acl-2017-part-2.html)\n* [Highlights of EMNLP 2017: Exciting Datasets, Return of the Clusters, and More!](http://blog.aylien.com/highlights-emnlp-2017-exciting-datasets-return-clusters/)\n* [Deep Learning for Natural Language Processing (NLP): Advancements & Trends](https://tryolabs.com/blog/2017/12/12/deep-learning-for-nlp-advancements-and-trends-in-2017/?utm_campaign=Revue%20newsletter&utm_medium=Newsletter&utm_source=The%20Wild%20Week%20in%20AI)\n* [Survey of the State of the Art in Natural Language Generation](https://arxiv.org/abs/1703.09902)\n\n## Prominent NLP Research Labs\n[Back to Top](#contents)\n\n* [The Berkeley NLP Group](http://nlp.cs.berkeley.edu/index.shtml) - Notable contributions include a tool to reconstruct long dead languages, referenced [here](https://www.bbc.com/news/science-environment-21427896) and by taking corpora from 637 languages currently spoken in Asia and the Pacific and recreating their descendant.\n* [Language Technologies Institute, Carnegie Mellon University](http://www.cs.cmu.edu/~nasmith/nlp-cl.html) - Notable projects include [Avenue Project](http://www.cs.cmu.edu/~avenue/), a syntax driven machine translation system for endangered lnaguages like Quechua and Aymara and previously, [Noah's Ark](http://www.cs.cmu.edu/~ark/) which created [AQMAR](http://www.cs.cmu.edu/~ark/AQMAR/) to imporve NLP tools for Arabic.\n* [NLP research group, Columbia University](http://www1.cs.columbia.edu/nlp/index.cgi) - Responsible for creating BOLT ( interactive error handling for speech translation systems) and an un-named project to characterize laughter in dialogue.\n* [The Center or Language and Speech Processing, John Hopkins University](http://clsp.jhu.edu/) - Recently in the news for developing speech recognition software to create a diagnostic test or Parkinson's Disease, [here](https://www.clsp.jhu.edu/2019/03/27/speech-recognition-software-and-machine-learning-tools-are-being-used-to-create-diagnostic-test-for-parkinsons-disease/#.XNFqrIkzYdU).\n* [Computational Linguistics and Information Processing Group, University of Maryland](https://wiki.umiacs.umd.edu/clip/index.php/Main_Page) - Notable contributions include [Human-Computer Cooperation or Word-by-Word Question Answering](http://www.umiacs.umd.edu/~jbg/projects/IIS-1652666) and modeling development of phonetic representations. \n* [Penn Natural Language Processing, University of Pennsylvania](http://nlp.cis.upenn.edu/index.php)- Famous for creating the [Penn Treebank](http://www.cis.upenn.edu/~treebank/).\n* [The Stanford Nautral Language Processing Group](https://nlp.stanford.edu/)- One of the top NLP research labs in the world, notable for creating [Stanford CoreNLP](https://nlp.stanford.edu/software/corenlp.shtml) and their [coreference resolution system](https://nlp.stanford.edu/software/dcoref.shtml)\n\n\n## Tutorials\n[Back to Top](#contents)\n\n### Reading Content\n\nGeneral Machine Learning\n\n* [Machine Learning 101](https://docs.google.com/presentation/d/1kSuQyW5DTnkVaZEjGYCkfOxvzCqGEFzWBy4e9Uedd9k/edit?usp=sharing) from Google's Senior Creative Engineer explains Machine Learning for engineer's and executives alike\n* [AI Playbook](https://aiplaybook.a16z.com/) - a16z AI playbook is a great link to forward to your managers or content for your presentations\n* [Ruder's Blog](http://ruder.io/#open) by [Sebastian Ruder](https://twitter.com/seb_ruder) for commentary on the best of NLP Research\n\nIntroductions and Guides to NLP\n\n* [Understand & Implement Natural Language Processing](https://www.analyticsvidhya.com/blog/2017/01/ultimate-guide-to-understand-implement-natural-language-processing-codes-in-python/)\n* [NLP in Python](http://github.com/NirantK/nlp-python-deep-learning) - Collection of Github notebooks \n* [Natural Language Processing: An Introduction](https://academic.oup.com/jamia/article/18/5/544/829676) - Oxford\n* [Deep Learning for NLP with Pytorch](https://pytorch.org/tutorials/beginner/deep_learning_nlp_tutorial.html)\n* [Hands-On NLTK Tutorial](https://github.com/hb20007/hands-on-nltk-tutorial) - NLTK Tutorials, Jupyter notebooks \n\nBlogs and Newsletters\n\n* [Deep Learning, NLP, and Representations](https://colah.github.io/posts/2014-07-NLP-RNNs-Representations/)\n* [The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)](https://jalammar.github.io/illustrated-bert/) and [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/)\n* [Natural Language Processing](https://nlpers.blogspot.com/) by Hal Daumé III\n* [arXiv: Natural Language Processing (Almost) from Scratch](https://arxiv.org/pdf/1103.0398.pdf)\n* [Karpathy's The Unreasonable Effectiveness of Recurrent Neural Networks](https://karpathy.github.io/2015/05/21/rnn-effectiveness)\n* [Machine Learning Mastery: Deep Learning for Natural Language Processing](https://machinelearningmastery.com/category/natural-language-processing)\n\n### Videos and Online Courses\n[Back to Top](#contents)\n\n* [Intro to Artificial Intelligence](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271) - Udacity course which touches upon NLP as well\n* [Deep Natural Language Processing](https://github.com/oxford-cs-deepnlp-2017/lectures) - Lectures series from Oxford\n* [Deep Learning for Natural Language Processing (cs224-n)](https://web.stanford.edu/class/cs224n/) - Richard Socher and Christopher Manning's Stanford Course\n* [Neural Networks for NLP](http://phontron.com/class/nn4nlp2017/) - Carnegie Mellon Language Technology Institute there\n* [Deep NLP Course](https://github.com/yandexdataschool/nlp_course) by Yandex Data School, covering important ideas from text embedding to machine translation including sequence modeling, language models and so on. \n* [fast.ai Code-First Intro to Natural Langauge Processing](https://www.youtube.com/playlist?list=PLtmWHNX-gukKocXQOkQjuVxglSDYWsSh9) - This covers a blend of traditional NLP topics (including regex, SVD, naive bayes, tokenization) and recent neural network approaches (including RNNs, seq2seq, GRUs, and the Transformer), as well as addressing urgent ethical issues, such as bias and disinformation. \n\n### Books\n\n* [Speech and Language Processing](https://web.stanford.edu/~jurafsky/slp3/) - free, by Prof. Dan Jurafsy\n* [Natural Language Processing] - free, NLP notes by Dr. Jacob Eisenstein at GeorgiaTech \n* [NLP with PyTorch] - Brian & Delip Rao \n* [Text Mining in R](https://www.tidytextmining.com)\n* [Natural Language Processing with Python](https://www.nltk.org/book/)\n\n## Libraries\n\n[Back to Top](#contents)\n\n* **Node.js and Javascript** - Node.js Libaries for NLP | [Back to Top](#contents)\n * [Twitter-text](https://github.com/twitter/twitter-text) - A JavaScript implementation of Twitter's text processing library\n * [Knwl.js](https://github.com/benhmoore/Knwl.js) - A Natural Language Processor in JS\n * [Retext](https://github.com/retextjs/retext) - Extensible system for analyzing and manipulating natural language\n * [NLP Compromise](https://github.com/spencermountain/compromise) - Natural Language processing in the browser\n * [Natural](https://github.com/NaturalNode/natural) - general natural language facilities for node\n * [Poplar](https://github.com/synyi/poplar) - A web-based annotation tool for natural language processing (NLP)\n * [NLP.js](https://github.com/axa-group/nlp.js) - An NLP library for building bots\n\n* **Python** - Python NLP Libraries | [Back to Top](#contents)\n\n - [TextBlob](http://textblob.readthedocs.org/) - Providing a consistent API for diving into common natural language processing (NLP) tasks. Stands on the giant shoulders of [Natural Language Toolkit (NLTK)](https://www.nltk.org/) and [Pattern](https://github.com/clips/pattern), and plays nicely with both :+1:\n - [spaCy](https://github.com/explosion/spaCy) - Industrial strength NLP with Python and Cython :+1:\n - [textacy](https://github.com/chartbeat-labs/textacy) - Higher level NLP built on spaCy\n - [gensim](https://radimrehurek.com/gensim/index.html) - Python library to conduct unsupervised semantic modelling from plain text :+1:\n - [scattertext](https://github.com/JasonKessler/scattertext) - Python library to produce d3 visualizations of how language differs between corpora\n - [GluonNLP](https://github.com/dmlc/gluon-nlp) - A deep learning toolkit for NLP, built on MXNet/Gluon, for research prototyping and industrial deployment of state-of-the-art models on a wide range of NLP tasks.\n - [AllenNLP](https://github.com/allenai/allennlp) - An NLP research library, built on PyTorch, for developing state-of-the-art deep learning models on a wide variety of linguistic tasks.\n - [PyTorch-NLP](https://github.com/PetrochukM/PyTorch-NLP) - NLP research toolkit designed to support rapid prototyping with better data loaders, word vector loaders, neural network layer representations, common NLP metrics such as BLEU\n - [Rosetta](https://github.com/columbia-applied-data-science/rosetta) - Text processing tools and wrappers (e.g. Vowpal Wabbit)\n - [PyNLPl](https://github.com/proycon/pynlpl) - Python Natural Language Processing Library. General purpose NLP library for Python. Also contains some specific modules for parsing common NLP formats, most notably for [FoLiA](https://proycon.github.io/folia/), but also ARPA language models, Moses phrasetables, GIZA++ alignments.\n - [jPTDP](https://github.com/datquocnguyen/jPTDP) - A toolkit for joint part-of-speech (POS) tagging and dependency parsing. jPTDP provides pre-trained models for 40+ languages.\n - [BigARTM](https://github.com/bigartm/bigartm) - a fast library for topic modelling\n - [Snips NLU](https://github.com/snipsco/snips-nlu) - A production ready library for intent parsing\n - [Chazutsu](https://github.com/chakki-works/chazutsu) - A library for downloading&parsing standard NLP research datasets\n - [Word Forms](https://github.com/gutfeeling/word_forms) - Word forms can accurately generate all possible forms of an English word\n - [Multilingual Latent Dirichlet Allocation (LDA)](https://github.com/ArtificiAI/Multilingual-Latent-Dirichlet-Allocation-LDA) - A multilingual and extensible document clustering pipeline\n - [NLP Architect](https://github.com/NervanaSystems/nlp-architect) - A library for exploring the state-of-the-art deep learning topologies and techniques for NLP and NLU\n - [Flair](https://github.com/zalandoresearch/flair) - A very simple framework for state-of-the-art multilingual NLP built on PyTorch. Includes BERT, ELMo and Flair embeddings.\n - [Kashgari](https://github.com/BrikerMan/Kashgari) - Simple, Keras-powered multilingual NLP framework, allows you to build your models in 5 minutes for named entity recognition (NER), part-of-speech tagging (PoS) and text classification tasks. Includes BERT and word2vec embedding.\n - [FARM](https://github.com/deepset-ai/FARM) - FARM makes cutting-edge transfer learning simple and helps you to leverage pretrained language models for your own NLP tasks.\n\n- **C++** - C++ Libraries | [Back to Top](#contents)\n - [MIT Information Extraction Toolkit](https://github.com/mit-nlp/MITIE) - C, C++, and Python tools for named entity recognition and relation extraction\n - [CRF++](https://taku910.github.io/crfpp/) - Open source implementation of Conditional Random Fields (CRFs) for segmenting/labeling sequential data & other Natural Language Processing tasks.\n - [CRFsuite](http://www.chokkan.org/software/crfsuite/) - CRFsuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data.\n - [BLLIP Parser](https://github.com/BLLIP/bllip-parser) - BLLIP Natural Language Parser (also known as the Charniak-Johnson parser)\n - [colibri-core](https://github.com/proycon/colibri-core) - C++ library, command line tools, and Python binding for extracting and working with basic linguistic constructions such as n-grams and skipgrams in a quick and memory-efficient way.\n - [ucto](https://github.com/LanguageMachines/ucto) - Unicode-aware regular-expression based tokenizer for various languages. Tool and C++ library. Supports FoLiA format.\n - [libfolia](https://github.com/LanguageMachines/libfolia) - C++ library for the [FoLiA format](https://proycon.github.io/folia/)\n - [frog](https://github.com/LanguageMachines/frog) - Memory-based NLP suite developed for Dutch: PoS tagger, lemmatiser, dependency parser, NER, shallow parser, morphological analyzer.\n - [MeTA](https://github.com/meta-toolkit/meta) - [MeTA : ModErn Text Analysis](https://meta-toolkit.org/) is a C++ Data Sciences Toolkit that facilitates mining big text data.\n - [Mecab (Japanese)](https://taku910.github.io/mecab/)\n - [Moses](http://statmt.org/moses/)\n - [StarSpace](https://github.com/facebookresearch/StarSpace) - a library from Facebook for creating embeddings of word-level, paragraph-level, document-level and for text classification\n\n- **Java** - Java NLP Libraries | [Back to Top](#contents)\n - [Stanford NLP](https://nlp.stanford.edu/software/index.shtml)\n - [OpenNLP](https://opennlp.apache.org/)\n - [NLP4J](https://emorynlp.github.io/nlp4j/)\n - [Word2vec in Java](https://deeplearning4j.org/docs/latest/deeplearning4j-nlp-word2vec)\n - [ReVerb](https://github.com/knowitall/reverb/) Web-Scale Open Information Extraction\n - [OpenRegex](https://github.com/knowitall/openregex) An efficient and flexible token-based regular expression language and engine.\n - [CogcompNLP](https://github.com/CogComp/cogcomp-nlp) - Core libraries developed in the U of Illinois' Cognitive Computation Group.\n - [MALLET](http://mallet.cs.umass.edu/) - MAchine Learning for LanguagE Toolkit - package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text.\n - [RDRPOSTagger](https://github.com/datquocnguyen/RDRPOSTagger) - A robust POS tagging toolkit available (in both Java & Python) together with pre-trained models for 40+ languages.\n \n- **Kotlin** - Kotlin NLP Libraries | [Back to Top](#contents)\n - [Lingua](https://github.com/pemistahl/lingua/) A language detection library for Kotlin and Java, suitable for long and short text alike\n - [Kotidgy](https://github.com/meiblorn/kotidgy) — an index-based text data generator written in Kotlin\n \n- **Scala** - Scala NLP Libraries | [Back to Top](#contents)\n - [Saul](https://github.com/CogComp/saul) - Library for developing NLP systems, including built in modules like SRL, POS, etc.\n - [ATR4S](https://github.com/ispras/atr4s) - Toolkit with state-of-the-art [automatic term recognition](https://en.wikipedia.org/wiki/Terminology_extraction) methods.\n - [tm](https://github.com/ispras/tm) - Implementation of topic modeling based on regularized multilingual [PLSA](https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis).\n - [word2vec-scala](https://github.com/Refefer/word2vec-scala) - Scala interface to word2vec model; includes operations on vectors like word-distance and word-analogy.\n - [Epic](https://github.com/dlwh/epic) - Epic is a high performance statistical parser written in Scala, along with a framework for building complex structured prediction models.\n\n- **R** - R NLP Libraries | [Back to Top](#contents)\n - [text2vec](https://github.com/dselivanov/text2vec) - Fast vectorization, topic modeling, distances and GloVe word embeddings in R.\n - [wordVectors](https://github.com/bmschmidt/wordVectors) - An R package for creating and exploring word2vec and other word embedding models\n - [RMallet](https://github.com/mimno/RMallet) - R package to interface with the Java machine learning tool MALLET\n - [dfr-browser](https://github.com/agoldst/dfr-browser) - Creates d3 visualizations for browsing topic models of text in a web browser.\n - [dfrtopics](https://github.com/agoldst/dfrtopics) - R package for exploring topic models of text.\n - [sentiment_classifier](https://github.com/kevincobain2000/sentiment_classifier) - Sentiment Classification using Word Sense Disambiguation and WordNet Reader\n - [jProcessing](https://github.com/kevincobain2000/jProcessing) - Japanese Natural Langauge Processing Libraries, with Japanese sentiment classification\n\n- **Clojure** | [Back to Top](#contents)\n - [Clojure-openNLP](https://github.com/dakrone/clojure-opennlp) - Natural Language Processing in Clojure (opennlp)\n - [Infections-clj](https://github.com/r0man/inflections-clj) - Rails-like inflection library for Clojure and ClojureScript\n - [postagga](https://github.com/fekr/postagga) - A library to parse natural language in Clojure and ClojureScript\n\n- **Ruby** | [Back to Top](#contents)\n - Kevin Dias's [A collection of Natural Language Processing (NLP) Ruby libraries, tools and software](https://github.com/diasks2/ruby-nlp)\n - [Practical Natural Language Processing done in Ruby](https://github.com/arbox/nlp-with-ruby)\n\n- **Rust**\n - [whatlang](https://github.com/greyblake/whatlang-rs) — Natural language recognition library based on trigrams\n - [snips-nlu-rs](https://github.com/snipsco/snips-nlu-rs) - A production ready library for intent parsing\n\n### Services\n\nNLP as API with higher level functionality such as NER, Topic tagging and so on | [Back to Top](#contents)\n\n- [Wit-ai](https://github.com/wit-ai/wit) - Natural Language Interface for apps and devices\n- [IBM Watson's Natural Language Understanding](https://github.com/watson-developer-cloud/natural-language-understanding-nodejs) - API and Github demo \n- [Amazon Comprehend](https://aws.amazon.com/comprehend/) - NLP and ML suite covers most common tasks like NER, tagging, and sentiment analysis\n- [Google Cloud Natural Language API](https://cloud.google.com/natural-language/) - Syntax Analysis, NER, Sentiment Analysis, and Content tagging in atleast 9 languages include English and Chinese (Simplified and Traditional).\n- [ParallelDots](https://www.paralleldots.com/text-analysis-apis) - High level Text Analysis API Service ranging from Sentiment Analysis to Intent Analysis\n- [Microsoft Cognitive Service](https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics/)\n- [TextRazor](https://www.textrazor.com/)\n- [Rosette](https://www.rosette.com/)\n- [Textalytic](https://www.textalytic.com) - Natural Language Processing in the Browser with sentiment analysis, named entity extraction, POS tagging, word frequencies, topic modeling, word clouds, and more\n\n### Annotation Tools\n\n- [GATE](https://gate.ac.uk/overview.html) - General Architecture and Text Engineering is 15+ years old, free and open source\n- [Anafora](https://github.com/weitechen/anafora) is free and open source, web-based raw text annotation tool\n- [brat](https://brat.nlplab.org/) - brat rapid annotation tool is an online environment for collaborative text annotation\n- [doccano](https://github.com/chakki-works/doccano) - doccano is free, open-source, and provides annotation features for text classification, sequence labeling and sequence to sequence\n- [tagtog](https://www.tagtog.net/), team-first web tool to find, create, maintain, and share datasets - costs $\n- [prodigy](https://prodi.gy/) is an annotation tool powered by active learning, costs $\n- [LightTag](https://lighttag.io) - Hosted and managed text annotation tool for teams, costs $\n- [rstWeb](https://corpling.uis.georgetown.edu/rstweb/info/) - open source local or online tool for discourse tree annotations\n- [GitDox](https://corpling.uis.georgetown.edu/gitdox/) - open source server annotation tool with GitHub version control and validation for XML data and collaborative spreadsheet grids\n\n## Techniques\n\n### Text Embeddings\n\n#### Word Embeddings\n\n- Thumb Rule: **fastText >> GloVe > word2vec**\n\n- [word2vec](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf) - [implementation](https://code.google.com/archive/p/word2vec/) - [explainer blog](http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/)\n- [glove](https://nlp.stanford.edu/pubs/glove.pdf) - [explainer blog](https://blog.acolyer.org/2016/04/22/glove-global-vectors-for-word-representation/)\n- fasttext - [implementation](https://github.com/facebookresearch/fastText) - [paper](https://arxiv.org/abs/1607.04606) - [explainer blog](https://towardsdatascience.com/fasttext-under-the-hood-11efc57b2b3)\n\n#### Sentence and Language Model Based Word Embeddings\n\n[Back to Top](#contents)\n\n- ElMo - [Deep Contextualized Word Represenations](https://arxiv.org/abs/1802.05365) - [PyTorch implmentation](https://github.com/allenai/allennlp/blob/master/tutorials/how_to/elmo.md) - [TF Implementation](https://github.com/allenai/bilm-tf)\n- ULimFit - [Universal Language Model Fine-tuning for Text Classification](https://arxiv.org/abs/1801.06146) by Jeremy Howard and Sebastian Ruder\n- InferSent - [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://arxiv.org/abs/1705.02364) by facebook\n- CoVe - [Learned in Translation: Contextualized Word Vectors](https://arxiv.org/abs/1708.00107)\n- Pargraph vectors - from [Distributed Representations of Sentences and Documents](https://cs.stanford.edu/~quocle/paragraph_vector.pdf). See [doc2vec tutorial at gensim](https://rare-technologies.com/doc2vec-tutorial/)\n- [sense2vec](https://arxiv.org/abs/1511.06388) - on word sense disambiguation\n- [Skip Thought Vectors](https://arxiv.org/abs/1506.06726) - word representation method\n- [Adaptive skip-gram](https://arxiv.org/abs/1502.07257) - similar approach, with adaptive properties\n- [Sequence to Sequence Learning](https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf) - word vectors for machine translation\n\n### Question Answering and Knowledge Extraction\n\n[Back to Top](#contents)\n\n- [DrQA](https://github.com/facebookresearch/DrQA) - Open Domain Question Answering work by Facebook Research on Wikipedia data\n- [Document-QA](https://github.com/allenai/document-qa) - Simple and Effective Multi-Paragraph Reading Comprehension by AllenAI\n- [Template-Based Information Extraction without the Templates](https://www.usna.edu/Users/cs/nchamber/pubs/acl2011-chambers-templates.pdf)\n- [Privee: An Architecture for Automatically Analyzing Web Privacy Policies](https://www.sebastianzimmeck.de/zimmeckAndBellovin2014Privee.pdf)\n\n## Datasets\n\n[Back to Top](#contents)\n\n- [nlp-datasets](https://github.com/niderhoff/nlp-datasets) great collection of nlp datasets\n\n## Multilingual NLP Frameworks\n\n[Back to Top](#contents)\n\n- [UDPipe](https://github.com/ufal/udpipe) is a trainable pipeline for tokenizing, tagging, lemmatizing and parsing Universal Treebanks and other CoNLL-U files. Primarily written in C++, offers a fast and reliable solution for multilingual NLP processing.\n- [NLP-Cube](https://github.com/adobe/NLP-Cube) : Natural Language Processing Pipeline - Sentence Splitting, Tokenization, Lemmatization, Part-of-speech Tagging and Dependency Parsing. New platform, written in Python with Dynet 2.0. Offers standalone (CLI/Python bindings) and server functionality (REST API).\n\n## NLP in Korean\n\n[Back to Top](#contents)\n\n### Libraries\n\n- [KoNLPy](http://konlpy.org) - Python package for Korean natural language processing.\n- [Mecab (Korean)](https://eunjeon.blogspot.com/) - C++ library for Korean NLP\n- [KoalaNLP](https://koalanlp.github.io/koalanlp/) - Scala library for Korean Natural Language Processing.\n- [KoNLP](https://cran.r-project.org/package=KoNLP) - R package for Korean Natural language processing\n\n### Blogs and Tutorials\n\n- [dsindex's blog](https://dsindex.github.io/)\n- [Kangwon University's NLP course in Korean](http://cs.kangwon.ac.kr/~leeck/NLP/)\n\n### Datasets\n\n- [KAIST Corpus](http://semanticweb.kaist.ac.kr/home/index.php/KAIST_Corpus) - A corpus from the Korea Advanced Institute of Science and Technology in Korean.\n- [Naver Sentiment Movie Corpus in Korean](https://github.com/e9t/nsmc/)\n- [Chosun Ilbo archive](http://srchdb1.chosun.com/pdf/i_archive/) - dataset in Korean from one of the major newspapers in South Korea, the Chosun Ilbo.\n\n## NLP in Arabic\n\n[Back to Top](#contents)\n\n### Libraries\n\n- [goarabic](https://github.com/01walid/goarabic) - Go package for Arabic text processing\n- [jsastem](https://github.com/ejtaal/jsastem) - Javascript for Arabic stemming\n- [PyArabic](https://pypi.org/project/PyArabic/) - Python libraries for Arabic\n- [RFTokenizer](https://github.com/amir-zeldes/RFTokenizer) - trainable Python segmenter for Arabic, Hebrew and Coptic\n\n### Datasets\n\n- [Multidomain Datasets](https://github.com/hadyelsahar/large-arabic-sentiment-analysis-resouces) - Largest Available Multi-Domain Resources for Arabic Sentiment Analysis\n- [LABR](https://github.com/mohamedadaly/labr) - LArge Arabic Book Reviews dataset\n- [Arabic Stopwords](https://github.com/mohataher/arabic-stop-words) - A list of Arabic stopwords from various resources\n\n## NLP in Chinese\n\n[Back to Top](#contents)\n\n### Libraries\n\n- [jieba](https://github.com/fxsjy/jieba#jieba-1) - Python package for Words Segmentation Utilities in Chinese\n- [SnowNLP](https://github.com/isnowfy/snownlp) - Python package for Chinese NLP\n- [FudanNLP](https://github.com/FudanNLP/fnlp) - Java library for Chinese text processing\n\n## NLP in German\n\n- [German-NLP](https://github.com/adbar/German-NLP) - Curated list of open-access/open-source/off-the-shelf resources and tools developed with a particular focus on German\n \n## NLP in Spanish\n\n[Back to Top](#contents)\n\n### Data\n\n- [Columbian Political Speeches](https://github.com/dav009/LatinamericanTextResources)\n- [Copenhagen Treebank](https://mbkromann.github.io/copenhagen-dependency-treebank/)\n- [Spanish Billion words corpus with Word2Vec embeddings](https://github.com/crscardellino/sbwce)\n- [Compilation of Spanish Unannotated Corpora](https://github.com/josecannete/spanish-unannotated-corpora)\n\n### Word and Sentence Embeddings\n- [Spanish Word Embeddings Computed with Different Methods and from Different Corpora](https://github.com/dccuchile/spanish-word-embeddings)\n- [Spanish Word Embeddings Computed from Large Corpora and Different Sizes Using fastText](https://github.com/BotCenter/spanishWordEmbeddings)\n- [Spanish Sentence Embeddings Computed from Large Corpora Using sent2vec](https://github.com/BotCenter/spanishSent2Vec)\n\n\n## NLP in Indic languages\n\n[Back to Top](#contents)\n\n### Data, Corpora and Treebanks\n\n- [Hindi Dependency Treebank](https://ltrc.iiit.ac.in/treebank_H2014/) - A multi-representational multi-layered treebank for Hindi and Urdu\n- [Universal Dependencies Treebank in Hindi](https://universaldependencies.org/treebanks/hi_hdtb/index.html)\n - [Parallel Universal Dependencies Treebank in Hindi](http://universaldependencies.org/treebanks/hi_pud/index.html) - A smaller part of the above-mentioned treebank.\n- [ISI FIRE Stopwords List (Hindi and Bangla)](https://www.isical.ac.in/~fire/data/)\n- [Peter Graham's Stopwords List](https://github.com/6/stopwords-json)\n- [NLTK Corpus](https://www.nltk.org/book/ch02.html) 60k Words POS Tagged, Bangla, Hindi, Marathi, Telugu\n- [Hindi Movie Reviews Dataset](https://github.com/goru001/nlp-for-hindi) ~1k Samples, 3 polarity classes\n- [BBC News Hindi Dataset](https://github.com/NirantK/hindi2vec/releases/tag/bbc-hindi-v0.1) 4.3k Samples, 14 classes\n- [IIT Patna Hindi ABSA Dataset](https://github.com/pnisarg/ABSA) 5.4k Samples, 12 Domains, 4k aspect terms, aspect and sentence level polarity in 4 classes\n- [Bangla ABSA](https://github.com/AtikRahman/Bangla_Datasets_ABSA) 5.5k Samples, 2 Domains, 10 aspect terms\n- [IIT Patna Movie Review Sentiment Dataset](https://www.iitp.ac.in/~ai-nlp-ml/resources.html) 2k Samples, 3 polarity labels\n\n#### Corpora/Datasets that need a login/access can be gained via email\n\n- [SAIL 2015](http://amitavadas.com/SAIL/) Twitter and Facebook labelled sentiment samples in Hindi, Bengali, Tamil, Telugu.\n- [IIT Bombay NLP Resources](http://www.cfilt.iitb.ac.in/Sentiment_Analysis_Resources.html) Sentiwordnet, Movie and Tourism parallel labelled corpora, polarity labelled sense annotated corpus, Marathi polarity labelled corpus.\n- [TDIL-IC aggregates a lot of useful resources and provides access to otherwise gated datasets](https://tdil-dc.in/index.php?option=com_catalogue&task=viewTools&id=83&lang=en)\n\n### Language Models and Word Embeddings\n\n- [Hindi2Vec](https://nirantk.com/hindi2vec/) and [nlp-for-hindi](https://github.com/goru001/nlp-for-hindi) ULMFIT style languge model\n- [IIT Patna Bilingual Word Embeddings Hi-En](https://www.iitp.ac.in/~ai-nlp-ml/resources.html)\n- [Fasttext word embeddings in a whole bunch of languages, trained on Common Crawl](https://fasttext.cc/docs/en/crawl-vectors.html)\n- [Hindi and Bengali Word2Vec](https://github.com/Kyubyong/wordvectors)\n- [Hindi and Urdu Elmo Model](https://github.com/HIT-SCIR/ELMoForManyLangs)\n\n### Libraries and Tooling\n\n- [Multi-Task Deep Morphological Analyzer](https://github.com/Saurav0074/mt-dma) Deep Network based Morphological Parser for Hindi and Urdu\n- [Anoop Kunchukuttan](https://github.com/anoopkunchukuttan/indic_nlp_library) 18 Languages, whole host of features from tokenization to translation\n- [SivaReddy's Dependency Parser](http://sivareddy.in/downloads) Dependency Parser and Pos Tagger for Kannada, Hindi and Telugu. [Python3 Port](https://github.com/CalmDownKarm/sivareddydependencyparser)\n- [iNLTK](https://github.com/goru001/inltk) - A Natural Language Toolkit for Indic Languages (Indian subcontinent languages) built on top of Pytorch/Fastai, which aims to provide out of the box support for common NLP tasks.\n\n## NLP in Thai\n\n[Back to Top](#contents)\n\n### Libraries\n\n- [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp) - Thai NLP in Python Package\n- [JTCC](https://github.com/wittawatj/jtcc) - A character cluster library in Java\n- [CutKum](https://github.com/pucktada/cutkum) - Word segmentation with deep learning in TensorFlow\n- [Thai Language Toolkit](https://pypi.python.org/pypi/tltk/) - Based on a paper by Wirote Aroonmanakun in 2002 with included dataset\n- [SynThai](https://github.com/KenjiroAI/SynThai) - Word segmentation and POS tagging using deep learning in Python\n\n### Data\n\n- [Inter-BEST](https://www.nectec.or.th/corpus/index.php?league=pm) - A text corpus with 5 million words with word segmentation\n- [Prime Minister 29](https://github.com/PyThaiNLP/lexicon-thai/tree/master/thai-corpus/Prime%20Minister%2029) - Dataset containing speeches of the current Prime Minister of Thailand\n\n## NLP in Danish \n\n- [Named Entity Recognition for Danish](https://github.com/ITUnlp/daner)\n- [DaNLP](https://github.com/alexandrainst/danlp) - NLP resources in Danish\n\n## NLP in Vietnamese \n\n### Libraries\n\n- [underthesea](https://github.com/undertheseanlp/underthesea) - Vietnamese NLP Toolkit\n- [vn.vitk](https://github.com/phuonglh/vn.vitk) - A Vietnamese Text Processing Toolkit\n- [VnCoreNLP](https://github.com/vncorenlp/VnCoreNLP) - A Vietnamese natural language processing toolkit\n\n### Data\n\n- [Vietnamese treebank](https://vlsp.hpda.vn/demo/?page=resources&lang=en) - 10,000 sentences for the constituency parsing task\n- [BKTreeBank](https://arxiv.org/pdf/1710.05519.pdf) - a Vietnamese Dependency Treebank\n- [UD_Vietnamese](https://github.com/UniversalDependencies/UD_Vietnamese-VTB) - Vietnamese Universal Dependency Treebank\n- [VIVOS](https://ailab.hcmus.edu.vn/vivos/) - a free Vietnamese speech corpus consisting of 15 hours of recording speech by AILab\n- [VNTQcorpus(big).txt](http://viet.jnlp.org/download-du-lieu-tu-vung-corpus) - 1.75 million sentences in news\n\n## NLP for Dutch\n\n[Back to Top](#contents)\n\n- [python-frog](https://github.com/proycon/python-frog) - Python binding to Frog, an NLP suite for Dutch. (pos tagging, lemmatisation, dependency parsing, NER)\n- [SimpleNLG_NL](https://github.com/rfdj/SimpleNLG-NL) - Dutch surface realiser used for Natural Language Generation in Dutch, based on the SimpleNLG implementation for English and French.\n\n## NLP in Indonesian \n\n### Datasets\n- Kompas and Tempo collections at [ILPS](http://ilps.science.uva.nl/resources/bahasa/)\n- [PANL10N for PoS tagging](http://www.panl10n.net/english/outputs/Indonesia/UI/0802/UI-1M-tagged.zip): 39K sentences and 900K word tokens\n- [IDN for PoS tagging](https://github.com/famrashel/idn-tagged-corpus): This corpus contains 10K sentences and 250K word tokens\n- [Indonesian Treebank](https://github.com/famrashel/idn-treebank) and [Universal Dependencies-Indonesian](https://github.com/UniversalDependencies/UD_Indonesian-GSD)\n- [IndoSum](https://github.com/kata-ai/indosum) for text summarization and classification both\n- [Wordnet-Bahasa](http://wn-msa.sourceforge.net/) - large, free, semantic dictionary\n\n### 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Status](https://app.fossa.io/api/projects/git%2Bgithub.com%2FModusCreateOrg%2Fbudgeting-sample-app-webpack2.svg?type=shield)](https://app.fossa.io/projects/git%2Bgithub.com%2FModusCreateOrg%2Fbudgeting-sample-app-webpack2?ref=badge_shield)\n[![CircleCI](https://circleci.com/gh/ModusCreateOrg/budgeting.svg?style=svg)](https://circleci.com/gh/ModusCreateOrg/budgeting)\n[![Coverage Status](https://coveralls.io/repos/github/ModusCreateOrg/budgeting/badge.svg)](https://coveralls.io/github/ModusCreateOrg/budgeting)\n[![Maintainability](https://api.codeclimate.com/v1/badges/22f9a5e29c1c4df3d3bf/maintainability)](https://codeclimate.com/github/ModusCreateOrg/budgeting/maintainability)\n[![Powered by Modus_Create](https://img.shields.io/badge/powered_by-Modus_Create-blue.svg?longCache=true&style=flat&logo=data:image/svg+xml;base64,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)](https://moduscreate.com)\n\n# Budgeting :: A Modern React, Redux, React Router 4, Webpack Sample App\n\n![React, Redux, Router, Webpack, Sass](https://cloud.githubusercontent.com/assets/733074/25338311/193a1a40-28ff-11e7-8f22-9a5d9dac7b84.png)\n\nProduction-ready React + Webpack architecture implemented on consumer web apps of some of the most successful enterprises in the world. Perceived performance and development experience are key factors in this setup. You can use this code base for learning or to scaffold your mission-critical project.\n\n[See live demo](https://budget.modus.app/).\n\n![React Budgeting App](https://cloud.githubusercontent.com/assets/733074/25340900/6ab1d536-2907-11e7-8083-b78f8ae601b4.png)\n\n## Budgeting Application\n\nThis is a simple budget management application. It tracks inflow and outflow, shows remaining budget, and interesting reports with charts. As such, it offers more features than the usual Todo App.\n\nBudgeting app is a showcase project that demonstrates important decisions in architecture and development of a modern React application.\n\nFeel free to use it as a reference app or a starter kit.\n\n## Key concepts:\n\n- [x] [Webpack 4 Tree shaking](http://moduscreate.com/webpack-2-tree-shaking-configuration/)\n- [x] [PRPL pattern](https://www.slideshare.net/grgur/prpl-pattern-with-webpack-and-react) with minimal application core\n- [x] Automatic code splitting with React Router 4 and `import()` statement\n- [x] Automatic common chunk bundling\n- [x] CSS modules\n- [x] Snapshot testing with Jest\n- [x] Flow static typing. Check out this [guide to using Flow in the project](https://github.com/ModusCreateOrg/budgeting-sample-app-webpack2/blob/master/docs/flow.md).\n- [x] Performance budgets in Webpack 3\n- [x] React 16 Error Boundaries\n\n## Performance\n\n![Budgeting App Performance](https://cloud.githubusercontent.com/assets/733074/25339194/1af94448-2902-11e7-8982-c1a9b647fac0.png)\n_The app loads in 1 second on 3G, cache disabled_\n\nBudgeting app is **blazing fast**, thanks to the smart architecture and Webpack 3 configuration. It takes about 1000ms (1s) to load on 3G (see above).\n\n![Alex Russel Test](https://cloud.githubusercontent.com/assets/733074/25586449/acf14628-2e9f-11e7-8839-2f7c20809581.png)\n_Emerging Markets 3G Filmstrip_\n\nThe [aggressive test](https://www.webpagetest.org/video/compare.php?tests=170501_0S_XQ5-r:2-c:0) above shows the budgeting app loads in under 5 seconds. It's a heavily limited connection that accounts for poor connectivity and limited bandwidth.\n\n![Waterfall](https://cloud.githubusercontent.com/assets/733074/25586623/676a378a-2ea0-11e7-9342-c040751b6ec6.png)\n\nAll important (aka critical path) assets are loaded as early as possible, while the others (e.g. images or GitHub buttons) will load after the first render.\n\n#### How did we get that performance?\n\n1. **Minimal application core.** We decided to ditch the usual convention of creating a vendor chunk. Instead, it's bundled in the app core. The app core is actually very small, containing just the code needed to bootstrap the app.\n2. **Common code is a chunk.** We let Webpack figure out which bundles we reuse in chunks and create a common chunk that's also asyncronous.\n3. **Redux module injection**. Each chunk contains respective views _and_ redux modules. Yes, that means reducers, action creators, actions - are all dynamically injected as we navigate through routes. That adds to the _minimal application core_ concept and PRPL pattern.\n4. **H2 Push.** The app is hosted on Firebase and we use the magic of _HTTP2 Push_ to push some of the scripts before they are requested.\n5. **Pre-caching**. Service Workers pre-cache resources so the browser can access them as soon as the user needs to.\n\n## Charts\n\nCharts are developed using the awesome D3 library. The idea behind showing charts is not only to show beautiful content, but also to demonstrate keeping heavy content in a chunk that owns it. In other words - we show how applications can run fast even if they use larger libraries.\n\nD3 is used in the `/reports` route only. Given that major routes are separate chunks (code splitting FTW!), the entire D3 library is bundled with the code that needs it. That makes the `/reports` route a bit heavier than the initial `/budget` route, but it also makes routes much faster to load.\n\n## Performance Budgets\n\nWe are looking to maintain the lightest possible application core (_aka entry chunk_). Our target is 300kB for the entrypoint and 300kB for all other assets. This is how we set it in [webpack configuration](https://github.com/ModusCreateOrg/budgeting-sample-app-webpack2/blob/master/webpack.config.js):\n\n```js\nperformance: {\n maxAssetSize: 300000,\n maxEntrypointSize: 300000,\n hints: 'warning',\n},\n```\n\nAdding lots of extra code to the entry chunk might cause the build (`npm run build`) process to show a warning.\n\n![Performance Budgets](https://cloud.githubusercontent.com/assets/733074/25352700/3ade5cfa-292d-11e7-8d2e-fed88c2c4da0.png)\n\n_Simulated size warning_\n\nNote that running webpack dev server in production mode (`npm run prod`) will trigger this warning because of the additional dev server code injected in the app. This code will not show in regular production builds.\n\n## Service Workers\n\nService workers are enabled only when serving static files, not through webpack-dev-server. Here's how you can test service worker functionality:\n\n1. Run `npm run build` to build the app\n2. Run `npm run prod` to serve the app on [localhost:3000](http://localhost:3000)\n3. Run a new instance of Chrome with disabled security (because localhost is not on https):\n\n**OS X**\n\n```bash\nopen -a \"Google Chrome\" --args --user-data-dir=/tmp/unsafe --unsafely-treat-insecure-origin-as-secure=http://localhost\n```\n\n**Linux**\n\n```bash\n/path/to/chrome --user-data-dir=/tmp/unsafe --unsafely-treat-insecure-origin-as-secure=http://localhost\n```\n\n**Windows**\n\n```bash\nchrome.exe --user-data-dir=c:\\temp --unsafely-treat-insecure-origin-as-secure=http://localhost\n```\n\n4. Now you can observe network traffic in the Network tab or SW activity in Application > Service Workers in Developer Tools\n\n## Stack\n\nThe app was built using these aweseome technologies\n\n- [x] [Webpack 4](https://webpack.js.org)\n- [x] [React 16.x](https://facebook.github.io/react/)\n- [x] [Redux 3.x](http://redux.js.org/)\n- [x] [React Router 4](https://reacttraining.com/react-router/)\n- [x] [Reselect](https://github.com/reactjs/reselect/)\n- [x] [Babel](https://babeljs.io/)\n- [x] [Prettier](https://github.com/prettier/prettier)\n- [x] [Jest](https://facebook.github.io/jest/)\n- [x] [Flow](https://flow.org/en/)\n- [x] [Ducks](https://github.com/erikras/ducks-modular-redux/) 🐣\n- [x] [Sass](http://sass-lang.com/)\n- [x] [Autoprefixer](https://github.com/postcss/autoprefixer)\n- [x] [D3 5.x](https://d3js.org/)\n\n## NPM Scripts\n\n- `npm install` - install dependencies\n- `npm start` - run development server\n- `npm run prod` - run production server\n- `npm run build` - build app for deployment\n- `npm run serve` - serve previously built app using pushstate server\n- `npm run lint` - lint check\n- `npm run lint:fix` - lint check + autofixes + prettify code with **prettier**\n- `npm test` - run test suite\n- `npm run test:fix` - run test suite watching files for changes\n- `npm run flow` - run flow type checking\n- `npm run update-types` - update flow library definitions\n\n## Honorary Mentions\n\n- Thanks to [React experts at Modus Create](https://moduscreate.com), particularly [Tim Eagan](https://twitter.com/TimothyEagan), [Jason Malfatto](https://twitter.com/jmalfatto), [Brice Mason](https://twitter.com/bricemason), and [Esteban Las](https://twitter.com/elas78) for infinite amounts of experience poured into this app\n- Kudos to [Andrea Grisogono](https://twitter.com/scrumolina) who Scrumorganized the team\n- Thanks to community contributors who helped with code and screamed about issues. Yeah, we really do appreciate all the screaming.\n- [Addy Osmani](https://twitter.com/addyosmani) and [Sam Saccone](https://twitter.com/samccone) who helped with the PRPL pattern\n- [Sean T Larkin](https://twitter.com/thelarkinn) who helped with Webpack wizardry\n\n## Want more?\n\nThis project is maintained by [Modus Create](https://moduscreate.com). Fantastic React apps are in our DNA so give us a buzz if we can help with your awesome project.\n\n## License\n\n[MIT](License.md)\n\n[![FOSSA Status](https://app.fossa.io/api/projects/git%2Bgithub.com%2FModusCreateOrg%2Fbudgeting-sample-app-webpack2.svg?type=large)](https://app.fossa.io/projects/git%2Bgithub.com%2FModusCreateOrg%2Fbudgeting-sample-app-webpack2?ref=badge_large)", + "readme_title": "Budgeting :: A Modern React, Redux, React Router 4, Webpack Sample App", + "releases_count": 0, + "repo": "budgeting", + "repo_name": "budgeting", + "stars": 0, + "topics": [], + "updated_at": "2019-11-09T22:02:23Z", + "url": "https://github.com/hassan11196/budgeting", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/budgeting" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/cocoapi" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 219, + "contributors_count": 16, + "contributors_top": [ + { + "contributions": 113, + "html_url": "https://github.com/pdollar", + "login": "pdollar" + }, + { + "contributions": 73, + "html_url": "https://github.com/tylin", + "login": "tylin" + }, + { + "contributions": 16, + "html_url": "https://github.com/ppwwyyxx", + "login": "ppwwyyxx" + }, + { + "contributions": 3, + "html_url": null, + "login": "pdollar" + }, + { + "contributions": 2, + "html_url": null, + "login": "Roman Donchenko" + }, + { + "contributions": 2, + "html_url": "https://github.com/yassersouri", + "login": "yassersouri" + }, + { + "contributions": 1, + "html_url": "https://github.com/Johnqczhang", + "login": "Johnqczhang" + }, + { + "contributions": 1, + "html_url": "https://github.com/mohomran", + "login": "mohomran" + } + ], + "created_at": "2022-06-26T01:19:10Z", + "default_branch": "master", + "description": "Contains the \"pycocotools\" package on PyPI. Changes made to the official cocoapi about packaging.", + "dirs_total_count": 10, + "files_root_entries": [ + { + "path": ".circleci", + "size": 0, + "type": "dir" + }, + { + "path": ".gitignore", + "size": 336, + "type": "file" + }, + { + "path": "LuaAPI", + "size": 0, + "type": "dir" + }, + { + "path": "MatlabAPI", + "size": 0, + "type": "dir" + }, + { + "path": "PythonAPI", + "size": 0, + "type": "dir" + }, + { + "path": "README.md", + "size": 464, + "type": "file" + }, + { + "path": "common", + "size": 0, + "type": "dir" + }, + { + "path": "license.txt", + "size": 1533, + "type": "file" + }, + { + "path": "results", + "size": 0, + "type": "dir" + }, + { + "path": "tests", + "size": 0, + "type": "dir" + } + ], + "files_total_count": 44, + "first_commit_date_default_branch": "2014-12-06T19:13:51Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": true, + "homepage_url": "https://pypi.org/project/pycocotools/", + "input_url": "https://github.com/hassan11196/cocoapi", + "is_archived": false, + "is_fork": true, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Jupyter Notebook", + "size": 1794764 + }, + { + "language": "MATLAB", + "size": 65356 + }, + { + "language": "Python", + "size": 50614 + }, + { + "language": "Lua", + "size": 23428 + }, + { + "language": "C++", + "size": 22250 + }, + { + "language": "C", + "size": 16567 + }, + { + "language": "Cython", + "size": 11477 + }, + { + "language": "Makefile", + "size": 217 + } + ], + "last_commit_date_default_branch": "2022-03-17T08:47:01Z", + "last_commit_oid_default_branch": "245dbb08794b584a38a80f86b9cc9b31a6cf66d5", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "Other", + "license_spdx": "NOASSERTION", + "name_with_owner": "hassan11196/cocoapi", + "owner": "hassan11196", + "parent_repo": "ppwwyyxx/cocoapi", + "parent_url": "https://github.com/ppwwyyxx/cocoapi", + "primary_language": "Jupyter Notebook", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2022-06-10T12:02:47Z", + "readme_length": 462, + "readme_text": "# pycocotools\n\nThis is the source of the pypi package pycocotools, available for download at [pypi](https://pypi.org/project/pycocotools/).\nIt is a fork of the original [cocoapi](https://github.com/cocodataset/cocoapi), with some small fixes and packaging improvements:\n\n* Add CircleCI tests\n* Correct dependencies so it's pip-installable\n* Support windows\n* Don't import matplotlib unless needed\n* Close file handle after openning\n* Fix a small bug in rleToBbox", + "readme_title": "pycocotools", + "releases_count": 0, + "repo": "cocoapi", + "repo_name": "cocoapi", + "stars": 0, + "topics": [], + "updated_at": "2022-06-09T09:08:17Z", + "url": "https://github.com/hassan11196/cocoapi", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/cocoapi" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/cocosplit" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 13, + "contributors_count": 2, + "contributors_top": [ + { + "contributions": 7, + "html_url": "https://github.com/ahmad-ra", + "login": "ahmad-ra" + }, + { + "contributions": 6, + "html_url": "https://github.com/akarazniewicz", + "login": "akarazniewicz" + } + ], + "created_at": "2022-06-12T02:09:52Z", + "default_branch": "master", + "description": "Simple tool to split COCO annotations into train/test datasets.", + "dirs_total_count": 0, + "files_root_entries": [ + { + "path": "README.md", + "size": 1919, + "type": "file" + }, + { + "path": "cocosplit.py", + "size": 4034, + "type": "file" + }, + { + "path": "requirements.txt", + "size": 41, + "type": "file" + } + ], + "files_total_count": 3, + "first_commit_date_default_branch": "2019-09-06T13:13:41Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/cocosplit", + "is_archived": false, + "is_fork": true, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Python", + "size": 4034 + } + ], + "last_commit_date_default_branch": "2022-04-16T03:49:27Z", + "last_commit_oid_default_branch": "4c8ddccee94f43f0c88451f06ea94fb2f82e208d", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/cocosplit", + "owner": "hassan11196", + "parent_repo": "akarazniewicz/cocosplit", + "parent_url": "https://github.com/akarazniewicz/cocosplit", + "primary_language": "Python", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2022-04-16T03:49:27Z", + "readme_length": 1918, + "readme_text": "Simple tool to split a multi-label coco annotation dataset with preserving class distributions among train and test sets.\n\nThe code is an updated version from [akarazniewicz/cocosplit](https://github.com/akarazniewicz/cocosplit) original repo, where the functionality of splitting multi-class data while preserving distributions is added.\n\n\n## Installation\n\n``cocosplit`` requires python 3 and basic set of dependencies:\n\nspecifically, in addition to the requirements of the original repo, (``scikit-multilearn``) is required, it is included the requirements.txt file\n\n```\npip install -r requirements\n```\n\n\n## Usage\n\nThe same as the original repo, with adding an argument (``--multi-class``) to preserve class distributions\nThe argument is optional to ensure backward compatibility\n\n```\n$ python cocosplit.py -h\nusage: cocosplit.py [-h] -s SPLIT [--having-annotations]\n coco_annotations train test\n\nSplits COCO annotations file into training and test sets.\n\npositional arguments:\n coco_annotations Path to COCO annotations file.\n train Where to store COCO training annotations\n test Where to store COCO test annotations\n\noptional arguments:\n -h, --help show this help message and exit\n -s SPLIT A percentage of a split; a number in (0, 1)\n --having-annotations Ignore all images without annotations. Keep only these\n with at least one annotation\n --multi-class Split a multi-class dataset while preserving class\n distributions in train and test sets\n```\n\n# Running\n\n```\n$ python cocosplit.py --having-annotations --multi-class -s 0.8 /path/to/your/coco_annotations.json train.json test.json\n```\n\nwill split ``coco_annotation.json`` into ``train.json`` and ``test.json`` with ratio 80%/20% respectively. It will skip all\nimages (``--having-annotations``) without annotations.", + "readme_title": "Running", + "releases_count": 0, + "repo": "cocosplit", + "repo_name": "cocosplit", + "stars": 0, + "topics": [], + "updated_at": "2022-06-06T08:06:48Z", + "url": "https://github.com/hassan11196/cocosplit", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/cocosplit" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/computer-networks-notes" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 8, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 8, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + } + ], + "created_at": "2020-06-29T07:37:13Z", + "default_branch": "master", + "description": "Computer Networks - A Top-Down Approach Notes", + "dirs_total_count": 1, + "files_root_entries": [ + { + "path": "README.md", + "size": 7926, + "type": "file" + }, + { + "path": "_config.yml", + "size": 281, + "type": "file" + }, + { + "path": "attachments", + "size": 0, + "type": "dir" + }, + { + "path": "index.md", + "size": 7968, + "type": "file" + } + ], + "files_total_count": 9, + "first_commit_date_default_branch": "2020-06-29T07:36:38Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": "https://hassan11196.github.io/computer-networks-notes/", + "input_url": "https://github.com/hassan11196/computer-networks-notes", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [], + "last_commit_date_default_branch": "2020-06-29T07:52:38Z", + "last_commit_oid_default_branch": "6393e11225bf05c4dc60da5d423c693499679beb", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/computer-networks-notes", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": null, + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2020-06-29T07:52:46Z", + "readme_length": 7894, + "readme_text": "---\nattachments: [Clipboard_2020-06-28-18-23-03.png, Clipboard_2020-06-28-20-56-31.png, Clipboard_2020-06-28-21-19-46.png, Clipboard_2020-06-28-21-20-47.png, Clipboard_2020-06-28-21-23-33.png, Clipboard_2020-06-28-21-46-15.png]\nfavorited: true\ntags: [CN]\ntitle: Computer Networks\ncreated: '2020-06-28T10:17:02.521Z'\nmodified: '2020-06-29T07:33:32.348Z'\n---\n\n# COMPUTER NETWORKING A Top-Down Approach\n\n## Chapter 1\n\n- 1.1.1 A Nuts-and-Bolts Description\n - End systems are connected together by a network of **communication links** and\n **packet switches**.\n\n - Cisco predicts annual global IP traffic will pass the zettabyte (&10^21& bytes) threshold by the end of 2016, and will reach 2 zettabytes per year by 2019 [Cisco\n VNI 2015].\n\n - End systems, packet switches, and other pieces of the Internet run **protocols** that\n control the sending and receiving of information within the Internet.\n\n- 1.1.3 What Is a Protocol?\nA **protocol** defines the format and the order of messages exchanged between two or more communicating entities, as well as the actions taken on the transmission and/or receipt of a message or other event.\n\n- 1.2.1\n - Digital Subscriber Line (**DSL**) modem uses the existing telephone line (twistedpair copper wire) to exchange data with a digital subscriber line access multiplexer (**DSLAM**) located in the telco’s local central office (**CO**).\n - On the customer side, a splitter separates the data and telephone signals arriving to the home and forwards the data signal to the **DSL** modem.\n - On the telco side, in the CO, the **DSLAM** separates the data and phone signals and sends the data into the Internet.\n - cable Internet access makes use of the cable television company’s infrastructure. Because both fiber and coaxial cable are employed in this system, it is often referred to as hybrid fiber coax(**HFC**)\n - the cable modem termination system (**CMTS**) serves a similar function as the DSL network’s DSLAM.\n - fiber to the home (**FTTH**) - the **FTTH** concept is simple—provide an optical fiber path from the CO directly to the home.\n - fiber is split near to the customer home with either active optical networks (**AONs**) or passive optical networks (**PONs**).\n - Each home has an optical network terminator (*ONT*), which is connected by dedicated optical fiber to a neighborhood splitter which connects to an optical line terminator (*OLT*) in the telco’s CO.\n\n- 1.2.2 Physical Media\n - Physical media fall into two categories: **guided media** and **unguided media**.\n - guided media, such as a fiber-optic cable, a twisted-pair copper wire, or a coaxial cable.\n - unguided media, such as in a wireless LAN or a digital satellite channel.\n\n- 1.3.1 Packet Switching\n - transmission rate is R &(bits)/sec&, if we send L bits time to transmit is &L/R& seconds.\n - **Store-and-forward transmission** means that the packet switch must receive the entire packet before it can begin to transmit the first bit of the packet onto the outbound link.\n - **routing** determines source destination route taken by packets.\n - **forwarding** move packets from routers input to appropiate route output.\n\n- 1.3.2 Circuit Switching\n - There are two fundamental approaches to moving data through a network of links and switches: **circuit switching** and **packet switching**\n - A circuit in a link is implemented with either **frequency-division multiplexing (FDM)** or **time-division multiplexing (TDM)**.\n - With **FDM**, each circuit continuously gets a fraction of the bandwidth. With **TDM**, each circuit gets all of the bandwidth periodically during brief intervals of time (that is, during slots)\n - Proponents of packet switching have always argued that circuit switching is wasteful because the dedicated circuits are idle during **silent periods**.\n\n- 1.3.3 A Network of Networks\n - A **PoP** is simply a group of one or more routers (at the same location) in the provider’s network where customer ISPs can connect into the provider ISP.\n - Any ISP(except Tier-1) may choose to **multi-home**, that is, to connect to two or more provider ISPs\n - A pair of ISP's can **peer**, that is, they can directly connect their networks together\n - A &3^(rd)& party can create an **Internet Exchange Point (IXP)**, which is a meeting point where multiple ISPs can peer together.\n\n- 1.4.1 Overview of Delay in Packet-Switched Networks\n - \n - &d_(nodal)=d_(proc)+d_(queue)+d_(trans)+d_(\"prop\")&\n - The time required to examine the packet’s header and determine where to direct the packet is part of the **processing delay**. \n - At the queue, the packet experiences a **queuing delay** as it waits to be transmitted onto the link.\n - The **transmission delay** is &L/R&. This is the amount of time required to push (that is, transmit) all of the packet’s bits into the link.\n - Once a bit is pushed into the link, it needs to propagate to router &B&. The time required to propagate from the beginning of the link to router &B& is the **propagation delay**, i.e, &d/s&, where &d& is the distance between router &A& and router &B& and &s& is the propagation speed of the link. \n - The **transmission delay** is the amount of time required for the router to push out the packet; it is a function of the packet’s length and the transmission rate of the link, but has nothing to do with the distance between the two routers. The **propagation delay**, on the other hand, is the time it takes a bit to propagate from one router to the next; it is a function of the distance between the two routers, but has nothing to do with the packet’s length or the transmission rate of the link.\n\n- 1.4.2 Queuing Delay and Packet Los\n - let &a& denote the average rate at which packets arrive at the queue (&a& is in units of packets/sec). The ratio &(La)/R&, called the traffic intensity.\n - if If &(La)/R& > 1: then queueing delay &->infty& \n - &therefore& *Design your system so that the traffic intensity is no greater than 1.*\n\n- 1.4.3 End-to-End Delay\n - Suppose there are &N - 1& routers between the source host and the destination host. with some other supposations we have,\n &d_(end-end)=N(d_(proc)+d_(trans)+d_(\"prop\"))& \n\n- 1.4.4 Throughput in Computer Networks\n - The **instantaneous throughput** at any instant of time is the rate (in bits/sec) at which Host &B& is receiving the file.\n - If the file consists of &F& bits and the transfer takes &T& seconds for Host &B& to receive all &F& bits, then the average throughput of the file transfer is &F/T& bits/sec.\n - \n\n - Throughput for a file transfer from server to client is &min{R_1, R_2, ..., R_N}&, which is once again the transmission rate of the bottleneck link along the path between server and client.\n\n- 1.5.1 Layered Architecture\n - \n - Application Layer: \n - We’ll refer to this packet of information at the application layer as a **message**.\n Examples includes HTTP, SMTP, FTP, etc\n - Transport Layer: \n - we’ll refer to a transport-layer packet as a **segment**.\n Examples includes TCP, UDP\n - Network Layer:\n - responsible for moving packets known as **datagrams** from one host to another\n Examples includes IP\n - Link Layer: \n - we’ll refer to the linklayer packets as **frames**.\n Examples of linklayer protocols include Ethernet, WiFi, and the cable access network’s DOCSIS protocol\n - Physical Layer:\n - the job of the physical layer is to move the individual bits within the frame from one node to the next.\n The protocols in this layer are again link dependent and further depend on the actual transmission medium of the link (for example, twisted-pair copper wire, single-mode fiber optics)", + "readme_title": "COMPUTER NETWORKING A Top-Down Approach", + "releases_count": 0, + "repo": "computer-networks-notes", + "repo_name": "computer-networks-notes", + "stars": 1, + "topics": [ + "computer-networks" + ], + "updated_at": "2020-06-30T03:51:05Z", + "url": "https://github.com/hassan11196/computer-networks-notes", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/computer-networks-notes" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/computer-science-flash-cards" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 123, + "contributors_count": 22, + "contributors_top": [ + { + "contributions": 78, + "html_url": "https://github.com/jwasham", + "login": "jwasham" + }, + { + "contributions": 12, + "html_url": "https://github.com/apps/dependabot", + "login": "dependabot[bot]" + }, + { + "contributions": 7, + "html_url": null, + "login": "tinpee" + }, + { + "contributions": 4, + "html_url": "https://github.com/linbingqiang", + "login": "linbingqiang" + }, + { + "contributions": 2, + "html_url": null, + "login": "Alvimm" + }, + { + "contributions": 2, + "html_url": "https://github.com/devyash", + "login": "devyash" + }, + { + "contributions": 2, + "html_url": "https://github.com/hofbi", + "login": "hofbi" + }, + { + "contributions": 2, + "html_url": "https://github.com/shivansh", + "login": "shivansh" + } + ], + "created_at": "2021-05-17T13:40:46Z", + "default_branch": "main", + "description": "Mini website for testing both general CS knowledge and enforce coding practice and common algorithm/data structure memorization.", + "dirs_total_count": 5, + "files_root_entries": [ + { + "path": ".gitignore", + "size": 67, + "type": "file" + }, + { + "path": 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"file" + }, + { + "path": "screenshots", + "size": 0, + "type": "dir" + }, + { + "path": "static", + "size": 0, + "type": "dir" + }, + { + "path": "templates", + "size": 0, + "type": "dir" + }, + { + "path": "wsgi.py", + "size": 71, + "type": "file" + } + ], + "files_total_count": 30, + "first_commit_date_default_branch": "2016-06-29T23:26:32Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": true, + "has_docs": false, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/computer-science-flash-cards", + "is_archived": false, + "is_fork": true, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "HTML", + "size": 12561 + }, + { + "language": "Python", + "size": 7211 + }, + { + "language": "CSS", + "size": 1792 + }, + { + "language": "JavaScript", + "size": 1781 + }, + { + "language": "Dockerfile", + "size": 294 + }, + { + "language": "Shell", + "size": 171 + } + ], + "last_commit_date_default_branch": "2021-05-02T17:47:48Z", + "last_commit_oid_default_branch": "91aa42d31737113aabdf23e48a86bc0f6407b9ca", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "Creative Commons Attribution Share Alike 4.0 International", + "license_spdx": "CC-BY-SA-4.0", + "name_with_owner": "hassan11196/computer-science-flash-cards", + "owner": "hassan11196", + "parent_repo": "jwasham/computer-science-flash-cards", + "parent_url": "https://github.com/jwasham/computer-science-flash-cards", + "primary_language": "HTML", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2021-05-02T17:47:48Z", + "readme_length": 7256, + "readme_text": "# Computer Science Flash Cards\n\nThis is a little website I've put together to allow me to easily make flash cards and quiz myself for memorization of:\n\n- General cs knowledge\n - vocabulary\n - definitions of processes\n - powers of 2\n - design patterns\n- Code\n - data structures\n - algorithms\n - solving problems\n - bitwise operations\n\nWill be able to use it on:\n\n- desktop\n- mobile (phone and tablet)\n\nIt uses:\n\n- Python 3\n- Flask\n- SQLite\n\n---\n\n## About the Site\n\nHere's a brief rundown: https://startupnextdoor.com/flash-cards-site-complete/\n\n## Screenshots\n\nUI for listing cards. From here you can add and edit cards.\n\n![Card UI](screenshots/cards_ui-1467754141259.png)\n\n---\n\nThe front of a General flash card.\n\n![Memorizing general knowledge](screenshots/memorize_ui-1467754306971.png)\n\n---\n\nThe reverse (answer side) of a Code flash card.\n\n![Code view](screenshots/memorize_code-1467754962142.png)\n\n## Important Note\n\nThe set included in this project (**cards-jwasham.db**) is not my full set, and is way too big already.\n\nThanks for asking for my list of 1,792 cards. But **it’s too much.** I even printed them out. It’s 50 pages, front and back, in tiny text. It would take about 8 hours to just read them all.\n\nMy set includes a lot of obscure info from books I’ve read, Python trivia, machine learning knowledge, assembly language, etc.\n\nI've added it to the project if you want it (**cards-jwasham-extreme.db**). You've been warned.\n\nPlease make your own set, and while you’re making them, only make cards for what you need to know. Otherwise, it gets out of hand.\n\n## How to convert to Anki or CSV\n\nIf you don't want to run a server, you can simply use Anki or a similar service/app. Use this script to convert from my sets (SQLite .db file), or yours, to CSV:\n\nhttps://github.com/eyedol/tools/blob/master/anki_data_builder.py\n\nThanks [@eyedol](https://github.com/eyedol)\n\n## How to run it on a server\n\n1. Clone project to a directory on your web server.\n1. Edit the config.txt file. Change the secret key, username and password. The username and password will be the login\n for your site. There is only one user - you.\n1. Follow this long tutorial to get Flask running. It was way more work than it should be:\n https://www.digitalocean.com/community/tutorials/how-to-serve-flask-applications-with-uwsgi-and-nginx-on-ubuntu-16-04\n - `wsgi.py` is the entry point. It calls `flash_cards.py`\n - This is my systemd file `/etc/systemd/system/flash_cards.service`: [view](flash_cards.service)\n - you can see the paths where I installed it, and the name of my virtualenv directory\n - when done with tutorial:\n ```shell\n sudo systemctl restart flash_cards\n sudo systemctl daemon-reload\n ```\n1. When you see a login page, you're good to go.\n1. Log in.\n1. Click the \"General\" or \"Code\" button and make a card!\n1. When you're ready to start memorizing, click either \"General\" or \"Code\"\n in the top menu.\n\n## How to run it on local host (Quick Guide)\n\n*Provided by [@devyash](https://github.com/devyash) - devyashsanghai@gmail.com - Reach out to this contributor if you have trouble.*\n\n1. Install dependencies:\n 1. Install [Python](https://www.python.org/download/releases)\n 1. Add python as environment variable [windows](http://stackoverflow.com/questions/3701646/how-to-add-to-the-pythonpath-in-windows-7)\n 1. To install pip, securely download [get-pip.py](https://bootstrap.pypa.io/get-pip.py)\n 1. Run `python get-pip.py` in terminal\n 1. Add pip to your PATH system variable [windows](https://stackoverflow.com/questions/23708898/pip-is-not-recognized-as-an-internal-or-external-command)\n 1. Run `pip install -r requirements.txt` in terminal after going to correct folder\n1. Open flash_cards.py and uncomment the line 52-55 beginning from `@app.route('/initdb')`\n1. Type `python flash_cards.py` - if you get error for flask then use `python -m pip install Flask` first then run `flash_card.py` file\n1. Open localhost:5000/initdb\n1. Login using id:USERNAME='admin', PASSWORD='default'.\n1. Comment the line 52-55 in flash_cards.py\n\n**NOTE:** If you wish to use John's flash cards then also do following steps:\n\n1. Copy db files such as `cards-jwasham-extreme` OR `cards-jwasham` and paste them in db folder\n1. Edit file `flash_cards.py` line 11 and replace cards with any of the other database files\n1. Repeat the above steps from step 3\n\nEvery time you wish to run your db just open folder in terminal and run `python flash_cards.py`\n\n## How to run with Docker\n\n*Provided by [@Tinpee](https://github.com/tinpee) - tinpee.dev@gmail.com - Reach out to this contributor if you have trouble.*\n\n__Make sure you already installed [docker](https://www.docker.com) and optionally [docker-compose](https://docs.docker.com/compose/install/)__\n\n1. Clone project to any where you want and go to source folder.\n1. Edit the `config.txt` file. Change the secret key, username and password. The username and password will be the login for your site. There is only one user - you.\n1. Build image:\n - Docker: `docker build . -t cs-flash-cards`\n - Compose: `docker-compose build`\n1. Run container:\n - Docker: `docker run -d -p 8000:8000 --name cs-flash-cards cs-flash-cards`\n - Compose: `docker-compose up`\n1. Go your browser and type `http://localhost:8000`\n\n__If you already had a backup file `cards.db`. Run following command:__\n\n*Note: We don't need to rebuild image, just delete old container if you already built.*\n\n```shell\ndocker run -d -p 8000:8000 --name cs-flash-cards -v :/src/db cs-flash-cards\n```\n\n- ``: is the full path contains `cards.db`.\n- Example: `/home/tinpee/cs-flash-cards/db`, and `cards.db` is inside this folder.\n\nFor convenience, if you don't have `cards.db`, this container will auto copy a new one from `cards-empty.db`.\n\n---\n\n### How to backup data ?\nWe just need store `cards.db` file, and don't need any sql command.\n- If you run container with `-v :/src/db` just go to `folder_db` and store `cards.db` anywhere you want.\n- Without `-v flag`. Type: `docker cp :/src/db/cards.db /path/to/save`\n\n### How to restore data ?\n- Delete old container (not image): `docker rm cs-flash-cards`\n- Build a new one with `-v flag`:\n`docker run -d -p 8000:8000 --name cs-flash-cards -v :/src/db cs-flash-cards`\n- Voila :)\n\n### How to deploy docker file on heroku\n\n- first install [heroku CLI](https://devcenter.heroku.com/articles/heroku-cli)\n- change `entrypoint.sh`\n\n```shell\n- export CARDS_SETTINGS=/src/config.txt\ngunicorn --bind 0.0.0.0:$8000 flash_cards:app\n+ export CARDS_SETTINGS=/src/config.txt\ngunicorn --bind 0.0.0.0:$PORT flash_cards:app\n```\n- deploy docker file with following commands\n\n```shell\nheroku login\nheroku container:login\nheroku create\n# Creating app... done, ⬢ your-app-name\nheroku container:push web --app your-app-name\nheroku container:release web --app your-app-name\nheroku open --app your-app-name\n```\n\n## Alternative for Node fans\n\n[@ashwanikumar04](https://github.com/ashwanikumar04) put together an alternative flash cards site running Node: https://github.com/ashwanikumar04/flash-cards\n\nCheck out the demo!\n\n*Happy learning!*", + "readme_title": "Computer Science Flash Cards", + "releases_count": 0, + "repo": "computer-science-flash-cards", + "repo_name": "computer-science-flash-cards", + "stars": 1, + "topics": [], + "updated_at": "2022-08-19T17:53:53Z", + "url": "https://github.com/hassan11196/computer-science-flash-cards", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/computer-science-flash-cards" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/dashdot" + ], + "github_repos_metadata": [ + { + 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"MauriceNino/dashdot", + "parent_url": "https://github.com/MauriceNino/dashdot", + "primary_language": "TypeScript", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2023-01-11T05:14:38Z", + "readme_length": 3220, + "readme_text": "\n

\n \"dash.\n

\n\n

\n \n \n \n\n \n \n \n

\n

\n Feel free to join the Discord and star the repo if you like the project!\n

\n\n
\n\n

\n dash. (or dashdot) is a modern server dashboard,\n running on the latest tech, designed with glassmorphism in mind.\n It is intended to be used for smaller VPS and private servers.\n

\n
\n

\n Live Demo\n |\n Docker Image\n

\n\n#\n\n\n \"Consider\n\n\n\n\n**dash.** is a open-source project, so any contribution is highly appreciated.\nIf you are interested in further developing this project, have a look at the\n[Contributing.md](./.github/CONTRIBUTING.md).\n\nIn case you want to financially support this project, you can visit my\n[GitHub Sponsors](https://github.com/sponsors/MauriceNino), or my [Ko-Fi](https://ko-fi.com/mauricenino).\n\n## Preview\n\n\n\n| Dark-Mode | Light-Mode |\n| -------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------- |\n| \"Screenshot | \"Screenshot |\n\n\n\n## Documentation\n\n- [Installation Options](https://getdashdot.com/docs/install)\n- [Configuration Options](https://getdashdot.com/docs/config)\n- [Contributing](./.github/CONTRIBUTING.md)\n- [Changelog](./.github/CHANGELOG.md)\n\n## Quick Install (Docker)\n\nImages are hosted on [DockerHub](https://hub.docker.com/r/mauricenino/dashdot),\nand are available for both AMD64 and ARM devices.\n\n```bash\ndocker container run -it \\\n -p 80:3001 \\\n -v /:/mnt/host:ro \\\n --privileged \\\n mauricenino/dashdot\n```\n\nTo get more information on why which flag is needed, or if you want to use other\ninstall options instead (`docker-compose`, or from source), have a look at the\n[installation options](https://getdashdot.com/docs/install).\n\nTo read more about configuration options, you can visit the [configuration options](https://getdashdot.com/docs/config).", + "readme_title": "", + "releases_count": 0, + "repo": "dashdot", + "repo_name": "dashdot", + "stars": 0, + "topics": [], + "updated_at": "2023-01-11T05:14:42Z", + "url": "https://github.com/hassan11196/dashdot", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/dashdot" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/demo_app" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 17, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 17, + "html_url": "https://github.com/therealwokewok", + "login": "therealwokewok" + } + ], + "created_at": "2024-07-12T00:09:57Z", + "default_branch": "main", + "description": "Demo application for the latest version of Flask-Tenants", + "dirs_total_count": 4, + "files_root_entries": [ + { + "path": ".env.example", + "size": 85, + "type": "file" + }, + { + "path": "LICENSE", + "size": 1091, + "type": "file" + }, + { + "path": "README.md", + "size": 5065, + "type": "file" + }, + { + "path": "app.py", + "size": 1388, + "type": "file" + }, + { + "path": "config.py", + "size": 223, + "type": "file" + }, + { + "path": "documentation", + "size": 0, + "type": "dir" + }, + { + "path": "errors.py", + "size": 729, + "type": "file" + }, + { + "path": "posts", + "size": 0, + "type": "dir" + }, + { + "path": "public", + "size": 0, + "type": "dir" + }, + { + "path": "requirements.txt", + "size": 686, + "type": "file" + }, + { + "path": "tenants", + "size": 0, + "type": "dir" + } + ], + "files_total_count": 19, + "first_commit_date_default_branch": "2024-07-02T22:09:35Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/demo_app", + "is_archived": false, + "is_fork": true, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Python", + "size": 14446 + } + ], + "last_commit_date_default_branch": "2024-07-06T00:06:31Z", + "last_commit_oid_default_branch": "d3365ed75da45614c608a0c1ac441dedc32c3013", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "MIT License", + "license_spdx": "MIT", + "name_with_owner": "hassan11196/demo_app", + "owner": "hassan11196", + "parent_repo": "Flask-Tenants/demo_app", + "parent_url": "https://github.com/Flask-Tenants/demo_app", + "primary_language": "Python", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2024-07-12T00:26:47Z", + "readme_length": 5064, + "readme_text": "# Flask-Tenants Demo Application\n\nThis demo application demonstrates the implementation of Flask-Tenants for a multi-tenant blog platform. The application allows for the creation, retrieval, updating, and deletion of tenants and their respective blog posts. This README will guide you through the setup and usage of the application using Flask and Postman.\n\nYou may utilize a [Postman Collection](https://raw.githubusercontent.com/Flask-Tenants/demo_app/main/documentation/Flask-Tenants.postman_collection.json) designed for this demo application.\n\n## Requirements\n\n- alembic==1.13.2\n- Flask==3.0.3\n- SQLAlchemy==2.0.31\n- Flask-SQLAlchemy==3.1.1\n- flask-tenants==0.4.6\n- psycopg2-binary==2.9.9\n- python-dotenv==1.0.1\n\n## Installation\n\n1. Clone the repository:\n\n ```sh\n git clone https://github.com/Flask-Tenants/demo_app.git\n cd demo_app\n ```\n\n2. Create and activate a virtual environment:\n\n ```sh\n python -m venv venv\n source venv/bin/activate # On Windows use `venv\\Scripts\\activate`\n ```\n\n3. Install the required dependencies:\n\n ```sh\n pip install -r requirements.txt\n ```\n\n## Running the Application\n\n1. Set environment variables for the application:\n\n ```sh\n export FLASK_APP=app.py\n export FLASK_ENV=development\n ```\n\n2. Initialize the database:\n\n Create a `public` PostgreSQL schema and then run migrations\n\n ```sh\n flask db init\n flask db migrate\n flask db upgrade\n ```\n\n3. Run the Flask application:\n\n ```sh\n flask run\n ```\n\n## API Endpoints\n\n### Public View\n\n#### Create Tenant\n\n- **Endpoint:** `POST /create_tenant`\n- **Description:** Creates a new tenant.\n- **Request Body:**\n\n ```json\n {\n \"name\": \"tenant1\",\n \"domain_name\": \"tenant1.flasktenants.com\",\n \"phone_number\": \"123-456-7890\",\n \"address\": \"123 Example St\"\n }\n ```\n\n- **Response:**\n\n ```json\n {\n \"message\": \"Tenant tenant1 created successfully\",\n \"tenant\": {\n \"address\": \"123 Example St\",\n \"domain_name\": \"tenant1.flasktenants.com\",\n \"id\": 1,\n \"name\": \"tenant1\",\n \"phone_number\": \"123-456-7890\"\n }\n }\n ```\n\n#### Read Tenant\n\n- **Endpoint:** `GET /get_tenant/`\n- **Description:** Retrieves information about a specific tenant.\n- **Response:**\n\n ```json\n {\n \"address\": \"123 Example St\",\n \"id\": 1,\n \"name\": \"tenant1\",\n \"phone_number\": \"123-456-7890\"\n }\n ```\n\n#### Update Tenant\n\n- **Endpoint:** `PUT /update_tenant/`\n- **Description:** Updates the information of a specific tenant.\n- **Request Body:**\n\n ```json\n {\n \"name\": \"tenant3\",\n \"domain_name\": \"tenant3.flasktenants.com\",\n \"phone_number\": \"123-456-7890\",\n \"address\": \"123 Example St\"\n }\n ```\n\n- **Response:**\n\n ```json\n {\n \"message\": \"Tenant tenant3 updated successfully\",\n \"tenant\": {\n \"address\": \"123 Example St\",\n \"name\": \"tenant3\",\n \"phone_number\": \"123-456-7890\"\n }\n }\n ```\n\n#### Delete Tenant\n\n- **Endpoint:** `DELETE /delete_tenant/`\n- **Description:** Deletes a specific tenant and its schema.\n- **Response:**\n\n ```json\n {\n \"message\": \"Tenant and its schema deleted successfully\"\n }\n ```\n\n### Tenant View\n\n#### Create Post\n\n- **Endpoint:** `POST /posts`\n- **Description:** Creates a new post for a tenant.\n- **Request Body:**\n\n ```json\n {\n \"title\": \"The Great Commission\",\n \"body\": \"Go therefore and make disciples of all nations, baptizing them in the name of the Father and of the Son and of the Holy Spirit, teaching them to observe all that I have commanded you. And behold, I am with you always, to the end of the age.\",\n \"author\": \"Jesus\"\n }\n ```\n\n- **Response:**\n\n ```json\n {\n \"message\": \"Post created successfully\"\n }\n ```\n\n#### Read Posts\n\n- **Endpoint:** `GET /posts`\n- **Description:** Retrieves all posts for a tenant.\n- **Response:**\n\n ```json\n [\n {\n \"author\": \"Jesus\",\n \"body\": \"Go therefore and make disciples of all nations, baptizing them in the name of the Father and of the Son and of the Holy Spirit, teaching them to observe all that I have commanded you. And behold, I am with you always, to the end of the age.\",\n \"id\": 1,\n \"title\": \"The Great Commission\"\n }\n ]\n ```\n\n#### Update Post\n\n- **Endpoint:** `PUT /posts/`\n- **Description:** Updates a specific post for a tenant.\n- **Request Body:**\n\n ```json\n {\n \"title\": \"Salvation\",\n \"body\": \"If you confess with your mouth that Jesus is Lord and believe in your heart that God raised him from the dead, you will be saved.\",\n \"author\": \"Paul\"\n }\n ```\n\n- **Response:**\n\n ```json\n {\n \"message\": \"Post updated successfully\"\n }\n ```\n\n#### Delete Post\n\n- **Endpoint:** `DELETE /posts/`\n- **Description:** Deletes a specific post for a tenant.\n- **Response:**\n\n ```json\n {\n \"message\": \"Post deleted successfully\"\n }\n ```\n\n## Contributing\n\nContributions are welcome! Please submit a pull request or open an issue to discuss any changes.\n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.", + "readme_title": "Flask-Tenants Demo Application", + "releases_count": 0, + "repo": "demo_app", + "repo_name": "demo_app", + "stars": 0, + "topics": [], + "updated_at": "2024-07-12T00:09:57Z", + "url": "https://github.com/hassan11196/demo_app", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/demo_app" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/dep_graph" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 24, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 24, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + } + ], + "created_at": "2023-02-10T21:49:18Z", + "default_branch": "main", + "description": "A simple Python package for generating resolved dependency graphs from JSON files.", + "dirs_total_count": 6, + "files_root_entries": [ + { + "path": ".github", + "size": 0, + "type": "dir" + }, + { + "path": ".gitignore", + "size": 3454, + "type": "file" + }, + { + "path": ".pre-commit-config.yaml", + "size": 180, + "type": "file" + }, + { + "path": "README.md", + "size": 1504, + "type": "file" + }, + { + "path": "dep_graph", + "size": 0, + "type": "dir" + }, + { + "path": "poetry.lock", + "size": 34016, + "type": "file" + }, + { + "path": "pyproject.toml", + "size": 414, + "type": "file" + }, + { + "path": "setup.py", + "size": 360, + "type": "file" + }, + { + "path": "tests", + "size": 0, + "type": "dir" + } + ], + "files_total_count": 21, + "first_commit_date_default_branch": "2023-02-10T21:49:19Z", + "forks": 0, + "has_ci": true, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": true, + "homepage_url": "https://pypi.org/project/dep-graph-hassan/", + "input_url": "https://github.com/hassan11196/dep_graph", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Python", + "size": 10257 + } + ], + "last_commit_date_default_branch": "2023-02-13T02:13:43Z", + "last_commit_oid_default_branch": "35cecbc41bbaef3dd113005f3ba9e0950e746eb3", + "latest_release_date": "2023-02-13T01:32:47Z", + "latest_release_tag": "1.0.1", + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/dep_graph", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "Python", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 1, + "pull_requests_total": 1, + "pushed_at": "2024-07-15T17:52:18Z", + "readme_length": 1503, + "readme_text": "# dep_graph\n\n[![PyPI version](https://badge.fury.io/py/dep-graph-hassan.svg)](https://pypi.org/project/dep-graph-hassan/)\n[![GitHub release](https://img.shields.io/github/release/hassan11196/dep_graph.svg)](https://github.com/hassan11196/dep_graph)\n\nA simple Python package for generating resolved dependency graphs from JSON files.\n\n## Running the Module\n\nTo run the module, use the following command:\n\n python -m dep_graph\n\nBy default, the module will read the dependencies from `/tmp/deps.json`.\n\nTo specify a different path to the dependencies, use the following command:\n\n python -m dep_graph --path ./some_fir/deps.json\n\n## Creating a Dummy Dependency JSON File\n\nTo create a dummy dependency JSON file, use the following command:\n\n python dep_graph/helper/create_dummy_file.py --path ./tests test_file.json\n\n By default it outputs to `/tmp/deps.json`\n \n## Running Tests\n\nTo run the tests, use the following command:\n\n python -m unittest discover tests/\n\n## Building the Package\n\nYou can build the package using either poetry or setup.py.\n\n### Method 1: Using Poetry\n\nTo build the package using poetry, use the following commands:\n\n1. Create a virtual environment:\n\n poetry install\n\n2. Build the package:\n\n poetry build\n\n### Method 2: Using setup.py\n\nTo build the package using setup.py, use the following command:\n\n python setup.py sdist bdist_wheel\n\n## Installing Pre-commit Hooks\n\nTo install pre-commit hooks, use the following command:\n\n pre-commit install", + "readme_title": "dep_graph", + "releases_count": 4, + "repo": "dep_graph", + "repo_name": "dep_graph", + "stars": 0, + "topics": [], + "updated_at": "2023-02-13T01:44:44Z", + "url": "https://github.com/hassan11196/dep_graph", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/dep_graph" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/developer-portfolios" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 593, + "contributors_count": 242, + "contributors_top": [ + { + "contributions": 76, + "html_url": null, + "login": "Emma Wedekind" + }, + { + "contributions": 55, + "html_url": "https://github.com/jakeherp", + "login": "jakeherp" + }, + { + "contributions": 53, + "html_url": "https://github.com/eclectic-coding", + "login": "eclectic-coding" + }, + { + "contributions": 42, + "html_url": "https://github.com/evanplaice", + "login": "evanplaice" + }, + { + "contributions": 27, + "html_url": "https://github.com/Easybuoy", + "login": "Easybuoy" + }, + { + "contributions": 12, + "html_url": "https://github.com/gep13", + "login": "gep13" + }, + { + "contributions": 12, + "html_url": "https://github.com/geekskick", + "login": "geekskick" + }, + { + "contributions": 8, + "html_url": "https://github.com/thblckjkr", + "login": "thblckjkr" + } + ], + "created_at": "2020-10-02T20:51:27Z", + "default_branch": "master", + "description": "A list of developer portfolios for your inspiration", + "dirs_total_count": 2, + "files_root_entries": [ + { + "path": ".github", + "size": 0, + "type": "dir" + }, + { + "path": "CONTRIBUTING.md", + "size": 8101, + "type": "file" + }, + { + "path": "README.md", + "size": 12951, + "type": "file" + } + ], + "files_total_count": 3, + "first_commit_date_default_branch": "2019-09-13T14:18:58Z", + "forks": 0, + "has_ci": true, + "has_contributing": true, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/developer-portfolios", + "is_archived": false, + "is_fork": true, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [], + "last_commit_date_default_branch": "2020-10-02T13:41:23Z", + "last_commit_oid_default_branch": "3eb074b35dc30e975fa92d8f1135e9f16d8558ac", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/developer-portfolios", + "owner": "hassan11196", + "parent_repo": "emmabostian/developer-portfolios", + "parent_url": "https://github.com/emmabostian/developer-portfolios", + "primary_language": null, + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2020-12-27T03:21:55Z", + "readme_length": 12940, + "readme_text": "# Developer Portfolios\n\nA list of developer portfolios for your inspiration\n\nHave you built a portfolio? Are you proud of it?! Open a [PR](./CONTRIBUTING.md) to this repo and let's showcase your work! Refer to the [CONTRIBUTING](./CONTRIBUTING.md) file for direction.\n\nThis repo was inspired by [Ali Spittel's](https://twitter.com/ASpittel) tweet\n[\"Portfolio\"](https://twitter.com/ASpittel/status/1171604728951779328)\n\nHopefully this repo can serve as a source of inspiration for your portfolio!\n\n---\n\n**Jump to:** [A](#a) | [B](#b) | [C](#c) | [D](#d) | [E](#e) | [F](#f) | [G](#g) | [H](#h) | [I](#i) | [J](#j) | [K](#k) | [L](#l) | [M](#m) | [N](#n) | [O](#o) | [P](#p) | [Q](#q) | [R](#r) | [S](#s) | [T](#t) | [V](#v) | [W](#w) | [Y](#y) | [Z](#z) \n\n---\n\n## A\n* [Aashutosh Rathi](https://aashutosh.dev)\n* [Abdelouahed Medjoudja](https://geekabdelouahed.github.io/flutter-web-portfolio)\n* [Abdul Rauf](https://armujahid.me)\n* [Abhinav Galodha](https://www.galodha.com)\n* [Abhushan Adhikari Joshi](https://www.abhushan.live/)\n* [Adam Alston](https://www.adamalston.com)\n* [Adam Conrad](http://conradadam.com)\n* [Aditya Vikram Singh](https://www.adityavsingh.com/)\n* [Agney Menon](https://agney.dev)\n* [Ahmad Awais](https://ahmadawais.com/)\n* [Ahsan Khan](https://ahsankhan.me/)\n* [Akhil Jacob John](https://akhiljacobjohn.github.io/)\n* [Akshat Gupta](https://www.akshatvg.com)\n* [Alejandro Sobko](http://alejandrosobko.com)\n* [Alestor Aldous](http://alestor123.github.io/)\n* [Alex Goff](http://alex-goff.com)\n* [Alexander Schmidt](https://alexanderschmidt.info/)\n* [Alexey Golub](http://tyrrrz.me)\n* [Alfred Dagenais](https://alfreddagenais.com)\n* [Ali Spittel](http://alispit.tel)\n* [Aloys Dillar](https://trolologuy.github.io/)\n* [Aman Mittal](http://amanhimself.dev)\n* [Amit Gujar](https://amitgujar.github.io/)\n* [Amruth Pillai](https://amruthpillai.com)\n* [Andrew Woods](https://andrewwoods.net)\n* [Andy Bell](https://andy-bell.design/)\n* [Anil Khatri](https://imkaka.github.io)\n* [Ankit Kumar](https://ankitdroidgit.github.io/)\n* [Anshuman Verma](https://anshumanv.dev/)\n* [Antoni Kepinski](https://kepinski.me)\n* [Anurag Hazra](https://anuraghazra.github.io)\n* [Arjun Ganesan](https://arjun-g.com)\n* [Aromal Anil](https://aromalanil.me)\n* [Ashlee Boyer](http://ashleemboyer.dev)\n* [Atanas Atanasov](https://atanas.info)\n* [Avi Patil](https://avipatil.ml)\n* [Azhar Zafar](http://azharr.com)\n\n## B\n* [Bah Cheick Marc Herve](https://marcbah.com)\n* [Bankole Ahmed](http://bankoleahmed.netlify.com)\n* [Becca Bailey](http://Becca.is)\n* [Bekah Hawrot Weigel](http://bekahhw.github.io)\n* [Benjamin Lannon](https://lannonbr.com)\n* [Berat Bozkurt](https://beratbozkurt.net/)\n* [Bertil Tandayamo](https://www.bertiltandayamo.me/)\n* [Bhavani Ravi](http://bhavaniravi.com)\n* [Bilal Tahseen](https://finalartproduct.com)\n* [Bob Matyas](https://www.bobmatyas.com)\n* [Bouwe Westerdijk](https://bouwe.io)\n* [Brad Garropy](https://bradgarropy.com)\n* [Brianna Roby](http://brianna-roby.com)\n* [Brittany Chiang](https://brittanychiang.com)\n\n## C\n* [Cade Kynaston](https://cade.codes)\n* [Caitlyn Greffly](https://caitlyngreffly.com/)\n* [Casper Hauge](http://csr-design.dk)\n* [Casper Iversen](https://caspertheghost.me)\n* [Cecelia Martinez](http://ceceliacreates.com)\n* [Charles C. Pustejovsky III](https://cpustejovsky.com/)\n* [Chetan Padia](https://chetbox.com)\n* [Chetan Raj](https://chetanraj.in/)\n* [Chetanya Kandhari](https://availchet.github.io)\n* [Chicago IT Systems](https://www.chicagoitsystems.com/)\n* [Chris Otto](https://chrisotto.dev/)\n* [Chris Poole](https://chrispoole.com)\n* [Christian Kaisermann](https://kaisermann.me)\n* [Chuck Smith](https://chucksmith.dev)\n* [Chuckz Okoye](https://chuckzokoye.com)\n* [Cole Emeruche](https://coleruche.com/works/)\n* [Colin Lord](https://colinlord.com/)\n* [Collins Koech](https://collinskoechportfolio.web.app)\n* [Conlin Durbin](https://wuz.fyi)\n\n\n## D\n* [Damian Markowski](http://damianmarkowski.pl)\n* [Daniel Canetti](https://danielcanetti.com/)\n* [Danstan Onyango](https://zemuldo.com/)\n* [Davide Santangelo](https://davidesantangelo.com)\n* [Deepraj Rakshit](http://deeprajrakshit.me/)\n* [Denis Tokarev](https://devlato.com)\n* [Dick Wyn Yong](https://dickwyn.xyz)\n* [Dillion Megida](http://dillionmegida.com/me)\n* [Dimitri Pashutskii](https://dpashutskii.com/)\n* [Drew Bredvick](https://drew.tech/)\n\n## E\n* [Efrén Martínez Rodríguez](https://www.efrenmartinez.dev/)\n* [Emily Kondziola](https://emilykondziola.com/)\n* [Emmanuel ADEKPLOVI](https://homescriptone.com)\n* [Enea Xharja](https://eneaxharja.com)\n* [Ezekiel Ekunola](https://ezekielekunola.com)\n\n## F\n* [Faisal Ahmed Khan](https://FaisalAhmedKhan.github.io/)\n* [Fayaz Bin Salam](https://p32929.github.io/)\n* [Felix Leupold](https://xiel.dev)\n* [Fidalgo Pedro](http://fidalgo.dev)\n* [Flavia Medici](https://t.co/iQK1Hbx8xD?amp=1)\n* [Franklin Castellanos](https://onecastell.github.io)\n\n## G\n* [Gabriel Romualdo](https://xtrp.io/) ([@xtrp](https://github.com/xtrp))\n* [Gabriela Radu](http://www.gabrielaradu.me/)\n* [Gabriele Corti](https://borntofrappe.github.io/)\n* [Garv Nanwani](https://garvnanwani.netlify.app/)\n* [Genesis Gabiola](https://genesisgabiola.now.sh/)\n* [Georgi Yanev](https://gyanev.com)\n* [Gherciu Gheorghe](https://gherciu.github.io/portfolio/)\n* [Gianluca Fiore](http://gianlucafiore.it)\n* [Gift Egwuenu](https://giftegwuenu.com)\n\n## H\n* [Hassan Murtaza](https://hassanmurtaza.com)\n* [Herman Starikov](http://starikov.dev)\n* [Hulya Karakaya](https://hulya.netlify.app/)\n* [Hungry Bear Studio](https://www.hungrybearstudios.com/)\n* [Huzaifa Mustafa](https://www.huzaifamustafa.com/)\n\n\n## I\n* [Ian Trent](https://iantrent.com)\n* [Ibrahim Hizlioglu](https://www.ibrahimhizlioglu.com)\n* [Isitha Subasinghe](https://isub.dev)\n* [Ismail Ghallou aka Smakosh](https://smakosh.com)\n\n## J\n* [Jacob Herper](https://herper.io)\n* [Jahir Fiquitiva](https://jahir.dev)\n* [Jai Kirdatt](https://JaiK.me)\n* [Jainam Desai](https://th3c0d3br34ker.github.io)\n* [Jakub Łukasiewicz](https://joren.ga)\n* [James Mutuku](https://jim4067.github.io/portfolio/)\n* [James Turner](http://turnerj.com)\n* [Jamie Hammond](https://jamiehammond.dev/)\n* [Jane Manchun Wong](http://wongmjane.com)\n* [Janessa Tran](http://janessatran.github.io)\n* [Jens van Wijhe](https://jens.ai)\n* [Jeremy Grifski](https://jeremygrifski.com/)\n* [Jerry Hirsch](https://jerryhirsch.com/)\n* [Jesus Rios](https://etxcoding.github.io/etxcodingcom/)\n* [Jibin Thomas](http://jibin.tech)\n* [Jim Raptis](http://www.raptis.wtf)\n* [Jo Lienhoop](https://jolienhoop.com/)\n* [Jordan Liu](https://jordanxliu.com)\n* [Josef Aidt](https://josefaidt.dev)\n* [Joseph Friedman](http://DecentGradient.com)\n* [Julia Johnson](http://juliacodes.com)\n\n## K\n* [Kaleigh Scruggs](http://kaleighscruggs.com)\n* [Kamran Hamid](https://mkamranhamid.netlify.com)\n* [Kapil Gorve](http://jskap.com)\n* [Karen Fletcher](https://knpfletcher.dev)\n* [Karthik Menon](https://www.karthikmenon.com/)\n* [Katheesh](https://katheesh.buzz) [console](https://katheesh.github.io/console/)\n* [Kenny Tran Co](https://www.kennytran.co)\n* [Kumar Abhirup](http://kumar.now.sh)\n* [Kunal Gaurav](https://kunalgaurav.me)\n* [Kyle Harrison](http://kyle.teamharrison.ca)\n* [Kyle Shook](http://Kyleshook.com)\n\n## L\n* [Laurie Barth](http://laurieontech.dev)\n* [Leandro Simões](https://lesimoes.dev)\n* [Lee Warrick](http://leewarrick.com)\n* [Lisa Blunt](https://lisablunt.github.io)\n* [Lisa Savoie](http://lscodes.com)\n* [Liz Lam](https://lizlam.github.io/)\n* [Long Do](https://longpdo.github.io/)\n* [Louay Hamada](https://louayhamada.com)\n* [Luca Fluri](https://www.lucafluri.ch/)\n* [Luca Lischetti](https://sirlisko.com)\n* [Luis Cacho](https://luiscachog.io)\n* [Luisa Rojas García](https://luisarojas.com)\n* [Lwin Moe Hein](http://lwinmoehein.me)\n\n## M\n* [Madhan K](https://madhank93.github.io/)\n* [Mads Hougesen](https://mhouge.dk/)\n* [Marc Backes](http://marc.dev)\n* [Marcos Aguayo](https://marcosaguayo.com)\n* [Margaret M Barringer](http://webwabisabi.com)\n* [Mario Kandut](https://www.mariokandut.com)\n* [Marissa Phul](http://mphul.codes/)\n* [Marouane Rassili](https://mrassili.com)\n* [Mason Slover](https://github.com/MasonSlover/ProcessingPortfolio)\n* [Matt Filer](http://mattfiler.co.uk)\n* [Matteo Lobello](https://matteolobello.it)\n* [Maya Shavin](https://www.mayashavin.com/)\n* [Md Nabil Ahsan](https://www.mdnabilahsan.com/)\n* [Michelle Brenner](http://MichelleBrenner.com)\n* [Miguel Rodriguez](https://migu.es)\n* [Mike Perry Y Attara](https://mikeattara.com)\n* [Mimi Kim](https://seeyouspacecow.com)\n* [Mitul Savani](http://mitulsavani.com)\n* [Mohamed Abdel Nasser](https://www.mohdabdelnasser.live/)\n* [Mohammad Darmousa](https://mdarmousa.github.io/)\n* [Mohammed Boudad](https://matrixersp.design/)\n* [Moritz Kornher](https://moritzkornher.de/)\n* [Mostafa Sameti](https://sameti.dev/)\n* [Mouad ZIANI](https://mouadziani.github.io/)\n* [Muhammad Muhaddis](https://muhaddis.info)\n* [Muhammad](http://muhammadraza.me)\n* [Muntadhar Haydar](https://muntadhar.net)\n\n## N\n* [Nacho Caiafa](https://nachokai.github.io/rpg-cv)\n* [Nam Phan](https://namtphan.github.io)\n* [Nesrine Sghaier](https://nesrinesghaier.me)\n* [Nico van Zyl](https://nicovanzyl.com)\n* [Nicolo Rebughini](https://nirebu.com/)\n* [Nurhak Altın](https://nurhakaltin.com)\n\n## O\n* [Oguzhan Ince](https://oguz.live)\n* [Olaolu Olawuyi](https://olaolu.dev)\n* [Omar Gastón Chalas](https://ogaston.com/)\n* [OOQQ](https://ooqq.me)\n* [Opeyemi Obembe](http://obem.be/opeyemi)\n* [Oscar Tian](http://www.bluexguardian.com)\n* [Oussama Bouchikhi](https://oussamabouchikhi.github.io/)\n\n## P\n* [Pandiyan Murugan](https://pandiyancool.github.io/pandiyan.cool/)\n* [Patricia Aas](https://patricia.no/)\n* [Patrick Hyatt](https://www.patrickhyatt.com/)\n* [Patrick Lehmann](https://patlehmann1.github.io/react_portfolio/)\n* [Patrick Obermeier](https://www.patrickobermeier.dev/)\n* [Patrick Reid](http://iamreliq.com)\n* [Pavel Rahman](https://pavstar619.github.io/pavel.rahman/)\n* [Praveen Kumar Purushothaman](https://praveen.science/)\n* [Praveen Saini](https://praveen-me.github.io)\n* [Priyadharshini Rajaram](https://priya.ws/)\n* [Pushpak Chhajed](https://pushpak1300.github.io/)\n\n## Q\n* [Qudusayo A](https://mequdusayo.netlify.app)\n\n## R\n* [Rafael Solis Melo](https://rsmelo92.github.io/portfolio/)\n* [Rahul Mahesh](https://rahulmahesh.netlify.com)\n* [Rahul Sawant](http://raalzz.com)\n* [Rajan Bhattarai](https://cdrrazan.com)\n* [Rick Hanlon](https://rickhanlonii.github.io/)\n* [Riley J. Shaw](https://rileyjshaw.com)\n* [Rimenes Ribeiro](https://rimenesribeiro.com)\n* [Roland L. Taylor](http://rolandixor.pro)\n* [Roman Smunyov](https://romanisthere.github.io/)\n* [Ropo John Olatujoye](http://simplycrownclothing.herokuapp.com)\n* [Roshan Kr Soni](https://roshankrsoni.github.io)\n* [Ryan Burgess](http://ryanburgess.com)\n* [Ryan MacLean](http://ryanmaclean.com)\n\n## S\n* [Sachin Srinivasan](https://s8sachin.github.io/)\n* [Sagar Giri](https://girisagar46.github.io/)\n* [Santosh Yadav](http://santoshyadav.dev)\n* [Saurabh Daware](https://www.saurabhdaware.in/)\n* [Saurav M H](https://sauravmh.com)\n* [Scott Spence](https://scottspence.com)\n* [Sedanur Yıldız](https://sedanuryildiz.com)\n* [Shaan Khan](https://www.shaankhan.dev/)\n* [Shahid Shaikh](http://shahidshaikh.com)\n* [Shannon Crabill](http://shannoncrabill.com)\n* [Shuvam Manna](http://shuvam.xyz)\n* [Simon Gilbert](https://www.simongilbert.net)\n* [Simon Knott](https://simonknott.de)\n* [Soham Mondal](https://sohammondal.com)\n* [Sonu kumar kushwaha](https://flyingsonu122.github.io/)\n* [Sourav Dutta](http://i-am-souravdutta.firebaseapp.com)\n* [Sree Godavarthi](http://sreegodavarthi.github.io)\n* [Stefan Bohacek](https://fourtonfish.com/)\n* [Stephen Klop](https://stephenklop.nl)\n* [Steve Baros](https://stevebaros.me)\n* [Subhayu Kr Bala](http://subhayu.me)\n* [Sulaiman Kareem](https://suleiman94.github.io/)\n* [Syeda Aimen Batool](http://aimen.dev)\n\n## T\n* [Tejas Kumar](http://tej.as)\n* [Thea Mushambadze](https://highflyer910.github.io/)\n* [Tom Sherman](https://tom-sherman.com)\n* [Travis Fischer](https://transitivebullsh.it)\n\n## V\n* [Vaibhav Singh](http://vaibhavsingh97.com)\n* [Valentine Elum](https://vahlcode.vercel.app)\n* [Varun Dey](https://varundey.me)\n* [Vatsal Shah](https://vatsalshah.in)\n* [Vicente G. Reyes](https://highcenburg.herokuapp.com/)\n* [Victor Aremu](http://bit.ly/victoraremu)\n* [Vidushan Chooriyakumaran](https://vidu.sh/an)\n* [Vincent Milum Jr](http://darkain.com)\n* [Vishwasa Navada K](https://vishwas.tech)\n* [Vito Sartori](https://vito.io)\n\n## W\n* [Wajahat Ali Abid](https://wajahataliabid.github.io/)\n\n## Y\n* [Yechiel Kalmenson](https://yechiel.me)\n* [YiMing Han](https://yiminghan.com)\n* [Yuri Faria](https://windows87.github.io/)\n\n## Z\n* [Ziyad](https://ziyadsk.github.io/portfolio-V2/)\n* [Zonayed Ahmed](https://zonayed.me/)\n* [Zunaid Aslam](https://zunaidaslam.com)", + "readme_title": "Developer Portfolios", + "releases_count": 0, + "repo": "developer-portfolios", + "repo_name": "developer-portfolios", + "stars": 0, + "topics": [], + "updated_at": "2020-10-02T20:51:29Z", + "url": "https://github.com/hassan11196/developer-portfolios", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/developer-portfolios" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/devfile-sample-python-basic" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 46, + "contributors_count": 10, + "contributors_top": [ + { + "contributions": 11, + "html_url": "https://github.com/maysunfaisal", + "login": "maysunfaisal" + }, + { + "contributions": 11, + "html_url": "https://github.com/yangcao77", + "login": "yangcao77" + }, + { + "contributions": 8, + "html_url": "https://github.com/schultzp2020", + "login": "schultzp2020" + }, + { + "contributions": 6, + "html_url": "https://github.com/elsony", + "login": "elsony" + }, + { + "contributions": 3, + "html_url": "https://github.com/johnmcollier", + "login": "johnmcollier" + }, + { + "contributions": 3, + "html_url": "https://github.com/thepetk", + "login": "thepetk" + }, + { + "contributions": 1, + "html_url": "https://github.com/jseseCCS", + "login": "jseseCCS" + }, + { + "contributions": 1, + "html_url": "https://github.com/jduimovich", + "login": "jduimovich" + } + ], + "created_at": "2023-10-04T12:52:45Z", + "default_branch": "main", + "description": "A basic sample application using Python with devfile", + "dirs_total_count": 1, + "files_root_entries": [ + { + "path": ".gitignore", + "size": 34, + "type": "file" + }, + { + "path": "LICENSE", + "size": 11357, + "type": "file" + }, + { + "path": "README.md", + "size": 1860, + "type": "file" + }, + { + "path": "app.py", + "size": 248, + "type": "file" + }, + { + "path": "deploy.yaml", + "size": 668, + "type": "file" + }, + { + "path": "devfile.yaml", + "size": 1612, + "type": "file" + }, + { + "path": "docker", + "size": 0, + "type": "dir" + }, + { + "path": "requirements.txt", + "size": 12, + "type": "file" + } + ], + "files_total_count": 8, + "first_commit_date_default_branch": "2021-04-26T15:01:20Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": true, + "has_docs": false, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/devfile-sample-python-basic", + "is_archived": false, + "is_fork": true, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Dockerfile", + "size": 548 + }, + { + "language": "Python", + "size": 248 + } + ], + "last_commit_date_default_branch": "2023-10-02T13:06:42Z", + "last_commit_oid_default_branch": "c4f07d1dbe746ed6ced3f847113a7bb32ca8679c", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "Apache License 2.0", + "license_spdx": "Apache-2.0", + "name_with_owner": "hassan11196/devfile-sample-python-basic", + "owner": "hassan11196", + "parent_repo": "devfile-samples/devfile-sample-python-basic", + "parent_url": "https://github.com/devfile-samples/devfile-sample-python-basic", + "primary_language": "Dockerfile", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2023-10-02T13:06:42Z", + "readme_length": 1860, + "readme_text": "# Creating an application with a Python code sample\n\n**Note:** The Python code sample uses the **8081** HTTP port.\n\nBefore you begin creating an application with this `devfile` code sample, it's helpful to understand the relationship between the `devfile` and `Dockerfile` and how they contribute to your build. You can find these files at the following URLs:\n\n* [Python `devfile.yaml`](https://github.com/devfile-samples/devfile-sample-python-basic/blob/main/devfile.yaml)\n* [Python `Dockerfile`](https://github.com/devfile-samples/devfile-sample-python-basic/blob/main/docker/Dockerfile)\n\n1. The `devfile.yaml` file has an [`image-build` component](https://github.com/devfile-samples/devfile-sample-python-basic/blob/main/devfile.yaml#L24-L30) that points to your `Dockerfile`.\n2. The [`docker/Dockerfile`](https://github.com/devfile-samples/devfile-sample-python-basic/blob/main/docker/Dockerfile) contains the instructions you need to build the code sample as a container image.\n3. The `devfile.yaml` [`kubernetes-deploy` component](https://github.com/devfile-samples/devfile-sample-python-basic/blob/main/devfile.yaml#L31-L44) points to a `deploy.yaml` file that contains instructions for deploying the built container image.\n4. The `devfile.yaml` [`deploy` command](https://github.com/devfile-samples/devfile-sample-python-basic/blob/main/devfile.yaml#L46-L59) completes the [outerloop](https://devfile.io/docs/2.2.0/innerloop-vs-outerloop) deployment phase by pointing to the `image-build` and `kubernetes-deploy` components to create your application.\n\n### Additional resources\n* For more information about Python, see [Python](https://www.python.org/).\n* For more information about devfiles, see [Devfile.io](https://devfile.io/).\n* For more information about Dockerfiles, see [Dockerfile reference](https://docs.docker.com/engine/reference/builder/).", + "readme_title": "Creating an application with a Python code sample", + "releases_count": 0, + "repo": "devfile-sample-python-basic", + "repo_name": "devfile-sample-python-basic", + "stars": 0, + "topics": [], + "updated_at": "2023-10-04T12:52:45Z", + "url": "https://github.com/hassan11196/devfile-sample-python-basic", + "watchers": 0 + } + ], + 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"path": "tests", + "size": 0, + "type": "dir" + }, + { + "path": "tox.ini", + "size": 981, + "type": "file" + } + ], + "files_total_count": 72, + "first_commit_date_default_branch": "2013-06-10T21:53:07Z", + "forks": 0, + "has_ci": true, + "has_contributing": false, + "has_docker": true, + "has_docs": true, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": true, + "homepage_url": "https://marcgibbons.com/django-rest-swagger/", + "input_url": "https://github.com/hassan11196/django-rest-swagger", + "is_archived": false, + "is_fork": true, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Python", + "size": 77400 + }, + { + "language": "JavaScript", + "size": 2808 + }, + { + "language": "HTML", + "size": 2453 + }, + { + "language": "CSS", + "size": 837 + }, + { + "language": "Dockerfile", + "size": 289 + }, + { + "language": "Shell", + "size": 135 + } + ], + "last_commit_date_default_branch": "2020-04-12T08:38:00Z", + "last_commit_oid_default_branch": "cfe92e7bf24197a950c270fad71341cda4040644", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "BSD 2-Clause \"Simplified\" License", + "license_spdx": "BSD-2-Clause", + "name_with_owner": "hassan11196/django-rest-swagger", + "owner": "hassan11196", + "parent_repo": "marcgibbons/django-rest-swagger", + "parent_url": "https://github.com/marcgibbons/django-rest-swagger", + "primary_language": "Python", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2020-04-12T08:38:01Z", + "readme_length": 2870, + "readme_text": "# Django REST Swagger: deprecated (2019-06-04)\n\nThis project is no longer being maintained. Please consider **drf-yasg** as an alternative/successor. I haven't personally used it, but it looks feature-complete and is actively maintained.\n\n### https://github.com/axnsan12/drf-yasg\n\nThanks for all the support and contributions over the years. Special thanks to [Lights on Software](https://lightsonsoftware.com/), [Lincoln Loop](https://lincolnloop.com/) and BNOTIONS for generously donating time to work on this project :heart:.\n\n---\n\n[![build-status-badge]][build-status]\n[![codecov](https://codecov.io/gh/marcgibbons/django-rest-swagger/branch/master/graph/badge.svg)](https://codecov.io/gh/marcgibbons/django-rest-swagger)\n[![pypi-version]][pypi]\n\n\n[![Deploy](https://www.herokucdn.com/deploy/button.svg)](https://heroku.com/deploy)\n\n\n#### An API documentation generator for Swagger UI and Django REST Framework\n\nFull documentation: http://marcgibbons.github.io/django-rest-swagger/\n\n\n## Installation\n\n1. `pip install django-rest-swagger`\n\n2. Add `rest_framework_swagger` to your `INSTALLED_APPS` setting:\n\n ```python\n INSTALLED_APPS = (\n ...\n 'rest_framework_swagger',\n )\n ```\n\n## Rendering Swagger Specification and Documentation\n\nThis package ships with two renderer classes:\n\n1. `OpenAPIRenderer` generates the OpenAPI (fka Swagger) JSON schema specification. This renderer will be presented if:\n - `Content-Type: application/openapi+json` is specified in the headers.\n - `?format=openapi` is passed as query param\n2. `SwaggerUIRenderer` generates the Swagger UI and requires the `OpenAPIRenderer`\n\n\n### Quick Start Example:\n```python\nfrom django.conf.urls import url\nfrom rest_framework_swagger.views import get_swagger_view\n\nschema_view = get_swagger_view(title='Pastebin API')\n\nurlpatterns = [\n url(r'^$', schema_view)\n]\n```\n\n## Requirements\n* Django 1.8+\n* Django REST framework 3.5.1+\n* Python 2.7, 3.5, 3.6\n\n\n## Testing\n\n- Run `$ tox` to execute the test suite against all supported environments.\n- Run `./runtests.py` to run the test suite within the current environment.\n\n## Bugs & Contributions\nPlease report bugs by opening an issue\n\nContributions are welcome and are encouraged!\n\n## Special Thanks\nMany thanks to Tom Christie & all the contributors who have developed [Django REST Framework](http://django-rest-framework.org/)\n\n\n[build-status-badge]: https://travis-ci.org/marcgibbons/django-rest-swagger.svg?branch=master\n[build-status]: https://travis-ci.org/marcgibbons/django-rest-swagger\n[pypi-version]: https://img.shields.io/pypi/v/django-rest-swagger.svg\n[pypi]: https://pypi.python.org/pypi/django-rest-swagger\n[license]: https://pypi.python.org/pypi/django-rest-swagger/\n[docs-badge]: https://readthedocs.io/projects/django-rest-swagger/badge/\n[docs]: http://django-rest-swagger.readthedocs.io/", + "readme_title": "Django REST Swagger: deprecated (2019-06-04)", + "releases_count": 0, + "repo": "django-rest-swagger", + "repo_name": "django-rest-swagger", + "stars": 1, + "topics": [], + "updated_at": "2020-05-06T04:39:03Z", + "url": "https://github.com/hassan11196/django-rest-swagger", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + 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https://deep-learning-with-pytorch.github.io/dlwpt-code/errata.html\n\n## About Deep Learning with PyTorch\n\nThis book has the aim of providing the foundations of deep learning with PyTorch and\nshowing them in action in a real-life project. We strive to provide the key concepts underlying deep learning and show how PyTorch puts them in the hands of practitioners. In\nthe book, we try to provide intuition that will support further exploration, and in doing\nso we selectively delve into details to show what is going on behind the curtain.\nDeep Learning with PyTorch doesn’t try to be a reference book; rather, it’s a conceptual companion that will allow you to independently explore more advanced material\nonline. As such, we focus on a subset of the features offered by PyTorch. The most\nnotable absence is recurrent neural networks, but the same is true for other parts of\nthe PyTorch API.\n\n## Who should read this book\n\nThis book is meant for developers who are or aim to become deep learning practitioners and who want to get acquainted with PyTorch. We imagine our typical reader\nto be a computer scientist, data scientist, or software engineer, or an undergraduateor-later student in a related program. Since we don’t assume prior knowledge of deep\nlearning, some parts in the first half of the book may be a repetition of concepts that\nare already known to experienced practitioners. For those readers, we hope the exposition will provide a slightly different angle to known topics.\n We expect readers to have basic knowledge of imperative and object-oriented programming. Since the book uses Python, you should be familiar with the syntax and\noperating environment. Knowing how to install Python packages and run scripts on\nyour platform of choice is a prerequisite. Readers coming from C++, Java, JavaScript,\nRuby, or other such languages should have an easy time picking it up but will need to\ndo some catch-up outside this book. Similarly, being familiar with NumPy will be useful, if not strictly required. We also expect familiarity with some basic linear algebra,\nsuch as knowing what matrices and vectors are and what a dot product is.\n\n## About the authors\n\nEli Stevens has spent the majority of his career working at startups in Silicon Valley,\nwith roles ranging from software engineer (making enterprise networking appliances)\nto CTO (developing software for radiation oncology). At publication, he is working\non machine learning in the self-driving-car industry.\n\nLuca Antiga worked as a researcher in biomedical engineering in the 2000s, and\nspent the last decade as a cofounder and CTO of an AI engineering company. He has\ncontributed to several open source projects, including the PyTorch core. He recently\ncofounded a US-based startup focused on infrastructure for data-defined software.\n\nThomas Viehmann is a machine learning and PyTorch specialty trainer and consultant based in Munich, Germany, and a PyTorch core developer. 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"[![Open Source Love](https://badges.frapsoft.com/os/v1/open-source.svg?v=103)](https://github.com/ellerbrock/open-source-badges/)\n[](https://join.slack.com/t/firstcontributors/shared_invite/enQtNjkxNzQwNzA2MTMwLTVhMWJjNjg2ODRlNWZhNjIzYjgwNDIyZWYwZjhjYTQ4OTBjMWM0MmFhZDUxNzBiYzczMGNiYzcxNjkzZDZlMDM)\n[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)\n[![Open Source Helpers](https://www.codetriage.com/roshanjossey/first-contributions/badges/users.svg)](https://www.codetriage.com/roshanjossey/first-contributions)\n\n\n# First Contributions\n\nIt's hard. It's always hard the first time you do something. Especially when you are collaborating, making mistakes isn't a comfortable thing. We wanted to simplify the way new open-source contributors learn & contribute for the first time.\n\nReading articles & watching tutorials can help, but what's better than actually doing the stuff in a practice environment? This project aims at providing guidance & simplifying the way beginners make their first contribution. If you are looking to make your first contribution, follow the steps below.\n\n#### *If you're not comfortable with command line, [here are tutorials using GUI tools.]( #tutorials-using-other-tools )*\n\n#### *Read this in [other languages](translations/Translations.md).*\n\n[:bangladesh:](translations/README.bn.md)\n[🇧🇬](translations/README.bg.md)\n[🇧🇷](translations/README.pt_br.md)\n[](translations/README.ca.md)\n[🇨🇳](translations/README.chs.md)\n[🇨🇿](translations/README.cs.md)\n[🇩🇪](translations/README.de.md)\n[🇩🇰](translations/README.da.md)\n[🇪🇬](translations/README.eg.md)\n[🇪🇸](translations/README.es.md)\n[🇫🇷](translations/README.fr.md)\n[🏴](translations/README.gl.md)\n[🇬🇷](translations/README.gr.md)\n[🇭🇺](translations/README.hu.md)\n[🇮🇩](translations/README.id.md)\n[🇮🇱](translations/README.hb.md)\n[🇮🇳](translations/Translations.md)\n[🇮🇷](translations/README.fa.md)\n[🇮🇷](translations/README.fa.en.md)\n[🇮🇹](translations/README.it.md)\n[🇯🇵](translations/README.ja.md)\n[🇰🇪](translations/README.kws.md)\n[🇰🇷 🇰🇵](translations/README.ko.md)\n[🇱🇹](translations/README.lt.md)\n[🇲🇩 🇷🇴](translations/README.ro.md)\n[🇲🇲](translations/README.mm_unicode.md)\n[🇲🇰](translations/README.mk.md)\n[🇲🇽](translations/README.mx.md)\n[🇲🇾](translations/README.my.md)\n[🇳🇱](translations/README.nl.md)\n[🇳🇬](translations/README.igb.md)\n[🇳🇵](translations/README.np.md)\n[🇵🇭](translations/README.tl.md)\n[](translations/README.en-pirate.md)\n[🇵🇰](translations/README.ur.md)\n[🇵🇱](translations/README.pl.md)\n[🇵🇹](translations/README.pt-pt.md)\n[🇷🇺](translations/README.ru.md)\n[🇸🇦](translations/README.ar.md)\n[🇸🇪](translations/README.se.md)\n[:slovakia:](translations/README.slk.md)\n[:slovenia:](translations/README.sl.md)\n[🇹🇭](translations/README.th.md)\n[🇹🇷](translations/README.tr.md)\n[🇹🇼](translations/README.cht.md)\n[🇺🇦](translations/README.ua.md)\n[🇻🇳](translations/README.vn.md)\n[🇿🇦](translations/README.zul.md)\n[🇿🇦](translations/README.afk.md)\n[🇰🇪](translations/README.kws.md)\n[🇳🇬](translations/README.igb.md)\n[🇱🇻](translations/README.lv.md)\n[GUJ](translations/README.guj.md)\n\n\n\n\"fork\n\nIf you don't have git on your machine, [install it]( https://help.github.com/articles/set-up-git/).\n\n## Fork this repository\n\nFork this repository by clicking on the fork button on the top of this page.\nThis will create a copy of this repository in your account.\n\n## Clone the repository\n\n\"clone\n\nNow clone the forked repository to your machine. Go to your GitHub account, open the forked repository, click on the clone button and then click the *copy to clipboard* icon.\n\nOpen a terminal and run the following git command:\n\n```\ngit clone \"url you just copied\"\n```\nwhere \"url you just copied\" (without the quote marks) is the url to this repository (your fork of this project). See the previous steps to obtain the url.\n\n\"copy\n\nFor example:\n```\ngit clone https://github.com/this-is-you/first-contributions.git\n```\nwhere `this-is-you` is your GitHub username. Here you're copying the contents of the first-contributions repository on GitHub to your computer.\n\n## Create a branch\n\nChange to the repository directory on your computer (if you are not already there):\n\n```\ncd first-contributions\n```\nNow create a branch using the `git checkout` command:\n```\ngit checkout -b \n```\n\nFor example:\n```\ngit checkout -b add-alonzo-church\n```\n(The name of the branch does not need to have the word *add* in it, but it's a reasonable thing to include because the purpose of this branch is to add your name to a list.)\n\n## Make necessary changes and commit those changes\n\nNow open `Contributors.md` file in a text editor, add your name to it. Don't add it at the beginning or end of the file. Put it anywhere in between. Now, save the file.\n\n\"git\n\n\nIf you go to the project directory and execute the command `git status`, you'll see there are changes.\n\n\nAdd those changes to the branch you just created using the `git add` command:\n\n```\ngit add Contributors.md\n```\n\nNow commit those changes using the `git commit` command:\n```\ngit commit -m \"Add to Contributors list\"\n```\nreplacing `` with your name.\n\n## Push changes to GitHub\n\nPush your changes using the command `git push`:\n```\ngit push origin \n```\nreplacing `` with the name of the branch you created earlier.\n\n## Submit your changes for review\n\nIf you go to your repository on GitHub, you'll see a `Compare & pull request` button. Click on that button.\n\n\"create\n\nNow submit the pull request.\n\n\"submit\n\nSoon I'll be merging all your changes into the master branch of this project. You will get a notification email once the changes have been merged.\n\n## Where to go from here?\n\nCongrats! You just completed the standard _fork -> clone -> edit -> PR_ workflow that you'll encounter often as a contributor!\n\nCelebrate your contribution and share it with your friends and followers by going to [web app](https://firstcontributions.github.io/#social-share).\n\nYou could join our slack team in case you need any help or have any questions. [Join slack team](https://join.slack.com/t/firstcontributors/shared_invite/enQtNjkxNzQwNzA2MTMwLTVhMWJjNjg2ODRlNWZhNjIzYjgwNDIyZWYwZjhjYTQ4OTBjMWM0MmFhZDUxNzBiYzczMGNiYzcxNjkzZDZlMDM).\n\nNow let's get you started with contributing to other projects. We've compiled a list of projects with easy issues you can get started on. Check out [the list of projects in the web app](https://firstcontributions.github.io/#project-list).\n\n### [Additional material](additional-material/git_workflow_scenarios/additional-material.md)\n\n\n## Tutorials Using Other Tools\n\n|\"GitHub|\"Visual|\"GitKraken\"|\"VS|\"Sourcetree|\n|---|---|---|---|---|\n|[GitHub Desktop](github-desktop-tutorial.md)|[Visual Studio 2017](github-windows-vs2017-tutorial.md)|[GitKraken](gitkraken-tutorial.md)|[Visual Studio Code](github-windows-vs-code-tutorial.md)|[Atlassian Sourcetree](sourcetree-macos-tutorial.md)|", + "readme_title": "First Contributions", + "releases_count": 0, + "repo": "first-contributions", + "repo_name": "first-contributions", + "stars": 0, + "topics": [], + "updated_at": "2020-01-04T16:44:41Z", + "url": "https://github.com/hassan11196/first-contributions", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/first-contributions" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/flask-tenants" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 85, + "contributors_count": 2, + "contributors_top": [ + { + "contributions": 83, + "html_url": "https://github.com/therealwokewok", + "login": "therealwokewok" + }, + { + "contributions": 2, + "html_url": null, + "login": "Gabe Rust" + } + ], + "created_at": "2024-07-12T00:24:49Z", + "default_branch": "main", + "description": "Multitenancy solution using Flask, SQLAlchemy, and PostgreSQL. Please join our Discord server at https://discord.gg/3AV2GkcB2Q.", + "dirs_total_count": 3, + "files_root_entries": [ + { + "path": ".github", + "size": 0, + "type": "dir" + }, + { + "path": "LICENSE", + "size": 1070, + "type": "file" + }, + { + "path": "README.md", + "size": 6830, + "type": "file" + }, + { + "path": "SECURITY.md", + "size": 3658, + "type": "file" + }, + { + "path": "flask_tenants", + "size": 0, + "type": "dir" + }, + { + "path": "manifest.in", + "size": 34, + "type": "file" + }, + { + "path": "setup.py", + "size": 999, + "type": "file" + } + ], + "files_total_count": 11, + "first_commit_date_default_branch": "2024-06-26T00:27:14Z", + "forks": 0, + "has_ci": true, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/flask-tenants", + "is_archived": false, + "is_fork": true, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Python", + "size": 14470 + } + ], + "last_commit_date_default_branch": "2024-07-03T23:59:50Z", + "last_commit_oid_default_branch": "e93cc425823f4d112bc90cc3455d9f6ccbfb50cd", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "MIT License", + "license_spdx": "MIT", + "name_with_owner": "hassan11196/flask-tenants", + "owner": "hassan11196", + "parent_repo": "cory-cline-netspi/flask-tenants", + "parent_url": "https://github.com/cory-cline-netspi/flask-tenants", + "primary_language": "Python", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2024-07-12T00:37:02Z", + "readme_length": 6828, + "readme_text": "# Flask Tenants\n\nFlask Tenants is a Flask extension for multi-tenancy support using subdomains and SQLAlchemy schemas. The `MultiTenancyMiddleware` extracts the tenant from the request host and switches the database schema accordingly. If no tenant is extracted, it defaults to the public schema.\n\n## Installation\n\n```bash\npip install flask-tenants\n```\n\n## Database Preparation\n\n1. Create a new PostgreSQL database (if not already created):\n\n```sql\nCREATE DATABASE flask_tenants;\n```\n\n1. Connect to the database and create the public schema and extension for UUID generation:\n\n```sql\n\\c flask_tenants\nCREATE SCHEMA IF NOT EXISTS public;\n```\n\n1. Ensure your database user has the necessary privileges to create schemas:\n\n```sql\nGRANT ALL PRIVILEGES ON DATABASE \"flask_tenants\" to your_user;\n```\n\n## Usage\n\n### Basic Setup\n\nCreate a Flask application and initialize SQLAlchemy. Set up the multi-tenancy middleware.\n\n```python\nfrom flask import Flask\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_tenants import MultiTenancyMiddleware\nfrom flask_tenants import init_app as tenants_init_app, create_tenancy\nfrom public.models import Tenant, Domain\n\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI'] = 'postgresql://user:password@localhost/dbname'\ndb = SQLAlchemy(app)\n\n# Initialize the tenancy\ntenants_init_app(app, tenant_model=Tenant, domain_model=Domain)\n\n# Set up tenancy middleware\ntenancy = create_tenancy(app, db, tenant_url_prefix='/_tenant')\n```\n\n#### tenant_url_prefix\n\nThis is optional, but the default is quite long. It is recommended to set this to a default value that will not be used in any other route. The module uses this on the backend to route tenant-scoped requests and handles it invisibly to prevent the need for a */tenant/* route prefixing all tenant-scoped requests.\n\n### Models\n\n#### Tenancy models\n\nDefine your tenant and domain models by inheriting from `BaseTenant` and `BaseDomain`.\n\n```python\nfrom flask_tenants import BaseTenant, BaseDomain, db\n\nclass Tenant(BaseTenant):\n __tablename__ = 'tenants'\n phone_number = db.Column(db.String(20), nullable=True)\n address = db.Column(db.String(255), nullable=True)\n deactivated = db.Column(db.Boolean(), nullable=False, default=False)\n\nclass Domain(BaseDomain):\n __tablename__ = 'domains'\n tenant_id = db.Column(db.Integer, db.ForeignKey('tenants.id'), nullable=False)\n tenant_name = db.Column(db.String(128), nullable=False)\n domain_name = db.Column(db.String(255), unique=True, nullable=False)\n is_primary = db.Column(db.Boolean, default=False, nullable=False)\n```\n\n#### BaseTenant\n\n`BaseTenant` provides *name*, *created_at*, and *updated_at* attributes.\n\n```python\nclass BaseTenant(db.Model):\n __abstract__ = True\n id = db.Column(db.Integer, primary_key=True, autoincrement=True, unique=True) # Ensure unique constraint\n name = db.Column(db.String(128), unique=True, nullable=False)\n created_at = db.Column(db.DateTime, nullable=False, default=db.func.current_timestamp())\n updated_at = db.Column(db.DateTime, nullable=False, default=db.func.current_timestamp(),\n onupdate=db.func.current_timestamp())\n```\n\n#### BaseDomain\n\n`BaseDomain` provides *tenant_name*, *domain_name*, and *is_primary* attributes.\n\n```python\nclass BaseDomain(db.Model):\n __abstract__ = True\n id = db.Column(db.Integer, primary_key=True, autoincrement=True)\n tenant_name = db.Column(db.String(128), db.ForeignKey('tenants.name'), nullable=False)\n domain_name = db.Column(db.String(255), unique=True, nullable=False)\n is_primary = db.Column(db.Boolean, default=False, nullable=False)\n```\n\n#### Tenant Deactivation\nIf you'd like to be able to deactivate a tenant without deleting it, \nfor example if a SaaS customer forgets to pay their bill, you can optionally\nadd a `deactivated` field to your tenant model:\n\n```python\nclass Tenant(BaseTenant):\n __tablename__ = 'tenants'\n # ...\n deactivated = db.Column(db.Boolean(), nullable=False, default=False)\n```\n\nFlask-Tenants will check if this field exists early in the request lifecycle and abort \nthe request early with a `TenantActivationError` if it is `True`.\n\n### Error Handling\n\nFlask-Tenants middleware raises two custom exceptions that need to be handled by the end developer. Namely, `TenantActivationError` (if deactivated is utilized in Tenant model) and `TenantNotFoundError`. These can be handled in a custom errors.py file, as shown:\n\n```python\nfrom flask import jsonify\nfrom flask_tenants.exceptions import TenantNotFoundError, TenantActivationError\n\n\ndef register_error_handlers(app):\n @app.errorhandler(TenantNotFoundError)\n def handle_tenant_not_found(error):\n response = jsonify({\n 'error': 'TenantNotFoundError',\n 'message': 'The requested tenant was not found.'\n })\n response.status_code = 404\n return response\n\n @app.errorhandler(TenantActivationError)\n def handle_tenant_activation_error(error):\n response = jsonify({\n 'error': 'TenantActivationError',\n 'message': 'The requested tenant is deactivated.'\n })\n response.status_code = 403\n return response\n```\n\nAdditionally, in app.py:\n\n```python\ndef create_app():\n app = Flask(__name__)\n app.config.from_object(Config)\n\n # Register error handlers\n register_error_handlers(app)\n\n tenants_init_app(app, tenant_model=Tenant, domain_model=Domain)\n ...\n```\n\nHere is the full list of custom exceptions provided by the Flask-Tenants module:\n\n- SchemaCreationError\n- SchemaAlreadyExistsError\n- SchemaDoesNotExistError\n- SchemaRenameError\n- SchemaDropError\n- TableCreationError\n- TenantActivationError\n- TenantNotFoundError\n\n### Tenant scoped models\n\nDefine tenant scoped models by inheriting from `BaseTenantModel` and setting the proper `info` table argument.\n\n```python\nfrom flask_tenants.models import db, BaseTenantModel\n\n\nclass Tank(BaseTenantModel):\n __abstract__ = False\n __tablename__ = 'tanks'\n __table_args__ = {'info': {'tenant_specific': True}}\n id = db.Column(db.Integer, primary_key=True, autoincrement=True)\n name = db.Column(db.String(128), nullable=True)\n capacity = db.Column(db.Float, nullable=True)\n location = db.Column(db.String(255), nullable=True)\n```\n\n#### BaseTenantModel\n\n`BaseTenantModel` provides no attributes.\n\n```python\nclass BaseTenantModel(db.Model):\n __abstract__ = True\n __table_args__ = ({'schema': 'tenant'})\n```\n\n## Implementing CRUD Operations\n\nThe `g.db_session` object must be used for all database accesses for search_path schema to automatically apply.\n\n```python\nfrom flask import g\n\ntanks = g.db_session.query(Tank).all()\n```\n\n### Example and Demos\nYou can find an example multi-tenant blog application at [this repo](https://github.com/Flask-Tenants/demo_app)", + "readme_title": "Flask Tenants", + "releases_count": 0, + "repo": "flask-tenants", + "repo_name": "flask-tenants", + "stars": 0, + "topics": [], + "updated_at": "2024-07-12T00:24:50Z", + "url": "https://github.com/hassan11196/flask-tenants", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/flask-tenants" + }, + { + "awards": [], + 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"readme_length": 1771, + "readme_text": "# gatsby-transformer-ipynb\n\nParses [jupyter](https://jupyter.org/) notebook files.\n\n\n\n## Install\n\n`npm install --save @gatsby-contrib/gatsby-transformer-ipynb`\n\n## How to use\n\n```javascript\n// In your gatsby-config.js\nplugins: [`@gatsby-contrib/gatsby-transformer-ipynb`];\n```\n\n## Parsing algorithm\n\nIt recognizes files with the `ipynb` extension.\n\nEach notebook file is parsed into a node of type `JupyterNotebook`.\n\nThis plugin adds additional fields to the `JupyterNotebook` GraphQL type\nincluding:\n\n- `html`: html string created using the react component `NotebookRender` from [`@gatsby-contrib/notebook-render`](https://github.com/nteract/nteract/tree/master/packages/notebook-render).\n- `metadata`: jupyter notebooks can embed metadata to indicate authors, titles...\n- `json`: the json notebook code converted into a javascript object with `JSON.parse`.\n- `internal.content`: contains the raw notebook code, it can be used to feed the react component `NotebookPreview` from [`@gatsby-contrib/notebook-preview`](https://github.com/nteract/nteract/tree/master/packages/notebook-preview).\n\n## How to query\n\nA sample GraphQL query to get JupyterNotebook nodes:\n\n```graphql\n{\n query\n JupyterQuery {\n allJupyterNotebook {\n edges {\n node {\n id\n metadata {\n kernelspec {\n name\n language\n display_name\n }\n }\n html\n json {\n nbformat\n nbformat_minor\n cells {\n cell_type\n execution_count\n }\n }\n internal {\n content\n }\n }\n }\n }\n }\n}\n```", + "readme_title": "gatsby-transformer-ipynb", + "releases_count": 4, + "repo": "gatsby-transformer-ipynb", + "repo_name": "gatsby-transformer-ipynb", + "stars": 0, + "topics": [], + "updated_at": "2021-10-29T10:14:23Z", + "url": "https://github.com/hassan11196/gatsby-transformer-ipynb", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + 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433, + "type": "file" + }, + { + "path": "README.md", + "size": 818, + "type": "file" + }, + { + "path": "app.py", + "size": 2627, + "type": "file" + }, + { + "path": "requirements.txt", + "size": 86, + "type": "file" + }, + { + "path": "streamlit_app.py", + "size": 4416, + "type": "file" + } + ], + "files_total_count": 7, + "first_commit_date_default_branch": "2024-11-10T15:04:58Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": true, + "has_docs": false, + "has_license_file": false, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/gemini-alchemy", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Python", + "size": 7043 + }, + { + "language": "Dockerfile", + "size": 433 + } + ], + "last_commit_date_default_branch": "2024-11-11T16:32:39Z", + "last_commit_oid_default_branch": "d0ec3321f4f7980351e69ec5d33da46fdd8b6261", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/gemini-alchemy", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "Python", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2024-11-11T16:32:40Z", + "readme_length": 817, + "readme_text": "# gemini-alchemy\n\nMind mapping is an enduringly popular method for recording, organizing, and presenting information. With Gemini and other models we are able to quickly visualize concepts but mindmapping is the area which still needs some work. We realized that it's super hard to create beautifully appealing mindmaps on complex concepts and data. Therefore, we came up with **Gemini-Alchemy**, which is a tool to convert abstract ideas into a visually appealing mindmap format.\n\n## Guide to locally run the code\n\n- Create and activate venv\n- install requirements by 'pip install -r requirements.txt'\n- first run the backend\n - Uvicorn app:app\n- then run the streamlit app:\n - Streamlit run streamlit_app.py\n- you should also have a .env file with all necessary variables in it such as API_URL, and GEMINI_API_KEY", + "readme_title": "gemini-alchemy", + "releases_count": 0, + "repo": "gemini-alchemy", + "repo_name": "gemini-alchemy", + "stars": 0, + "topics": [], + "updated_at": "2024-11-11T16:32:43Z", + "url": "https://github.com/hassan11196/gemini-alchemy", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/gemini-alchemy" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/go-dialogflow-basic" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 3, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 3, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + } + ], + "created_at": "2020-02-12T20:41:58Z", + "default_branch": "master", + "description": "A Basic Chatbot Interface with Dialogflow using go", + "dirs_total_count": 3, + "files_root_entries": [ + { + "path": ".gitignore", + "size": 5, + "type": "file" + }, + { + "path": "README.md", + "size": 438, + "type": "file" + }, + { + "path": "export_env.ps1", + "size": 88, + "type": "file" + }, + { + "path": "src", + "size": 0, + "type": "dir" + } + ], + "files_total_count": 5, + "first_commit_date_default_branch": "2020-02-12T20:42:23Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/go-dialogflow-basic", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Go", + "size": 3744 + }, + { + "language": "PowerShell", + "size": 88 + } + ], + "last_commit_date_default_branch": "2020-03-18T16:46:48Z", + "last_commit_oid_default_branch": "c473ed29b4006f985f4bd04ae499efb776607548", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/go-dialogflow-basic", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "Go", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2020-03-18T16:47:01Z", + "readme_length": 438, + "readme_text": "# Basic Go Socket Programming interacting With DialogFlow API\n\nTo run server :\n\n go run .\\src\\server\\server.go \n\nTo run client :\n\n go run .\\src\\client\\client.go \n\nP.S : enclose messages from client - cli in inverted commas(e.g \"Hello\") or else the server would crash.\n\n\n## References :\n\nhttps://systembash.com/a-simple-go-tcp-server-and-tcp-client/", + "readme_title": "Basic Go Socket Programming interacting With DialogFlow API", + "releases_count": 0, + "repo": "go-dialogflow-basic", + "repo_name": "go-dialogflow-basic", + "stars": 0, + "topics": [ + "go", + "goalng", + "dialogflow" + ], + "updated_at": "2020-03-18T16:47:03Z", + "url": "https://github.com/hassan11196/go-dialogflow-basic", + "watchers": 1 + } + ], + "hackathon_location": null, + 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"files_total_count": 7, + "first_commit_date_default_branch": "2020-02-11T19:10:44Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/go-starter", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Go", + "size": 2192 + } + ], + "last_commit_date_default_branch": "2020-02-24T18:51:38Z", + "last_commit_oid_default_branch": "3614755d0193aae42f65e698f22c3f64653a5738", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/go-starter", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "Go", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2020-02-24T18:51:58Z", + "readme_length": 1293, + "readme_text": "# Go Starter\nBasic Hello World Programs for Go.\n \n \n### Run a Go Program:\n\n go run hello-world.go\n### Create a Binary:\n\n go build hello-world.go\n \n \n\n\n### Work-space directory structure\n\nA Go workspace must have three sub-directories.\n\n /bin --> binary exectutables generated by go install\n /pkg --> Compiled Package Objects\n /src --> Source Code\n \n \n - package object save compilation time. 3rd party packages are also installed in /pkg directory.\n - go install creates packages objects of any imported packages. go install internally calls go build\n\n### Installing Third Party Package:\n\n go get github.com/jinzhu/gorm\n\n### Creating Binaries for Workspaces :\n\t\n /*\n This Requires setting up the GOPATH and GOBIN environment variables. \n where \n GOPATH = .../GoWorkspaceName\n GOBIN = .../GoWorkspaceName/bin\n */\n go install hello\n\t/* \n\tAbove command will build main/src/hello/main.go \n\tA binary file will be generated inside bin directory with the name of the package.\n\t */\n\n\n \n \n\n## References :\n\n - [https://medium.com/rungo/working-in-go-workspace-3b0576e0534a](https://medium.com/rungo/working-in-go-workspace-3b0576e0534a)\n - [https://gobyexample.com/](https://gobyexample.com/)\n - [https://awesome-go.com/](https://awesome-go.com/)", + "readme_title": "Go Starter", + "releases_count": 0, + "repo": "go-starter", + "repo_name": "go-starter", + "stars": 0, + "topics": [ + "golang", + "go", + "hello-world" + ], + "updated_at": "2020-02-24T18:52:00Z", + "url": "https://github.com/hassan11196/go-starter", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/go-starter" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/hassan11196" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 85657, + "contributors_count": 3, + "contributors_top": [ + { + "contributions": 85531, + "html_url": "https://github.com/apps/github-actions", + "login": "github-actions[bot]" + }, + { + "contributions": 99, + "html_url": null, + "login": "readme-bot" + }, + { + "contributions": 27, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + } + ], + "created_at": "2019-04-18T09:10:59Z", + "default_branch": "master", + "description": null, + "dirs_total_count": 1, + "files_root_entries": [ + { + "path": "README.md", + "size": 10720, + "type": "file" + }, + { + "path": "charts", + "size": 0, + "type": "dir" + } + ], + "files_total_count": 2, + "first_commit_date_default_branch": "2020-08-15T04:13:01Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/hassan11196", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [], + "last_commit_date_default_branch": "2026-01-08T11:37:06Z", + "last_commit_oid_default_branch": "8c1e7228c85ecf590528a09cd393363fb80ae4d0", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/hassan11196", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": null, + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2026-03-31T14:56:28Z", + "readme_length": 10709, + "readme_text": "Hi, I'm M.Hassan Ahmed.\nMost of my recent contribitutions are on CERN Gitlab. https://gitlab.cern.ch/ahmedmu\n\n[hassanahmed.net](https://hassanahmed.net) | [github.com/hassan11196](https://github.com/hassan11196 ) | [gitlab.cern.ch/ahmedmu](https://gitlab.cern.ch/ahmedmu) | [linkedin.com/in/hassan11196](https://linkedin.com/in/hassan11196) | [Google Scholar](https://scholar.google.com/citations?user=n0hS0rYAAAAJ)\n\n# 💻Tech Stack\n![C](https://img.shields.io/badge/c-%2300599C.svg?style=for-the-badge&logo=c&logoColor=white) ![C++](https://img.shields.io/badge/c++-%2300599C.svg?style=for-the-badge&logo=c%2B%2B&logoColor=white) ![CSS3](https://img.shields.io/badge/css3-%231572B6.svg?style=for-the-badge&logo=css3&logoColor=white) ![Dart](https://img.shields.io/badge/dart-%230175C2.svg?style=for-the-badge&logo=dart&logoColor=white) ![Go](https://img.shields.io/badge/go-%2300ADD8.svg?style=for-the-badge&logo=go&logoColor=white) ![GraphQL](https://img.shields.io/badge/-GraphQL-E10098?style=for-the-badge&logo=graphql&logoColor=white) ![Java](https://img.shields.io/badge/java-%23ED8B00.svg?style=for-the-badge&logo=java&logoColor=white) ![JavaScript](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E) ![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54) ![R](https://img.shields.io/badge/r-%23276DC3.svg?style=for-the-badge&logo=r&logoColor=white) ![TypeScript](https://img.shields.io/badge/typescript-%23007ACC.svg?style=for-the-badge&logo=typescript&logoColor=white) ![AWS](https://img.shields.io/badge/AWS-%23FF9900.svg?style=for-the-badge&logo=amazon-aws&logoColor=white) 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![NumPy](https://img.shields.io/badge/numpy-%23013243.svg?style=for-the-badge&logo=numpy&logoColor=white) ![Pandas](https://img.shields.io/badge/pandas-%23150458.svg?style=for-the-badge&logo=pandas&logoColor=white) ![Plotly](https://img.shields.io/badge/Plotly-%233F4F75.svg?style=for-the-badge&logo=plotly&logoColor=white) ![PyTorch](https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?style=for-the-badge&logo=PyTorch&logoColor=white) ![TensorFlow](https://img.shields.io/badge/TensorFlow-%23FF6F00.svg?style=for-the-badge&logo=TensorFlow&logoColor=white) ![Docker](https://img.shields.io/badge/docker-%230db7ed.svg?style=for-the-badge&logo=docker&logoColor=white) ![ElasticSearch](https://img.shields.io/badge/-ElasticSearch-005571?style=for-the-badge&logo=elasticsearch) ![Swagger](https://img.shields.io/badge/-Swagger-%23Clojure?style=for-the-badge&logo=swagger&logoColor=white) ![Notion](https://img.shields.io/badge/Notion-%23000000.svg?style=for-the-badge&logo=notion&logoColor=white)\n\n\n\n\n\n\"\"\n\"\"\n\"\"\n\"\"\n\"\"\n\"\"\n\"\"\n\n\n\n\n## 🌐Socials\n [![LinkedIn](https://img.shields.io/badge/LinkedIn-%230077B5.svg?logo=linkedin&logoColor=white)](https://linkedin.com/in/hassan11196) \n\n\n---", + "readme_title": "💻Tech Stack", + "releases_count": 0, + "repo": "hassan11196", + "repo_name": "hassan11196", + "stars": 0, + "topics": [], + "updated_at": "2026-01-08T11:37:10Z", + "url": "https://github.com/hassan11196/hassan11196", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/hassan11196" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + 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"issues_total": 0, + "languages_top": [ + { + "language": "Lua", + "size": 55753 + }, + { + "language": "Shell", + "size": 1971 + } + ], + "last_commit_date_default_branch": "2020-04-13T15:24:26Z", + "last_commit_oid_default_branch": "31589706dccd384398591e41f73dd64ba107afb7", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "MIT License", + "license_spdx": "MIT", + "name_with_owner": "hassan11196/heroku-kong", + "owner": "hassan11196", + "parent_repo": "heroku/heroku-kong", + "parent_url": "https://github.com/heroku/heroku-kong", + "primary_language": "Lua", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2020-04-13T15:24:27Z", + "readme_length": 18919, + "readme_text": "Kong as a Heroku app\n====================\n\n⚠️ **This project is no longer maintained.**\n\nDeploy [Kong 1.0](https://konghq.com/blog/kong-1-0-ga/) clusters to Heroku Common Runtime and Private Spaces using the [Kong buildpack](https://github.com/heroku/heroku-buildpack-kong/).\n\n⏫ **Upgrading from an earlier version?** See [Upgrade Guide](#user-content-upgrade-guide).\n\n🔬 This is a community proof-of-concept, [MIT license](LICENSE), provided \"as is\", without warranty of any kind.\n\n[![Deploy to Heroku](https://www.herokucdn.com/deploy/button.svg)](https://heroku.com/deploy)\n\n* [Purpose](#user-content-purpose)\n* [Usage](#user-content-usage)\n * [Deploy](#user-content-deploy)\n * [Connect local to Heroku app](#user-content-connect-local-to-heroku-app)\n * [Admin console](#user-content-admin-console)\n * [Admin API](#user-content-admin-api)\n * [The API Key](#user-content-admin-api-key)\n * [Accessing](#user-content-accessing-the-external-admin-api)\n * [Disabling](#user-content-disabling-the-external-admin-api)\n * [Terraform](#user-content-terraform)\n * [Upgrade guide](#user-content-upgrade-guide)\n* [Customization](#user-content-customization)\n * [Configuration](#user-content-configuration)\n * [Kong plugins & additional Lua modules](#user-content-kong-plugins--additional-lua-modules)\n* [Demos](#user-content-demos)\n * [API Rate Limiting](#user-content-demo-api-rate-limiting)\n * [Custom plugin: hello-world-header](#user-content-demo-custom-plugin-hello-world-header)\n * [Custom plugin: API translation, JSON→XML](#user-content-demo-custom-plugin-api-translation-jsonxml)\n* [Development Notes](#user-content-dev-notes)\n * [Learning the Language of Kong](#user-content-learning-the-language-of-kong)\n * [Programming with Lua](#user-content-programming-with-lua)\n * [Local Development](#user-content-local-development)\n * [Requirements](#user-content-requirements)\n * [Clone & connect](#user-content-clone--connect)\n * [Setup](#user-content-setup)\n * [Running](#user-content-running)\n * [Testing](#user-content-testing)\n\nPurpose\n-------\nKong is an extensible [web proxy](https://en.m.wikipedia.org/wiki/Proxy_server) based on [OpenResty](http://openresty.org/en/), a web app framework built on the embedded [Lua language](http://www.lua.org) capabilities of the [Nginx web server](http://nginx.org/en/).\n\nWith Heroku, Kong may be used for a variety of purposes. A few examples:\n\n * unify access control & observability for a suite of microservices\n * enforce request rate & size limits globally, based on the endpoint, or the authenticated consumer\n * create a single management point for routing requests based on DNS hostnames, URL paths, and HTTP headers\n\n🦍 Visit [Kong HQ](https://konghq.com), the official resource for everything Kong.\n\nUsage\n-----\n\n### Deploy\n\nUse the deploy button to create a Kong app in your Heroku account:\n\n[![Deploy to Heroku](https://www.herokucdn.com/deploy/button.svg)](https://heroku.com/deploy)\n\n### Connect local to Heroku app\n\nTo make changes to the Kong app's source, clone and connect this repo (or your own fork) to the Heroku app:\n\n```bash\ngit clone https://github.com/heroku/heroku-kong.git\ncd heroku-kong\n\n# Use the name of the Heroku app:\nheroku git:remote --app $APP_NAME\nheroku info\n```\n\n### Admin console\n\nTo gain local console access to Kong deployed on Heroku, see [ADMIN](ADMIN.md).\n\nConsole access is primarily useful for performing `kong` CLI commands against the deployed app. Most administrative features do not require console access and instead are available through the Kong Admin API.\n\n### Admin API\n\nWhen this app is deployed to Heroku, it automatically provisions a protected, external-facing [loopback proxy](https://docs.konghq.com/1.0.x/secure-admin-api/#kong-api-loopback) to [Kong's Admin API](https://docs.konghq.com/1.0.x/admin-api/), secured by the `KONG_HEROKU_ADMIN_KEY` config var.\n\n#### Admin API key\n\n`KONG_HEROKU_ADMIN_KEY` is generated automatically when this app is [deployed using the automated app setup](#user-content-deploy).\n\nYou can explicitly set a new admin key value:\n\n```bash\nheroku config:set KONG_HEROKU_ADMIN_KEY=xxxxx\n```\n\n⚠️ **Always set a unique, cryptographically strong key value.** A weak admin key may result in the proxy being compromised and abused by malicious actors.\n\n#### Accessing the external Admin API\n\nMake HTTPS requests using a tool like [`curl`](https://curl.haxx.se) or [Paw.cloud](https://paw.cloud):\n\n1. Base URL of the app's [Kong Admin API](https://docs.konghq.com/1.0.x/admin-api/) is `https://$APP_NAME.herokuapp.com/kong-admin`\n2. Set the current [admin key](#user-content-admin-api-key) in the `apikey` HTTP header\n\nFor example, set the current admin key into a local shell variable:\n\n```bash\nKONG_HEROKU_ADMIN_KEY=`heroku config:get KONG_HEROKU_ADMIN_KEY`\n```\n\nNow use the following HTTP request style to interact with the [Kong's Admin API](https://docs.konghq.com/1.0.x/admin-api/):\n\n✏️ *Replace the variable `$APP_NAME` with value for your unique deployment.*\n\n```bash\ncurl -H \"apikey: $KONG_HEROKU_ADMIN_KEY\" https://$APP_NAME.herokuapp.com/kong-admin/status\n```\n\n#### Disabling the external Admin API\n\nIf you prefer to only use the [console-based Admin API](ADMIN.md), then this externally-facing proxy can be disabled:\n\n```bash\ncurl -H \"apikey: $KONG_HEROKU_ADMIN_KEY\" https://$APP_NAME.herokuapp.com/kong-admin/services/kong-admin/routes\n# For the returned Route's `id`,\ncurl -H \"apikey: $KONG_HEROKU_ADMIN_KEY\" -X DELETE https://$APP_NAME.herokuapp.com/kong-admin/routes/$ROUTE_ID\n# Now there's no longer admin access!\n# Finally, clear out the old admin key value.\nheroku config:unset KONG_HEROKU_ADMIN_KEY\n```\n\n### Terraform\n\nKong may be provisioned and configured on Heroku using [Hashicorp Terraform](https://www.terraform.io) and a third-party [Kong provider](https://github.com/kevholditch/terraform-provider-kong).\n\nSee these examples of [Using Terraform with Heroku](https://devcenter.heroku.com/articles/using-terraform-with-heroku):\n\n* [Common Runtime microservices with a unified gateway](https://github.com/mars/terraform-heroku-common-kong-microservices)\n* [Private Spaces microservices with a unified gateway](https://github.com/mars/terraform-heroku-enterprise-kong-microservices)\n\n### Upgrade guide\n\n🚨 **Potentially breaking changes.** Please attempt upgrades on a staging system before upgrading production.\n\n#### The buildpack\n\n[Buildpack v6.0.0](https://github.com/heroku/heroku-buildpack-kong/releases) supports rapid deployments using a\npre-compiled Kong binary. A pre-existing, customized app may require changes continue functioning, if the app explicitly uses the `/app/.heroku` directory prefix.\n\n[Buildpack v7.0.0-rc\\*](https://github.com/heroku/heroku-buildpack-kong/releases) supports Kong 1.0 release candidates. The \"rc\" releases only support upgrading from Kong 0.14, not earlier versions or other release candidates.\n\n[Buildpack v7.0.0](https://github.com/heroku/heroku-buildpack-kong/releases) supports Kong 1.0.\n\n▶️ See [UPGRADING the buildpack](https://github.com/heroku/heroku-buildpack-kong/blob/master/UPGRADING.md).\n\n#### Kong\n\nFirst, see [Kong's official upgrade path](https://github.com/Kong/kong/blob/master/UPGRADE.md).\n\nThen, take into account these facts about how this Kong on Heroku app works:\n\n* this app automatically runs `kong migrations up` for every deployment\n* you may prevent the previous version of Kong from attempting to use the new database schema during the upgrade (this will cause downtime):\n 1. check the current formation size with `heroku ps`\n 1. scale the web workers down `heroku ps:scale web=0`\n 1. [perform the upgrade](https://github.com/Kong/kong/blob/master/UPGRADE.md)\n 1. allow release process to run\n 1. finally restart to the original formation size `heroku ps:scale web=$PREVIOUS_SIZE`\n* once Kong 1.0 is successfully deployed, execute: `\nheroku run \"kong migrations finish --conf /app/config/kong.conf\"`\n\n🏥 Please [open an issue](https://github.com/heroku/heroku-kong/issues), if you encounter problems or have feedback about this process.\n\n\nCustomization\n-------------\nKong may be customized through configuration and plugins.\n\n### Configuration\n\nKong is automatically configured at runtime with a `.profile.d` script:\n\n * renders the `config/kong.conf` file based on:\n * the customizable [`config/kong.conf.etlua`](config/kong.conf.etlua) template\n * values of [config vars, as defined in the buildpack](https://github.com/heroku/heroku-buildpack-kong#user-content-environment-variables)\n * exports environment variables\n * see: `.profile.d/kong-env` in a running dyno\n\nAll file-based config may be overridden by setting `KONG_`-prefixed config vars, e.g. `heroku config:set KONG_LOG_LEVEL=debug`\n\n👓 See: [Kong 1.0 Configuration Reference](https://docs.konghq.com/1.0.x/configuration/)\n\n\n### Kong plugins & additional Lua modules\n\n👓 See: [buildpack usage](https://github.com/heroku/heroku-buildpack-kong#user-content-usage)\n\n\nDemos\n-----\nUsage examples and sample plugins are includes with this Heroku Kong app.\n\n### Demo: [API Rate Limiting](https://getkong.org/plugins/rate-limiting/)\n\nRequest [this Bay Lights API](https://kong-proxy-public.herokuapp.com/bay-lights/lights) more than five times in a minute, and you'll get **HTTP Status 429: API rate limit exceeded**, along with `X-Ratelimit-Limit-Minute` & `X-Ratelimit-Remaining-Minute` headers to help the API consumers regulate their usage.\n\nTry it in your shell terminal:\n```bash\ncurl --head https://kong-proxy-public.herokuapp.com/bay-lights/lights\n# HTTP/1.1 200 OK\ncurl --head https://kong-proxy-public.herokuapp.com/bay-lights/lights\n# HTTP/1.1 200 OK\ncurl --head https://kong-proxy-public.herokuapp.com/bay-lights/lights\n# HTTP/1.1 200 OK\ncurl --head https://kong-proxy-public.herokuapp.com/bay-lights/lights\n# HTTP/1.1 200 OK\ncurl --head https://kong-proxy-public.herokuapp.com/bay-lights/lights\n# HTTP/1.1 200 OK\ncurl --head https://kong-proxy-public.herokuapp.com/bay-lights/lights\n# HTTP/1.1 429\n```\n\nHere's the whole configuration for this API rate limiter:\n\n```bash\ncurl http://localhost:8001/services/ -i -X POST \\\n --data 'name=bay-lights' \\\n --data 'protocol=https' \\\n --data 'port=443' \\\n --data 'host=bay-lights-api-production.herokuapp.com'\n# Note the Service ID returned in previous response, use it in place of `$SERVICE_ID`.\ncurl http://localhost:8001/plugins/ -i -X POST \\\n --data 'name=request-size-limiting' \\\n --data \"config.allowed_payload_size=8\" \\\n --data \"service.id=$SERVICE_ID\"\ncurl http://localhost:8001/plugins/ -i -X POST \\\n --data 'name=rate-limiting' \\\n --data \"config.minute=5\" \\\n --data \"service.id=$SERVICE_ID\"\ncurl http://localhost:8001/routes/ -i -X POST \\\n --data 'paths[]=/bay-lights' \\\n --data \"service.id=$SERVICE_ID\"\n```\n\n### Demo: custom plugin: hello-world-header\n\n[Custom plugins](https://docs.konghq.com/1.0.x/plugin-development/) allow you to observe and transform HTTP traffic using lightweight, high-performance [Lua](http://www.lua.org) code in Nginx [request processing contexts](https://docs.konghq.com/1.0.x/plugin-development/custom-logic/#available-request-contexts). Building on the [previous example](#user-content-demo-api-rate-limiting), let's add a simple plugin to Kong.\n\n[hello-world-header](lib/kong/plugins/hello-world-header/handler.lua) will add an HTTP response header **X-Hello-World** showing the date and a message from an environment variable.\n\nActivate this plugin for the API:\n\n```bash\ncurl http://localhost:8001/plugins/ -i -X POST \\\n --data 'name=hello-world-header' \\\n --data \"service.id=$SERVICE_ID\"\n```\n\nThen, set a message through the Heroku config var:\n\n```bash\nheroku config:set HELLO_WORLD_MESSAGE='🌈🙈'\n# …the app will restart.\n```\n\nNow, when fetching an API response, notice the **X-Hello-World** header:\n\n```bash\ncurl --head https://kong-proxy-public.herokuapp.com/bay-lights/lights\n# ↩︎\n# HTTP/1.1 200 OK\n# Connection: keep-alive\n# Content-Type: application/json;charset=utf-8\n# Content-Length: 9204\n# X-Ratelimit-Limit-Minute: 5\n# X-Ratelimit-Remaining-Minute: 4\n# Server: Cowboy\n# Date: Mon, 28 Aug 2017 23:14:47 GMT\n# Strict-Transport-Security: max-age=31536000\n# X-Content-Type-Options: nosniff\n# Vary: Accept-Encoding\n# Request-Id: 6c815aae-a5e9-496f-b731-dc72bbe2b63e\n# Via: kong/0.14.0, 1.1 vegur\n# X-Hello-World: Today is 2017-08-28. 🌈🙈 <--- The injected header\n# X-Kong-Upstream-Latency: 49\n# X-Kong-Proxy-Latency: 161\n```\n\n\n### Demo: custom plugin: API translation, JSON→XML\n\nJSON/REST has taken over as the internet API lingua franca, shedding the complexity of XML/SOAP. The [National Digital Forecast Database [NDFD]](http://graphical.weather.gov/xml/) is a legacy XML/SOAP service.\n\nThis app includes a sample, custom plugin [ndfd-xml-as-json](lib/kong/plugins/ndfd-xml-as-json). This plugin exposes a JSON API that returns the maximum temperatures forecast for a location from the NDFD SOAP service. Using the single-resource concept of REST, the many variations of a SOAP or other legacy interfaces may be broken out into elegant, individual JSON APIs.\n\nTry it in your shell terminal:\n```bash\ncurl https://kong-proxy-public.herokuapp.com/ndfd-max-temps \\\n --data '{\"latitude\":37.733795,\"longitude\":-122.446747}'\n# Response contains max temperatures forecast for San Francisco, CA\n\ncurl https://kong-proxy-public.herokuapp.com/ndfd-max-temps \\\n --data '{\"latitude\":27.964157,\"longitude\":-82.452606}'\n# Response contains max temperatures forecast for Tampa, FL\n\ncurl https://kong-proxy-public.herokuapp.com/ndfd-max-temps \\\n --data '{\"latitude\":41.696629,\"longitude\":-71.149994}'\n# Response contains max temperatures forecast for Fall River, MA\n```\n\nMuch more elegant than the legacy API. See the [sample request body](spec/data/ndfd-request.xml):\n```bash\ncurl --data @spec/data/ndfd-request.xml -H 'Content-Type:text/xml' -X POST https://graphical.weather.gov/xml/SOAP_server/ndfdXMLserver.php\n# Response contains wrapped XML data. Enjoy decoding that.\n```\n\nThis technique may be used to create a suite of cohesive JSON APIs out of various legacy APIs.\n\nHere's the configuration for this API translator:\n\n```bash\ncurl http://localhost:8001/services/ -i -X POST \\\n --data 'name=ndfd-max-temps' \\\n --data 'protocol=https' \\\n --data 'port=443' \\\n --data 'host=graphical.weather.gov' \\\n --data 'path=/xml/SOAP_server/ndfdXMLserver.php'\n# Note the Service ID returned in previous response, use it in place of `$SERVICE_ID`.\ncurl http://localhost:8001/plugins/ -i -X POST \\\n --data 'name=request-size-limiting' \\\n --data \"config.allowed_payload_size=8\" \\\n --data \"service.id=$SERVICE_ID\"\ncurl http://localhost:8001/plugins/ -i -X POST \\\n --data 'name=rate-limiting' \\\n --data \"config.minute=5\" \\\n --data \"service.id=$SERVICE_ID\"\ncurl http://localhost:8001/plugins/ -i -X POST \\\n --data 'name=ndfd-xml-as-json' \\\n --data \"service.id=$SERVICE_ID\"\ncurl http://localhost:8001/routes/ -i -X POST \\\n --data 'paths[]=/ndfd-max-temps' \\\n --data \"service.id=$SERVICE_ID\"\n```\n\n👓 See the implementation of the custom plugin's [Lua source code](lib/kong/plugins/ndfd-xml-as-json), [unit tests](spec/unit/kong/plugins/ndfd-xml-as-json/handler_spec.lua), and [integration tests](spec/integration/kong/plugins/ndfd-xml-as-json_spec.lua).\n\n\n## Dev notes\n\n### Learning the language of Kong\n\n* [Definitely an openresty guide](http://www.staticshin.com/programming/definitely-an-open-resty-guide/)\n* [An Introduction To OpenResty - Part 1](http://openmymind.net/An-Introduction-To-OpenResty-Nginx-Lua/), [2](http://openmymind.net/An-Introduction-To-OpenResty-Part-2/), & [3](http://openmymind.net/An-Introduction-To-OpenResty-Part-3/)\n* [Nginx API for Lua](https://github.com/openresty/lua-nginx-module#nginx-api-for-lua), `ngx` reference, for use in Kong plugins\n * [Nginx variables](http://nginx.org/en/docs/http/ngx_http_upstream_module.html#var_upstream_status), accessible through `ngx.var`\n\n### Programming with Lua\n\n* [Lua 5.1](http://www.lua.org/manual/5.1/), Note: Kong is not compatible with the newest Lua version\n* [Classic Objects](https://github.com/rxi/classic), the basis of Kong's plugins\n* [Moses](http://yonaba.github.io/Moses/doc/), functional programming\n* [Lubyk](http://doc.lubyk.org), realtime programming (performance- & game-oriented)\n* [resty-http](https://github.com/pintsized/lua-resty-http), Nginx-Lua co-routine based HTTP client\n* [Serpent](http://notebook.kulchenko.com/programming/serpent-lua-serializer-pretty-printer), inspect values\n* [Busted](http://olivinelabs.com/busted/), testing framework\n\n### Local development\n\nTo work with Kong locally on macOS X.\n\n#### Requirements\n\n* [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)\n* [Heroku](https://www.heroku.com/home)\n * [command-line tools (CLI)](https://toolbelt.heroku.com)\n * [a free account](https://signup.heroku.com)\n* Kong & its dependencies\n * as a single package install\n * [Kong](https://docs.konghq.com/install/macos/) for macOS\n * or, from source\n * [Lua](https://www.lua.org/versions.html) 5.1\n * [LuaRocks](https://github.com/luarocks/luarocks) 2.4.4\n * [OpenSSL](https://www.openssl.org/source/) 1.1.1\n * [OpenResty](https://openresty.org/en/installation.html) 1.13.6.2\n * [Install from source](https://docs.konghq.com/install/source/)\n * `./configure -j2 --with-openssl=~/Downloads/openssl-1.1.1a --with-http_realip_module --with-http_stub_status_module`\n * [Kong](https://github.com/Kong/kong) 1.0.0\n * `luarocks install kong 1.0.0 OPENSSL_DIR=/usr/local/opt/openssl CRYPTO_DIR=/usr/local/opt/openssl`\n\n#### Clone & connect\n\nIf you haven't already, clone and connect your own fork of this repo to the Heroku app:\n\n```bash\n# Replace the main repo with your own fork:\ngit clone https://github.com/heroku/heroku-kong.git\ncd heroku-kong\n\n# Use the name of the Heroku app:\nheroku git:remote --app $APP_NAME\nheroku info\n```\n\n##### Setup\n\n1. Ensure [requirements](#user-content-requirements) are met\n1. Create the Postgres user & databases:\n\n ```bash\n createuser --pwprompt kong\n # set the password \"kong\"\n\n createdb --owner=kong kong_dev\n createdb --owner=kong kong_tests\n ```\n1. Execute `./bin/setup`\n\n##### Running\n\n```bash\nbin/start\n```\n\n* Logs in `/usr/local/var/kong/logs/`\n* Prefix is `/usr/local/var/kong` for commands like:\n * `kong health -p /usr/local/var/kong`\n * `kong stop -p /usr/local/var/kong`\n\n##### Testing\n\nAny test-specific Lua rocks should be specified in `Rockfile_test` file, so that they are not installed when the app is deployed.\n\nAdd tests in `spec/`:\n\n * Uses the [Busted testing framework](http://olivinelabs.com/busted)\n * [Kong plugin testing guide](https://docs.konghq.com/1.0.x/plugin-development/tests/)\n * [buildpack requirements for testing](https://github.com/heroku/heroku-buildpack-kong/blob/master/README.markdown#user-content-testing)\n\n```bash\nbin/test\n```", + "readme_title": "Kong as a Heroku app", + "releases_count": 0, + "repo": "heroku-kong", + "repo_name": "heroku-kong", + "stars": 0, + "topics": [], + "updated_at": "2020-07-29T12:12:46Z", + "url": "https://github.com/hassan11196/heroku-kong", + 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"[![CircleCI](https://circleci.com/gh/ifmeorg/ifme/tree/master.svg?style=svg)](https://circleci.com/gh/ifmeorg/ifme/tree/master)\n[![Code Climate](https://codeclimate.com/github/ifmeorg/ifme/badges/gpa.svg)](https://codeclimate.com/github/ifmeorg/ifme)\n[![Test Coverage](https://api.codeclimate.com/v1/badges/f9444a4d4116720518fe/test_coverage)](https://codeclimate.com/github/ifmeorg/ifme/test_coverage)\n[![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-v1.4%20adopted-ff69b4.svg)](code-of-conduct.md)\n[![Backers on Open Collective](https://opencollective.com/ifme/backers/badge.svg)](#backers)\n[![Sponsors on Open Collective](https://opencollective.com/ifme/sponsors/badge.svg)](#sponsors)\n\nREADME in: [Spanish](https://github.com/ifmeorg/ifme/blob/master/README-ES.md), [Portuguese](https://github.com/ifmeorg/ifme/blob/master/README-PT.md), [French](https://github.com/ifmeorg/ifme/blob/master/README-FR.md)\n\n# if me\n\n[if me](https://www.if-me.org/) is a community for mental health experiences\nthat encourages people to share their personal stories with trusted allies.\nTrusted allies are the people we interact with on a daily basis, including\nfriends, family members, co-workers, teachers, and mental health workers.\n\nDealing with mental health is what makes us human. But for a lot of us, it's a\nstruggle to be open about it. Not everyone is a counsellor or therapist. The\npeople who we interact with everyday shape our emotions and behavior. Getting\nthem involved in mental health treatment is the key to recovery.\n\nThe live site can be found at [if-me.org](https://www.if-me.org/). The live design systems can be found at [design.if-me.org](http://design.if-me.org/).\n\nWe use the wonderful [Contributor Covenant](http://contributor-covenant.org) for\nour code of conduct. Please\n[read it](https://github.com/ifmeorg/ifme/blob/master/code_of_conduct.md)\nbefore joining our project.\n\n**Read about our project goals and how to contribute (not just as a developer) [here](https://github.com/ifmeorg/ifme/blob/master/CONTRIBUTING.md).**\n\n## Documentation\n\nPlease check out our [Wiki](https://github.com/ifmeorg/ifme/wiki) for full documentation.\n\n### Installation\n\nInformation about installing and configuring the app [here](https://github.com/ifmeorg/ifme/wiki/Installation). Test, development, and production instances are covered.\n\n### Contributor Blurb\n\nEveryone is highly encouraged to [add themselves](https://github.com/ifmeorg/ifme/wiki/Contributor-Blurb) to our Contribute page.\n\n## Donate\n\nWe also welcome financial contributions in full transparency on our\n[Open Collective](https://opencollective.com/ifme).\nAnyone can file an expense. If the expense makes sense for the development of\nthe community, it will be \"merged\" in the ledger of our Open Collective by the\ncore contributors and the person who filed the expense will be reimbursed.\n\nWe also have a [Patreon](https://www.patreon.com/ifme) page where you can give\nmonthly donations.\n\n### Backers\n\nThank you to our Patreon backers [Rob Drimmie](https://www.patreon.com/user?u=3251857),\n[Joseph D. Marhee](https://www.patreon.com/user?u=2899171), and\n[Carol Willing](https://www.patreon.com/user?u=202458)!\n\nThank you to all our Open Collective backers!\n[Become a backer!](https://opencollective.com/ifme#backer)\n\n\n\n### Sponsors\n\nThank you to all our sponsors! (please ask your company to also support this\nopen source project by [becoming a sponsor](https://opencollective.com/ifme#sponsor))\n\n\n\n\n\n\n\n\n\n\n\n\n## License\n\nThe source code is licensed under GNU AGPLv3. For more information see\nhttp://www.gnu.org/licenses/agpl-3.0.txt or\n[LICENSE.txt](https://github.com/ifmeorg/ifme/blob/master/LICENSE.txt).", + "readme_title": "if me", + "releases_count": 0, + "repo": "ifme", + "repo_name": "ifme", + "stars": 0, + "topics": [], + "updated_at": "2020-01-06T20:54:52Z", + "url": "https://github.com/hassan11196/ifme", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/ifme" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/income-prediction" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 1, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 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"MIT License", + "license_spdx": "MIT", + "name_with_owner": "hassan11196/introtodeeplearning", + "owner": "hassan11196", + "parent_repo": "MITDeepLearning/introtodeeplearning", + "parent_url": "https://github.com/MITDeepLearning/introtodeeplearning", + "primary_language": "Jupyter Notebook", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2020-08-23T07:28:43Z", + "readme_length": 2363, + "readme_text": "[![banner](assets/banner.png)](http://introtodeeplearning.com)\n\nThis repository contains all of the code and software labs for [MIT 6.S191: Introduction to Deep Learning](http://introtodeeplearning.com)! All lecture slides and videos are available on the course website.\n\n\n## Opening the labs in Google Colaboratory:\n\nThe 2020 6.S191 labs will be run in Google's Colaboratory, a Jupyter notebook environment that runs entirely in the cloud, you don't need to download anything. To run these labs, you must have a Google account.\n\nOn this Github repo, navigate to the lab folder you want to run (`lab1`, `lab2`, `lab3`) and open the appropriate python notebook (\\*.ipynb). Click the \"Run in Colab\" link on the top of the lab. That's it!\n\n## Running the labs\nNow, to run the labs, open the Jupyter notebook on Colab. Navigate to the \"Runtime\" tab --> \"Change runtime type\". In the pop-up window, under \"Runtime type\" select \"Python 3\", and under \"Hardware accelerator\" select \"GPU\". Go through the notebooks and fill in the `#TODO` cells to get the code to compile for yourself!\n\n\n### MIT Deep Learning package\nYou might notice that inside the labs we install the `mitdeeplearning` python package from the Python Package repository:\n\n`pip install mitdeeplearning`\n\nThis package contains convienence functions that we use throughout the course and can be imported like any other Python package.\n\n`>>> import mitdeeplearning as mdl`\n\nWe do this for you in each of the labs, but the package is also open source under the same license so you can also use it outside the class.\n\n## Lecture Videos\n\n[](https://www.youtube.com/watch?v=njKP3FqW3Sk&list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI&index=1)\n\nAll lecture videos are available publicly online and linked above! Use and/or modification of lecture slides outside of 6.S191 must reference:\n\n> © MIT 6.S191: Introduction to Deep Learning\n>\n> http://introtodeeplearning.com\n\n## License\nAll code in this repository is copyright 2020 [MIT 6.S191 Introduction to Deep Learning](http://introtodeeplearning.com). All Rights Reserved.\n\nLicensed under the MIT License. You may not use this file except in compliance with the License. Use and/or modification of this code outside of 6.S191 must reference:\n\n> © MIT 6.S191: Introduction to Deep Learning\n>\n> http://introtodeeplearning.com", + "readme_title": "[![banner](assets/banner.png)](http://introtodeeplearning.com)", + "releases_count": 0, + "repo": "introtodeeplearning", + "repo_name": "introtodeeplearning", + "stars": 0, + "topics": [], + "updated_at": "2025-01-05T21:35:35Z", + "url": "https://github.com/hassan11196/introtodeeplearning", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/introtodeeplearning" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/kafka-bank-transactions" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 2, + "contributors_count": 2, + "contributors_top": [ + { + "contributions": 1, + "html_url": "https://github.com/digio-au", + "login": "digio-au" + }, + { + "contributions": 1, + "html_url": "https://github.com/deanbaker", + "login": "deanbaker" + } + ], + "created_at": "2021-06-19T18:38:01Z", + "default_branch": "master", + "description": "Kafka Streams training exercise", + "dirs_total_count": 17, + "files_root_entries": [ + { + "path": ".gitignore", + "size": 27, + "type": "file" + }, + { + "path": "LICENSE", + "size": 1067, + "type": "file" + }, + { + "path": "README.md", + "size": 5887, + "type": "file" + }, + { + "path": "build.gradle", + "size": 379, + "type": "file" + }, + { + "path": "create-topics.sh", + "size": 3258, + "type": "file" + }, + { + "path": "gradle", + "size": 0, + "type": "dir" + }, + { + "path": "gradlew", + "size": 5296, + "type": "file" + }, + { + "path": "gradlew.bat", + "size": 2260, + "type": "file" + }, + { + "path": "settings.gradle", + "size": 35, + "type": "file" + }, + { + "path": "src", + "size": 0, + "type": "dir" + } + ], + "files_total_count": 20, + "first_commit_date_default_branch": "2019-09-03T09:55:13Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/kafka-bank-transactions", + "is_archived": false, + "is_fork": true, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Java", + "size": 16987 + }, + { + "language": "Shell", + "size": 3258 + } + ], + "last_commit_date_default_branch": "2019-09-03T10:19:11Z", + "last_commit_oid_default_branch": "8f5fca61d3d3b98a592602a00da8c706e681f2aa", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "MIT License", + "license_spdx": "MIT", + "name_with_owner": "hassan11196/kafka-bank-transactions", + "owner": "hassan11196", + "parent_repo": "deanbaker/kafka-bank-transactions", + "parent_url": "https://github.com/deanbaker/kafka-bank-transactions", + "primary_language": "Java", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2019-10-16T03:37:27Z", + "readme_length": 5886, + "readme_text": "# Kafka Transactions\n\n## Problem statement\n\nWe want to create a streaming application that will make use of some simple account transaction messages.\n\n```json\n{\n \"customer\": \"Bob\",\n \"amount\": 64,\n \"category\": \"CG01\",\n \"occurred\": 1567148940000 \n}\n```\n\nBroadly we will perform the following tasks:\n* Calculate the total for each customer.\n* Calculate the total for a month for each customer\n* Enrich the transaction data with another source.\n\nTo do this we will use 5 topics, 2 input and 3 output:\n\n| Topic | Type | Key | Value |\n| ---------------------------- |--------| -------|---|\n| transactions-topic | Input | String | json (String) |\n| category-topic | Input | String | String |\n| customer-total-topic | Output | String | Long |\n| customer-rolling-total-topic | Output |String | Long |\n| enhanced-transactions-topic | OUtput | String | json (String) |\n\nWe do not need to tell Kafka what it will be storing when we create the topics, but we do need to tell\nour `Producer` and `Streaming Application` how to serialize and deserialize these values.\n\n### The Producer\n\nUltimately we will be writing transactions to a topic `transactions-topic` in json format. Have a bit of a think\nabout what key we should use!\n\n\nWe will be following kafka's exactly-once semantics, read about it here: https://www.confluent.io/blog/exactly-once-semantics-are-possible-heres-how-apache-kafka-does-it/\n\nThe producer will have access to a `LegacyBankingSystem` that will provide you with \ntransactions to be published, so don't worry about generating this data yourself.\n\n### Calculating a customers balance\n\nWe will then create a streaming topology that can handle these events. We will then \naggregate a customers balance and write this back to another topic - for this we will use a \n`compacted` topic: https://kafka.apache.org/documentation/#compaction\n\nIf we were to see the following events to the `transactions-topic`:\n```json\n[{\n \"customer\": \"Bob\",\n \"amount\": 64,\n \"category\": \"CG01\",\n \"occurred\": 1567148940000 \n},\n{\n \"customer\": \"Bob\",\n \"amount\": 64,\n \"category\": \"CG01\",\n \"occurred\": 1567148940000 \n},\n{\n \"customer\": \"Alice\",\n \"amount\": 64,\n \"category\": \"CG01\",\n \"occurred\": 1567148940000 \n}]\n```\n\nHow many records should be in the `customer-total-topic`? And what should their values be?\n\n\n### Calculating the last 30 day spend.\n\nWe will use the same topology to create another view of the data - the total spend over the \nlast 30 days (so the balance will reset to 0 after 30 days)\n\nAgain we will look to use a Compacted topic here.\n\n\n### Enriching transaction data.\n\nYou might notice that there is a `category` code embedded into each transaction record. Not very useful huh?\nLet's use a GlobalKTable to allow us to reference data sourced from the `category-topic` to enrich out transactions.\nIf there is no corresponding value for a category, we will retain the code value. \n\nhttps://docs.confluent.io/current/streams/concepts.html#streams-concepts-ktable\n\n\n## Scripts to help\n\nGo have a look at `create-topics.sh` file for more details... \n\n### Create the topics\n\n#### Create the input topic:\n```bash\n./bin/kafka-topics.sh --bootstrap-server kafka-1:19092 --create --topic transaction-topic --partitions 1 --replication-factor 1\n```\n\n#### Create the category topic:\n```bash\n./bin/kafka-topics.sh --bootstrap-server kafka-1:19092 --create --topic category-topic --partitions 1 --replication-factor 1\n```\n\n#### Create the enhanced transaction topic\n```bash\n./bin/kafka-topics.sh --bootstrap-server kafka-1:19092 --create --topic enhanced-transaction-topic --partitions 1 --replication-factor 1 \n```\n\n#### Create the running total topic:\n```bash\n./bin/kafka-topics.sh --bootstrap-server kafka-1:19092 --create \\\n --topic customer-total-topic \\\n --partitions 1 \\\n --replication-factor 1 \\\n --config cleanup.policy=compact \\\n --config min.cleanable.dirty.ratio=0.01 \\\n --config segment.ms=100\n```\nYou will notice here are are using a `cleanup.policy` of `compact`, but also making\nthe segment super small (100ms)! This is so we can see the log compaction happening without\nwaiting the default hours to see compaction in action.\n\n#### Create the windowed topic \n\n```bash\n./bin/kafka-topics.sh --bootstrap-server kafka-1:19092 --create \\\n --topic customer-rolling-total-topic \\\n --partitions 1 \\\n --replication-factor 1 \\\n --config cleanup.policy=compact \\\n --config min.cleanable.dirty.ratio=0.01 \\\n --config segment.ms=100\n```\n\n#### Consume a topic\nWe will use the console consumer to keep track of how things are going:\n```bash\n ./bin/kafka-console-consumer.sh --bootstrap-server kafka-1:19092 \\\n --topic customer-total-topic \\\n --from-beginning \\\n --formatter kafka.tools.DefaultMessageFormatter \\\n --property print.key=true \\\n --property print.value=true \\\n --property key.deserializer=org.apache.kafka.common.serialization.StringDeserializer \\\n --property value.deserializer=org.apache.kafka.common.serialization.LongDeserializer\n```\n\n#### Other useful commands\n````bash\n# list topics\n./bin/kafka-topics.sh --bootstrap-server kafka-1:19092 --list\n \n \n # Read the raw transactions\n ./bin/kafka-console-consumer.sh --bootstrap-server kafka-1:19092 \\\n --topic transaction-topic \\\n --from-beginning \\\n --formatter kafka.tools.DefaultMessageFormatter \\\n --property print.key=true \\\n --property print.value=true \\ \n \n# Produce directly to the queue \n./bin/kafka-console-producer.sh --broker-list kafka-1:19092 --topic category-topic --property \"parse.key=true\" --property \"key.separator=:\" \n````", + "readme_title": "Kafka Transactions", + "releases_count": 0, + "repo": "kafka-bank-transactions", + "repo_name": "kafka-bank-transactions", + "stars": 0, + "topics": [], + "updated_at": "2021-06-19T18:38:03Z", + "url": "https://github.com/hassan11196/kafka-bank-transactions", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + 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"readme_text": "# Keras Paint Detection", + "readme_title": "Keras Paint Detection", + "releases_count": 0, + "repo": "keras-paint", + "repo_name": "keras-paint", + "stars": 0, + "topics": [], + "updated_at": "2020-04-02T14:21:31Z", + "url": "https://github.com/hassan11196/keras-paint", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/keras-paint" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/leetcode-solutions" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 18, + "contributors_count": 3, + "contributors_top": [ + { + "contributions": 9, + "html_url": "https://github.com/EishaMazhar", + 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"has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/leetcode-solutions", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 3, + "issues_total": 3, + "languages_top": [ + { + "language": "Python", + "size": 3497 + }, + { + "language": "Java", + "size": 729 + } + ], + "last_commit_date_default_branch": "2023-10-22T17:30:33Z", + "last_commit_oid_default_branch": "16591a6a3b44e1ffcd42209dc92181535f8e856d", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/leetcode-solutions", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "Python", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 7, + "pull_requests_open": 0, + "pull_requests_total": 7, + "pushed_at": "2023-10-22T17:30:33Z", + "readme_length": 86, + "readme_text": "# Random leetcode solutions by category\n\nfind the problem link inside the problem code", + "readme_title": "Random leetcode solutions by category", + "releases_count": 0, + "repo": "leetcode-solutions", + "repo_name": "leetcode-solutions", + "stars": 1, + "topics": [ + "leetcode", + "hacktoberfest", + "hacktoberfest-accepted", + "hacktoberfest2022" + ], + "updated_at": "2022-11-11T07:02:37Z", + "url": "https://github.com/hassan11196/leetcode-solutions", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/leetcode-solutions" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/llm-dev-mate" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 6, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 6, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + } + ], + "created_at": "2024-10-13T17:14:42Z", + "default_branch": "main", + "description": "A VSCode Extensions to help developers with LLM interaction. Just copy and paste code with 1 click and let LLM-DevMate handle the context.", + "dirs_total_count": 3, + "files_root_entries": [ + { + "path": ".gitignore", + "size": 34, + "type": "file" + }, + { + "path": ".vscode-test.mjs", + "size": 113, + "type": "file" + }, + { + "path": ".vscode", + "size": 0, + "type": "dir" + }, + { + "path": ".vscodeignore", + "size": 147, + "type": "file" + }, + { + "path": "CHANGELOG.md", + "size": 238, + "type": "file" + }, + { + "path": "LICENSE", + "size": 1078, + "type": "file" + }, + { + "path": "README.md", + "size": 3494, + "type": "file" + }, + { + "path": "eslint.config.mjs", + "size": 689, + "type": "file" + }, + { + "path": "extension.js", + "size": 6735, + "type": "file" + }, + { + "path": "images", + "size": 0, + "type": "dir" + }, + { + "path": "jsconfig.json", + "size": 182, + "type": "file" + }, + { + "path": "package-lock.json", + "size": 112050, + "type": "file" + }, + { + "path": "package.json", + "size": 2790, + "type": "file" + }, + { + "path": "test", + "size": 0, + "type": "dir" + }, + { + "path": "vsc-extension-quickstart.md", + "size": 2800, + "type": "file" + } + ], + "files_total_count": 16, + "first_commit_date_default_branch": "2024-10-13T16:46:23Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": true, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/llm-dev-mate", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "JavaScript", + "size": 9308 + } + ], + "last_commit_date_default_branch": "2024-10-13T18:37:59Z", + "last_commit_oid_default_branch": "ec5d1dc8514f029b6b3a33e9f01fb2f3953b38f8", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "MIT License", + "license_spdx": "MIT", + "name_with_owner": "hassan11196/llm-dev-mate", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "JavaScript", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 2, + "pull_requests_total": 2, + "pushed_at": "2025-11-19T20:41:07Z", + "readme_length": 3491, + "readme_text": "# LLM DevMate\n\n**LLM DevMate** is a Visual Studio Code extension designed to assist developers by providing utilities to work with Large Language Models (LLMs). This extension enables users to:\n\n- Calculate the number of tokens in the current file, selection, or entire repository.\n- Copy the content of the current file, including its relative path, to the clipboard.\n- Copy the entire repository (excluding files in `.gitignore`) to the clipboard, along with the relative paths of the files.\n\n## Features\n\n1. **Count Tokens in Current File** \n This command allows you to count the number of tokens in the currently open file. This is especially useful when working with LLMs where token limits are important.\n\n2. **Count Tokens in Selection** \n If you only want to count the tokens in a specific selection, this command will calculate the token count for the selected text within the file.\n\n3. **Count Tokens in Entire Repository** \n This feature will calculate the number of tokens for all files in the repository, excluding files listed in `.gitignore`.\n\n4. **Copy Current File to Clipboard** \n Copy the entire content of the currently open file, along with its relative path in the project, to the clipboard.\n\n5. **Copy Entire Repository to Clipboard** \n Copy the content of all files in the repository, excluding files in `.gitignore`, along with their relative paths to the clipboard. This is useful for preparing large prompts or input for LLMs.\n\n## Installation\n\n1. Clone the repository or download the files to your local machine.\n2. Open the project in Visual Studio Code.\n3. Run the extension in a VS Code development environment by pressing `F5`.\n\n## Commands\n\nHere are the available commands in **LLM DevMate**:\n\n- `LLM DevMate: Count Tokens in Current File`: Count tokens in the currently open file.\n- `LLM DevMate: Count Tokens in Selection`: Count tokens in the currently selected text.\n- `LLM DevMate: Count Tokens in Repo`: Count tokens in the entire repository (excluding `.gitignore` files).\n- `LLM DevMate: Copy Current File to Clipboard`: Copy the content of the current file along with its relative path to the clipboard.\n- `LLM DevMate: Copy Repo to Clipboard`: Copy the entire repository content, excluding `.gitignore` files, to the clipboard.\n\nThese commands can be executed through the Command Palette (`Ctrl+Shift+P` or `Cmd+Shift+P`) or by right-clicking in the editor.\n\n## Usage\n\n### Counting Tokens\n\n1. Open a file or select a portion of the file.\n2. Use the Command Palette (`Ctrl+Shift+P` or `Cmd+Shift+P`) and type:\n - `LLM DevMate: Count Tokens in Current File` for the entire file.\n - `LLM DevMate: Count Tokens in Selection` for a specific selection.\n - `LLM DevMate: Count Tokens in Repo` for the entire repository.\n\n### Copying Files to Clipboard\n\n- To copy the content of the current file to the clipboard, including the file’s relative path, use the command `LLM DevMate: Copy Current File to Clipboard`.\n- To copy the content of the entire repository to the clipboard, excluding files listed in `.gitignore`, use the command `LLM DevMate: Copy Repo to Clipboard`.\n\n## Configuration\n\nThe extension supports token calculation for different models. You can configure which LLM model to use for token counting in your VS Code settings. By default, **GPT-3.5 Turbo** is used, but other models like **GPT-4** can be selected.\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.", + "readme_title": "LLM DevMate", + "releases_count": 0, + "repo": "llm-dev-mate", + "repo_name": "llm-dev-mate", + "stars": 0, + "topics": [ + "development", + "llm", + "openai", + "token", + "vscode", + "vscode-extension" + ], + "updated_at": "2024-10-13T18:38:06Z", + "url": "https://github.com/hassan11196/llm-dev-mate", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/llm-dev-mate" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/minecraft-heroku" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 7, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 7, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + } + ], + "created_at": "2020-03-21T00:17:58Z", + "default_branch": "master", + "description": "A minecraft server hosted using heroku.", + "dirs_total_count": 0, + "files_root_entries": [ + { + "path": "README.md", + "size": 163, + "type": "file" + }, + { + "path": "eula.txt", + "size": 9, + "type": "file" + }, + { + "path": "forcepusher.txt", + "size": 21, + "type": "file" + }, + { + "path": "server.properties", + "size": 942, + "type": "file" + } + ], + "files_total_count": 4, + "first_commit_date_default_branch": "2020-03-20T23:08:44Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/minecraft-heroku", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [], + "last_commit_date_default_branch": "2020-03-21T04:42:12Z", + "last_commit_oid_default_branch": "4a2c366ae683c15433a5aa66d2fc144c7c411acd", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/minecraft-heroku", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": null, + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2020-03-21T04:42:12Z", + "readme_length": 162, + "readme_text": "# Minecraft Server on Heroku\n\n## Reference :\n\nhttps://jkutner.github.io/2015/03/11/minecraft-on-heroku.html\n\nhttps://github.com/jkutner/heroku-buildpack-minecraft", + "readme_title": "Minecraft Server on Heroku", + "releases_count": 0, + "repo": "minecraft-heroku", + "repo_name": "minecraft-heroku", + "stars": 1, + "topics": [], + "updated_at": "2020-04-02T14:14:17Z", + "url": "https://github.com/hassan11196/minecraft-heroku", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/minecraft-heroku" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/miniBANK_2.0" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 8, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 8, + "html_url": "https://github.com/junedraghib", + "login": "junedraghib" + } + ], + "created_at": "2021-06-19T18:39:04Z", + "default_branch": "master", + "description": "a virtual bank application having all the common features such as unique account generation based on user choice/transfer money/withdraw money/deposit money/message alert /unique transaction id generating/user dashboard/admin dashboard/transaction statics of last 10 days for admin/data tables /edit/delete user info/sign in /sign up", + "dirs_total_count": 601, + "files_root_entries": [ + { + "path": "README.md", + "size": 16306, + "type": "file" + }, + { + "path": "Scaffold", + "size": 0, + "type": "dir" + }, + { + "path": "ScreenShots", + "size": 0, + "type": "dir" + }, + { + "path": "admin.html", + "size": 6415, + "type": "file" + }, + { + "path": "admin", + "size": 0, + "type": "dir" + }, + { + "path": "css", + "size": 0, + "type": "dir" + }, + { + "path": "font-awesome", + "size": 0, + "type": "dir" + }, + { + "path": "fonts", + "size": 0, + "type": "dir" + }, + { + "path": "img", + "size": 0, + "type": "dir" + }, + { + "path": "index.html", + "size": 17105, + "type": "file" + }, + { + "path": "js", + "size": 0, + "type": "dir" + }, + { + "path": "login.css", + "size": 7543, + "type": "file" + }, + { + "path": "login1.html", + "size": 6436, + "type": "file" + }, + { + "path": "main.css", + "size": 0, + "type": "file" + }, + { + "path": "register1.html", + "size": 11428, + "type": "file" + }, + { + "path": "responsive.css", + "size": 8451, + "type": "file" + }, + { + "path": "scss", + "size": 0, + "type": "dir" + }, + { + "path": "style.css", + "size": 19281, + "type": "file" + }, + { + "path": "theme-default.css", + "size": 168365, + "type": "file" + }, + { + "path": "welcomeRegister.php", + "size": 2075, + "type": "file" + } + ], + "files_total_count": 2627, + "first_commit_date_default_branch": "2018-05-29T09:20:06Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/miniBANK_2.0", + "is_archived": false, + "is_fork": true, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "HTML", + "size": 7136897 + }, + { + "language": "JavaScript", + "size": 5761375 + }, + { + "language": "CSS", + "size": 3370327 + }, + { + "language": "PHP", + "size": 1012277 + }, + { + "language": "Python", + "size": 116505 + }, + { + "language": "C", + "size": 93057 + }, + { + "language": "Java", + "size": 31935 + }, + { + "language": "Perl", + "size": 8703 + }, + { + "language": "ASP", + "size": 8133 + }, + { + "language": "Lasso", + "size": 6981 + } + ], + "last_commit_date_default_branch": "2018-10-19T23:34:35Z", + "last_commit_oid_default_branch": "10fd87e1ad646f7dd2e8a3a7819b19c54e5c4956", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/miniBANK_2.0", + "owner": "hassan11196", + "parent_repo": "junedraghib/miniBANK_2.0", + "parent_url": "https://github.com/junedraghib/miniBANK_2.0", + "primary_language": "HTML", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2018-10-23T18:23:33Z", + "readme_length": 16287, + "readme_text": "# miniBANK_2.0\na virtual bank application having all the bank features such as unique account generation based on user choice/transfer money/withdraw money/deposit money/message alert /unique transaction id generating/user dashboard/admin dashboard/transaction statics of last 10 days for admin/data tables /edit/delete user info/sign in /sign up\n \n ## Initial Screen for miniBank Project\n![initial screen](https://raw.githubusercontent.com/junedraghib/miniBANK_2.0/master/ScreenShots/Screenshot%20from%202018-10-19%2022-28-11.png)\n\n# User Control\nthere are plenty of things that a user can do, a new user may **register** for his/ her account by simple filling up their informations or existing user can **sign in** to their account and take advantage various features offered:\n## New User Registration Page\n![User registration page](https://raw.githubusercontent.com/junedraghib/miniBANK_2.0/master/ScreenShots/Screenshot%20from%202018-10-19%2022-28-40.png)\n\n##User Sign In Page\n![user sign in](https://raw.githubusercontent.com/junedraghib/miniBANK_2.0/master/ScreenShots/Screenshot%20from%202018-10-19%2022-28-48.png)\n\n\n## Initial User Dashboard\nWidgets on Right Side of Page shows:\n * Users Account Number used for Fund Transfer, Prefix of User Account Number tells about Customer Type and Account Type\n * Customer Type:\n * DPTR : Customer who Deposit Money in miniBank\n * ADTR : Customer who takes loan from miniBank (not yet implemented)\n * Account Type:\n * SD : Saving Deposit\n * FD : Fixed Deposit\n * CD : Current Deposit\n * RD : Recuring Deposit\n \n * User Customer Identification Number user for identification with in the database from two diffrent type of Customer(Depositor and AdvancerTaker)\n \n * Total Amount Present in user account, Recent Debit Transaction & Recent Credit Transaction. \n \n ![initial user dashboard](https://raw.githubusercontent.com/junedraghib/miniBANK_2.0/master/ScreenShots/Screenshot%20from%202018-10-19%2022-29-43.png)\n\n\n\n## User Transaction History \n * sorted with most recent transaction being tracked by unique transaction Id assigned to each transaction, \n * Prefix of each Transaction Id tellS about the type of transactions (Transfer: TXNMNT, Withdrawal: TXNWDL, Deposit: TXNDPT)\n\n![user transaction history](https://raw.githubusercontent.com/junedraghib/miniBANK_2.0/master/ScreenShots/Screenshot%20from%202018-10-19%2022-31-04.png)\n\n\n## User Money Transfer Interface\n* User can transfer money using other user's miniBank Account Number or using his miniBank username\n* When user click **Proceed**, system uses ***Ajax*** to search for Payee's information in the database and render it for verification purpose.\n* by clicking **Pay Now** system uses ***Ajax*** to perform money transfer conserving the **ACID PROPERTIES** i.e. either the complete transaction ***(subtracting amount from Payer and adding amount to payee account)*** will occur or everything will roll back to initial state thus making each transaction safe.\n* Payer will recive a message alert regarding debit of amount from his/ her account\n* Payer will recive a message alert regarding credit of amount to his/ her account\n\n![money tarnsfer interface](https://raw.githubusercontent.com/junedraghib/miniBANK_2.0/master/ScreenShots/Screenshot%20from%202018-10-19%2022-31-28.png)\n\n\n## Money Withdraw/ Deposit Interface\n* User can select their account, enter amount to be withdraw and click **Proceed** to withdraw money from there account.\n* System will check if current balance after debition of amount during transaction is atleast Rs. 1000, otherwise transaction will not occur.\n* User will recive a proper message alert regarding withdrawal/ deposition of amount into there account.\n* using similar interface user can deposit monet into there account\n* all transaction will be preserving **ACID PROPERTIES** of database at any point in time.\n\n![withdrawal money interface](https://raw.githubusercontent.com/junedraghib/miniBANK_2.0/master/ScreenShots/Screenshot%20from%202018-10-19%2022-31-59.png)\n\n\n## User Messages & Notifications\n* All the message alert messages will be shown here.\n* User may recive various notifications from admin in this section.\n* Also user can write a message to Admin in this section (currently not working)\n\n![Messages and Notifications](https://raw.githubusercontent.com/junedraghib/miniBANK_2.0/master/ScreenShots/Screenshot%20from%202018-10-19%2023-44-45.png)\n\n\n# Admin Control\nminiBank admin will **sign in** to their account first, and can perform various actions:\n\n![admin login](https://raw.githubusercontent.com/junedraghib/miniBANK_2.0/master/ScreenShots/Screenshot%20from%202018-10-19%2023-47-13.png)\n\n## Initial Admin Dashboard\n* First widget shows **number of users** having **Saving, Fixed, Current & Recurring Deposit Account** in miniBank at point in time.\n* Second widget shows **total number of users** registered in minibank.\n* Third widget gives insight about the **total amount** present in all the accounts of minibank.\n* Pie Chart gives insight about the amount present in diffrent type of Accounts.\n* Line and Bar Chart gives insight about the diffrent type transaction(Money Transfer, Withdrawal & Deposit) performed by all the users in last 100 days(it doesn't actually shows the last 100 days from present day but from some fixed day fixed at the time of deployment of this project due to the fact to due less usage of minibank by its user so chart might be flat due to unavalibility of past data) \n\n![initial admin dashboard](https://github.com/junedraghib/miniBANK_2.0/blob/master/ScreenShots/Screenshot%20from%202018-10-19%2023-56-37.png)\n\n\n## User Details Available to miniBank Admin only!\nAll the users are classified based on the type of account they are holding, and their details are available to admin in a very convenient manner\n\n![user details](https://raw.githubusercontent.com/junedraghib/miniBANK_2.0/master/ScreenShots/Screenshot%20from%202018-10-19%2023-57-16.png)\n\n## User Tables\nThese are the tables from database directly visible to the admin from a web interface.\n\n### User Detail Table(named as user in miniBank database)\nthis table is the initial table in the miniBank database, when a new user registers for account in miniBank therir information are stored in this table, as a new entery is made in this table (i.e new user register) following two **Triggers: generateAccountInfo is fired updating ***userID table*** with two 4 digit b/w 1234 & 8766 random arbitrary number which aren't present in the table previously as an cust_id and acc_id and cust_type, acc_type from initial user table.\n```sql\n\ndelimiter //\ncreate trigger generateAccountInfo\nafter insert on user\nfor each row\nbegin\n if new.username is not null then\n insert into userID(username,cust_id,acc_id,cust_type,acc_type)\n values(new.username,\n (SELECT random_num\n FROM (\n SELECT FLOOR(RAND() * 8766)+1234 AS random_num \n ) AS numbers_mst_plus_2\n WHERE `random_num` NOT IN (SELECT cust_id FROM (select * from userID) as userID1)\n LIMIT 1),\n (SELECT random_num\n FROM (\n SELECT FLOOR(RAND() * 8766)+1234 AS random_num \n ) AS numbers_mst_plus_2\n WHERE `random_num` NOT IN (SELECT acc_id FROM (select * from userID) as userID2)\n LIMIT 1),\n new.cust_type,\n new.account_type\n );\n end if;\nend //\ndelimiter ; \n\n```\n\n![user details table](https://raw.githubusercontent.com/junedraghib/miniBANK_2.0/master/ScreenShots/Screenshot%20from%202018-10-19%2023-58-01.png)\n\n### userID Table\nThis table contains customer id, and account id generated randomly corrosponding to each entry in initial ***user table*** and plays an essential role in generation of unique account number and unique CIN for each user, as new entry occur into this table a **trigger: generateUserCIN** is fired updating cin in two tables **depositor & advance_taker** depending upon the customer type. \nCIN is generated by concatination of cust_id generated earlier and the cust_type taken fron initial user type as choosen by user.\n```sql\ndelimiter //\ncreate trigger generateUserCIN\nafter insert on userID\nfor each row\nbegin\n if new.username is not null then\n case\n when new.cust_type = \"DPTR\" then\n insert into \n depositor(username,cin,cust_type,account_type)\n values(\n new.username,\n cast(concat(new.cust_type,cast((select cust_id from (select * from userID) as userID6 where cust_id=new.cust_id \n ) as char))as char),new.cust_type,new.acc_type\n );\n when new.cust_type = \"ADTR\" then\n insert into \n advance_taker(username,cin,cust_type,account_type)\n values(\n new.username,\n cast(concat(new.cust_type,cast((select cust_id from (select * from userID) as userID6 where cust_id=new.cust_id \n ) as char))as char),new.cust_type,new.acc_type\n );\n end case;\n end if;\nend //\ndelimiter ;\n```\n\n![userID table](https://raw.githubusercontent.com/junedraghib/miniBANK_2.0/master/ScreenShots/Screenshot%20from%202018-10-19%2023-58-09.png)\n\n## Depositor & Advance Taker Tables\nThe idea behind the Customer Identification Number(CIN) is to uniquely identify the user among those who deposit money in miniBank and those who take loan from miniBank, since loan feature isn't yet implemented so a user who chose customer type as ADTR his CIN is generated but account number isn't generated.\n\n![depositor and advancetaker table](https://raw.githubusercontent.com/junedraghib/miniBANK_2.0/master/ScreenShots/Screenshot%20from%202018-10-19%2023-58-23.png)\n\nif the insertion happen into advance_taker table based on user cust_type choice the system doen't perform any further task.\nbut if user cust_type id DPTR i.e Depositor then furthur account generation occurs based on users account_type.\n**Account is generated by concatibntion of cust_type, acc_type, cust_id and acc_id generated in userID table**\n__generateAccountNo Trriger is fired updating diffrent account tables based on user account type choice__\n```sql\ndelimiter //\ncreate trigger generateAccountNo\nafter insert on account\nfor each row\nbegin\n if new.username is not null then\n case\n when new.acc_type = \"SD\" then\n insert into \n saving_dept(username,cin,account_no,opening_date)\n values(\n new.username,\n new.cin,\n cast(concat(new.cust_type,new.acc_type,cast((select cust_id from (select * from userID) as userID6 where username=new.username \n ) as char),cast((select acc_id from (select * from userID) as userID7 where username=new.username) \n as char))as char),\n new.opening_date\n );\n when new.acc_type = \"CD\" then\n insert into \n current_dept(username,cin,account_no,opening_date)\n values(\n new.username,\n new.cin,\n cast(concat(new.cust_type,new.acc_type,cast((select cust_id from (select * from userID) as userID6 where username=new.username \n ) as char),cast((select acc_id from (select * from userID) as userID7 where username=new.username) \n as char))as char),\n new.opening_date\n ); \n when new.acc_type = \"RD\" then\n insert into \n recurring_dept(username,cin,account_no,opening_date)\n values(\n new.username,\n new.cin,\n cast(concat(new.cust_type,new.acc_type,cast((select cust_id from (select * from userID) as userID6 where username=new.username \n ) as char),cast((select acc_id from (select * from userID) as userID7 where username=new.username) \n as char))as char),\n new.opening_date\n );\n when new.acc_type = \"FD\" then\n insert into \n fixed_dept(username,cin,account_no,opening_date)\n values(\n new.username,\n new.cin,\n cast(concat(new.cust_type,new.acc_type,cast((select cust_id from (select * from userID) as userID6 where username=new.username \n ) as char),cast((select acc_id from (select * from userID) as userID7 where username=new.username) \n as char))as char),\n new.opening_date\n ); \n end case; \n end if; \nend//\ndelimiter ;\n```\n![saving deposit, reccuring deposit etc tables ](https://github.com/junedraghib/miniBANK_2.0/blob/master/ScreenShots/Screenshot%20from%202018-10-19%2023-58-34.png)\n\n\n![saving deposit, reccuring deposit etc tables ](https://raw.githubusercontent.com/junedraghib/miniBANK_2.0/master/ScreenShots/Screenshot%20from%202018-10-19%2023-58-37.png)\n\n## Transaction Tables\nthese tables hold the information about all the transaction occuring within the minibank, all transaction may tracked using a unique id assigned to each transaction\n\n### All Transactions\nthis table hold the information for each transaction such as date & time of transaction, type of transaction whether Credit or Debit\n\n![all transaction table](https://raw.githubusercontent.com/junedraghib/miniBANK_2.0/master/ScreenShots/Screenshot%20from%202018-10-19%2023-58-47.png)\n\n### Deposit Money Transaction\nthis table holds information for all the deposit transaction for all the user.\n\n![deposit money](https://raw.githubusercontent.com/junedraghib/miniBANK_2.0/master/ScreenShots/Screenshot%20from%202018-10-19%2023-58-51.png)\n\n### Withdrawal Money Transaction\nthis table holds information for the withdrawal by all the user.\n\n![withdrawal money](https://raw.githubusercontent.com/junedraghib/miniBANK_2.0/master/ScreenShots/Screenshot%20from%202018-10-19%2023-58-54.png)\n\n### Transfer Money Transaction\nthis table holds information about the payee and payer account number along with other informations.\n\n![transfer money](https://github.com/junedraghib/miniBANK_2.0/blob/master/ScreenShots/Screenshot%20from%202018-10-19%2023-58-56.png)\n\n## Message from User Interface\nthis interface includes shows all the messages from users and equip with replying mechanism\n![message from user](https://raw.githubusercontent.com/junedraghib/miniBANK_2.0/master/ScreenShots/Screenshot%20from%202018-10-20%2000-00-00.png)\n\n\n## Message Alert and Notification Table\nthis table includes all the messages that are generated by the different type of transactions that were made by all the users.\n\n![messages](https://raw.githubusercontent.com/junedraghib/miniBANK_2.0/master/ScreenShots/Screenshot%20from%202018-10-20%2000-00-24.png)\n\n## Notification to User and Message from User Tables\nthese tables holds the message sent by the user to admin(not working at the moment) and the automated notification generated when a admin sent message to users\n\n![notification](https://raw.githubusercontent.com/junedraghib/miniBANK_2.0/master/ScreenShots/Screenshot%20from%202018-10-20%2000-03-09.png)", + "readme_title": "User Control", + "releases_count": 0, + "repo": "miniBANK_2.0", + "repo_name": "miniBANK_2.0", + "stars": 0, + "topics": [], + "updated_at": "2021-06-19T18:39:05Z", + "url": "https://github.com/hassan11196/miniBANK_2.0", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + 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"file" + }, + { + "path": "gatsby-config.js", + "size": 4297, + "type": "file" + }, + { + "path": "gatsby-node.js", + "size": 4111, + "type": "file" + }, + { + "path": "netlify.toml", + "size": 108, + "type": "file" + }, + { + "path": "package.json", + "size": 2081, + "type": "file" + }, + { + "path": "screenshot.png", + "size": 100259, + "type": "file" + }, + { + "path": "src", + "size": 0, + "type": "dir" + }, + { + "path": "static", + "size": 0, + "type": "dir" + }, + { + "path": "yarn.lock", + "size": 648814, + "type": "file" + } + ], + "files_total_count": 61, + "first_commit_date_default_branch": "2023-09-22T23:17:43Z", + "forks": 0, + "has_ci": true, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/netlify-test107", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "JavaScript", + "size": 42559 + }, + { + "language": "CSS", + "size": 6300 + } + ], + "last_commit_date_default_branch": "2023-09-22T23:58:26Z", + "last_commit_oid_default_branch": "3f5c3ebb15ba9e0afb47975a66622508be964540", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "MIT License", + "license_spdx": "MIT", + "name_with_owner": "hassan11196/netlify-test107", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "JavaScript", + "project_foreign_key": null, + "pull_requests_closed": 7, + "pull_requests_merged": 0, + "pull_requests_open": 3, + "pull_requests_total": 10, + "pushed_at": "2024-01-09T07:38:25Z", + "readme_length": 3686, + "readme_text": "# Netlify CMS __TemplateName__ \n\n## One-click deploy\n\n[![Deploy to Netlify](https://www.netlify.com/img/deploy/button.svg)](https://app.netlify.com/start/deploy?repository=https://github.com/hassan11196/__TemplateName__&utm_source=github)\n\n\n# About the Template - Gatsby Starter Glass\n\nA Minimal & Beautiful Gatsby Personal Blog Starter With Nice Glassmorphism UI.\n\n[View Live Demo](https://gatsbyglass.netlify.app)\n\n![demo site screenshot](./screenshot.png)\n\n## Features\n\n* Fully responsive\n* SEO metadata and Open Graph tags\n* Maximized lighthouse score\n* Contact form with Netlify Form\n* Edit Content with Netlify CMS\n* Easy to deploy\n* Syntax highlighting via PrismJS\n\n## Local Install\n\n```bash\n# 1. Clone the repository\ngit clone https://github.com/yinkakun/gatsby-starter-glass\n\n# 2. Navigate into repository\ncd gatsby-starter-glass\n\n# 3. Install the dependencies\nyarn install\n\n# 4. Start the development server\nyarn start\n\n# 5. Start the build mode\nyarn build\n```\n\n## Configuration\n\nWithin gatsby-config.js, you can specify information about your site (metadata) like the site title and description to properly generate meta tags.\n\n```js\n// gatsby-config.js\n\nmodule.exports = {\n siteMetadata: {\n title: `Gatsby Starter Glass`,\n author: {\n name: `Yinka Adedire`,\n summary: `self-taught front-end dev. jamstack enthusaist.`,\n },\n description: `A Minimal & Beautiful Gatsby Personal Blog Starter With Nice Glassmorphism Ui.`,\n siteUrl: `https://gatsbyglass.netlify.app`,\n social: {\n twitter: `yinkakun`,\n },\n },\n\n // ...\n};\n```\n\n## Deployment\n\nNetlify is a great way to easily deploy sites plus this starter uses Netlify Form for the Contact form.\n\n- Generate a new repo from this repo which will copy all files from this repo to your newly created repo.\n\n- Go to . Once you’ve logged in, click the 'New site from Git' button on your dashboard and choose your newly created repo.\n\n- Follow the prompts, it will build and deploy the new site on Netlify, bringing you to the site dashboard when the build is complete.\n\n## Manually Editing contents\n\n### Blog Posts\n\nBlog contents can be updated in markdown format at `content/blog`. Delete placeholder posts and start blogging.\n\n```md\n---\ntitle: Hello World\ndate: '2021-05-01'\ndescription: 'Hello World'\n---\n\nThis top portion is the beginning of the post and will show up as the excerpt on the homepage.\n```\n\n### Pages\n\nHomepage intro, Contact, and About page content can be updated in Markdown format at `content/pages`.\n\n# Editing Contents with Netlify CMS\n\nThis project is preconfigured to work with Netlify CMS.\nWhen Netlify CMS makes commits to your repo, Netlify will auto-trigger a rebuild / deploy when new commits are made.\nYou’ll need to set up Netlify’s Identity service to authorize users to log in to the CMS.\n\n- Go to > select your website from the list.\n- Go to Identity and click Enable Identity.\n- Click on Invite Users and invite yourself. You will receive an email and you need to accept the invitation to set the password.\n- Now headover to Settings > Identity > Services and Enable Git Gateway.\n- You can also manage who can register and log in to your CMS. Go to Settings > Identity > Registration Registration Preferences. I would prefer to keep it to Invite Only if I am the only one using it.\n- Now, go to to site-name.netlify.app/admin/, and login with your credentials.\n\nOnce you are in your Netlify CMS, you can navigate to Posts and Pages. Here you will find a list of existing pages and posts.\n\n## Built with\n\n- Gatsby for Static Site Generation\n- Netlify CMS for content management\n- Styled Component for styling", + "readme_title": "Netlify CMS __TemplateName__", + "releases_count": 0, + "repo": "netlify-test107", + "repo_name": "netlify-test107", + "stars": 0, + "topics": [], + "updated_at": "2023-09-22T23:12:39Z", + "url": "https://github.com/hassan11196/netlify-test107", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/netlify-test107" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/open-source-template" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 35, + "contributors_count": 5, + "contributors_top": [ + { + "contributions": 23, + "html_url": "https://github.com/joshcanhelp", + "login": "joshcanhelp" + }, + { + "contributions": 6, + "html_url": "https://github.com/ScottRudiger", + "login": "ScottRudiger" + }, + { + "contributions": 4, + "html_url": "https://github.com/kim-codes", + "login": "kim-codes" + }, + { + "contributions": 1, + "html_url": "https://github.com/mosspilot", + "login": "mosspilot" + }, + { + "contributions": 1, + "html_url": null, + "login": "Sean Prashad" + } + ], + "created_at": "2021-10-31T09:42:26Z", + "default_branch": "master", + "description": "A template for open source projects at Auth0", + "dirs_total_count": 6, + "files_root_entries": [ + { + "path": ".github", + "size": 0, + "type": "dir" + }, + { + "path": ".gitignore", + "size": 464, + "type": "file" + }, + { + "path": "CHANGELOG.md", + "size": 652, + "type": "file" + }, + { + "path": "CODE-OF-CONDUCT.md", + "size": 4501, + "type": "file" + }, + { + "path": "CONTRIBUTING.md", + "size": 1113, + "type": "file" + }, + { + "path": "GENERAL-CONTRIBUTING.md", + "size": 4093, + "type": "file" + }, + { + "path": "LICENSE", + "size": 1045, + "type": "file" + }, + { + "path": "README-sample.md", + "size": 6719, + "type": "file" + }, + { + "path": "README.md", + "size": 2893, + "type": "file" + }, + { + "path": "codecov.yml", + "size": 405, + "type": "file" + }, + { + "path": "example.env", + "size": 1083, + "type": "file" + }, + { + "path": "javascript", + "size": 0, + "type": "dir" + }, + { + "path": "php", + "size": 0, + "type": "dir" + }, + { + "path": "python", + "size": 0, + "type": "dir" + }, + { + "path": "ruby", + "size": 0, + "type": "dir" + } + ], + "files_total_count": 20, + "first_commit_date_default_branch": "2018-07-25T21:19:44Z", + "forks": 0, + "has_ci": true, + "has_contributing": true, + "has_docker": false, + "has_docs": false, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": "https://auth0.com/opensource", + "input_url": "https://github.com/hassan11196/open-source-template", + "is_archived": false, + "is_fork": true, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [], + "last_commit_date_default_branch": "2019-04-05T19:30:58Z", + "last_commit_oid_default_branch": "b2518ea25bbae710f38456423bbd51da50424b02", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "MIT License", + "license_spdx": "MIT", + "name_with_owner": "hassan11196/open-source-template", + "owner": "hassan11196", + "parent_repo": "auth0/open-source-template", + "parent_url": "https://github.com/auth0/open-source-template", + "primary_language": null, + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2020-11-12T08:31:32Z", + "readme_length": 2892, + "readme_text": "# Auth0 Open Source Template\n\nThis repo contains sample files to be used in Auth0 open source software repositories. These files are guidelines that can be used in an Auth0 or any open source project to ensure that the project is described properly, the contribution process is clear, and additional tools like CI, coverage monitors, and quality checkers are configured properly.\n\n## Table of Contents\n\n- [Getting Started](#getting-started)\n- [Contribution](#contribution)\n- [Thank You](#thank-you)\n- [License](#license)\n\n## Getting Started\n\nThis repo can be cloned, modified, then re-purposed for new open source projects. It can also be used piecemeal to improve existing repos.\n\n### Copy README-sample.md to README.md\n\nThe core of open source repo quality is the `README.md` file that's displayed on the repo's homepage in Github. The `README-sample.md` included here was collaboratively created by Auth0's SDK engineering team and community engineering team based on samples of high-quality README files around the web.\n\nMost sections within this file should be considered required, even if that section contains a link to a more relevant page. Certain sections will contain repo-specific information and should be modified as needed:\n\n- Description (below the title)\n- Badges\n- Documentation\n- Installation\n- Getting Started\n- Thank you\n- License\n\nOther sections should be used as-is with minimal changes:\n\n- Table of Contents\n- Contribution\n- Support + Feedback\n- Vulnerability Reporting\n- What is Auth0?\n\n### Modify templates in `.github`\n\nThe issue and PR templates contained in this repo are general guidelines for what to include. What is being asked for is entirely up to the repo maintainer but a template of some kind should be included.\n\n### Update LICENSE\n\nSee `README-sample.md` for guidelines.\n\n### Modify .gitignore\n\nThis file contains files that will be excluded from git. Modify as needed based on the technology being used.\n\n### Modify/remove platform-specific files\n\nAdditional template files have been included here for the specific technology that we work with. These should be considered as guidelines and not enforced in any way.\n\n## Contribution\n\nWe appreciate feedback and contribution to this template! Before you get started, please see the following:\n\n- [Auth0's general contribution guidelines](GENERAL-CONTRIBUTING.md)\n- [Auth0's code of conduct guidelines](CODE-OF-CONDUCT.md)\n\n## Thank You!\n\nA big thank you to all of our sources of inspiration!\n\n- [First Contributions by @Roshanjossey](https://github.com/Roshanjossey/first-contributions)\n- [First Timers Only](https://www.firsttimersonly.com/)\n- [Sane Github Labels](https://medium.com/@dave_lunny/sane-github-labels-c5d2e6004b63)\n- [Awesome README by @matiassingers](https://github.com/matiassingers/awesome-readme)\n\n... and many more!\n\n## License\n\nThis repo is covered under [The MIT License](LICENSE).", + "readme_title": "Auth0 Open Source Template", + "releases_count": 0, + "repo": "open-source-template", + "repo_name": "open-source-template", + "stars": 0, + "topics": [], + "updated_at": "2021-10-31T09:42:27Z", + "url": "https://github.com/hassan11196/open-source-template", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/open-source-template" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/openshift-cronjob-example" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 8, + "contributors_count": 1, + "contributors_top": [ + { + "contributions": 8, + "html_url": "https://github.com/clcollins", + "login": "clcollins" + } + ], + "created_at": "2023-08-31T10:17:11Z", + "default_branch": "master", + "description": "An example OpenShift cronjob, demonstrating many features of Kubernetes and OpenShift", + "dirs_total_count": 0, + "files_root_entries": [ + { + "path": "LICENSE", + "size": 7048, + "type": "file" + }, + { + "path": "README.md", + "size": 17818, + "type": "file" + }, + { + "path": "app.py", + "size": 2508, + "type": "file" + }, + { + "path": "buildConfig.yml", + "size": 469, + "type": "file" + }, + { + "path": "cronJob.yml", + "size": 826, + "type": "file" + }, + { + "path": "requirements.txt", + "size": 21, + "type": "file" + } + ], + "files_total_count": 6, + "first_commit_date_default_branch": "2018-12-03T18:37:00Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/openshift-cronjob-example", + "is_archived": false, + "is_fork": true, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Python", + "size": 2508 + } + ], + "last_commit_date_default_branch": "2018-12-11T01:38:08Z", + "last_commit_oid_default_branch": "39b975f6d90c000edf6bfe712bc78999a1a3d279", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "Creative Commons Zero v1.0 Universal", + "license_spdx": "CC0-1.0", + "name_with_owner": "hassan11196/openshift-cronjob-example", + "owner": "hassan11196", + "parent_repo": "clcollins/openshift-cronjob-example", + "parent_url": "https://github.com/clcollins/openshift-cronjob-example", + "primary_language": "Python", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2019-04-17T19:22:47Z", + "readme_length": 17817, + "readme_text": "# Create, Build and Deploy a CronJob in OKD \n\n_Skill Level: 2 out of 5_\n\nGetting started with Kubernetes and OKD (a Kubernetes distribution; formerly known as OpenShift Origin) can be daunting. There are a lot of concepts and components to take in and understand. This example walks through the creation of an example Kubernetes CronJob which uses a service account and Python script to list all the pods in the current project/namespace. The actual job itself is relatively useless, but the process of setting it up will help introduce many parts of the Kubernetes & OKD infrastructure. In addition, the Python script itself is a good example of using the OKD RestAPI for cluster-related tasks.\n\nThis example covers several pieces of the Kubernetes/OKD infrastructure, including:\n\n* ServiceAccounts and Tokens\n* Role-Based-Access-Control (RBAC)\n* ImageStreams\n* BuildConfigs\n* Source-To-Image\n* Jobs and CronJobs (mostly the latter)\n* Kubernetes DownwardAPI\n\n_Note: The Python script and example OKD YAML files for this example can be found in the GitHub repository: [https://github.com/clcollins/openshift-cronjob-example](https://github.com/clcollins/openshift-cronjob-example), and can be used as-ios with the exception of replacing the `value: https://okd.host:port` line in 'cronJob.yml' with the hostname of your OKD instance.\n\n## Requirements\n\n### Software\n\n* An OKD or MiniShift cluster with the default Source-to-Image ImageStreams installed, and an integrated image registry\n\n\n### Optional, for playing with the Python script\n\n1. Python3\n2. [OpenShift-RestClient-Python](https://github.com/openshift/openshift-restclient-python)\n3. [Kubernetes Python Client](https://github.com/kubernetes-client/python)\n\n_Note:_ Run `pip3 install --user openshift kubernetes` to install the modules.\n\n\n### Authentication\n\nThe Python script will use the ServiceAccount API token to authenticate to OKD. The script expects an environment variable pointing to the OKD host it should be connecting to:\n\n* HOST: OKD host to connect to, eg: ()\n\nAdditionally, OKD will need the token created automatically for the ServiceAccount that will run the CronJob pods(see \"How Environment Variables and Tokens Are Used\", below).\n\n\n## Process\n\nSetting up this sync was a good learning experience. There are a number of Kuberentes and OKD tasks that needed to be done, and it gives a good feel for various features.\n\nThe general process works as follows:\n\n1. Create a new project\n2. Create a Git repository with the python script in it\n3. Create a service account\n4. Grant RBAC permissions to the service account\n6. How environment variables and tokens are used\n7. Create an `ImageStream` to accept the images created by the `BuildConfig`\n8. Create a `BuildConfig` to turn the python script into an image/ImageStream\n9. Build the image\n10. Create the cron job\n\n\n### Create a new Project\n\nCreate a new project in OKD to use for this exercise:\n\n`oc new-project py-cron`\n\n_Note: Depending on how your cluster is setup, you may need to ask your cluster administrator to create a new project for you._\n\n\n### Create a Git repository with the python script\n\nClone or fork this repo, `https://github.com/clcollins/openshift-cronjob-example.git`, or alternatively reference it directly in the code examples below. This will serve as the repository from which the python script will be pulled and built into the final running image.\n\n\n### Create a ServiceAccount\n\nA service account is a non-user account that can be associated with resources, permissions, etc.. within OKD. For this exercise, a service account needs to be created to run the pod with the Python script in it, authenticate to the OKD API via token auth, and make RestAPI calls to list all the pods.\n\nSince the python script will query the OKD RestAPI to get a list of pods in the namespace, the ServiceAccount will need permissions to `list pods` and `namespaces`. Technically, one of the default service accounts that are automatically created in a project - `system:serviceaccount:default:deployer` - already has the permissions for this. However this exercise will create a new ServiceAccount as an example of both ServiceAccount creation and RBAC permissions.\n\nCreate a new service account:\n\n```\noc create serviceaccount py-cron\n```\n\nThis creates a ServiceAccount named, appropriately, `py-cron`. (Technically, `system:serviceaccounts:py-cron:py-cron`, the \"py-cron\" serviceaccount in the \"py-cron\" namespace) The account automatically receives two secrets - an OKD API token, and credentials for the OKD Container Registry. The API token will be used in the python script to identify the service account to OKD for RestAPI calls.\n\nThe tokens associated with the ServiceAccount can be viewed with the `oc describe serviceaccount py-cron` command.\n\n\n### Grant RBAC permissions for the ServiceAccount\n\nOKD and Kubernetes use RBAC, or [Role-based Access Control](https://docs.okd.io/3.11/admin_guide/manage_rbac.html) to allow fine-grained control of who can do what in a complicated cluster.\n\n**Quick and Dirty RBAC Overview:**\n\n1. Permissions are based on `verbs` and `resources` - eg. \"create group\", \"delete pod\", etc..\n2. Sets of permissions are grouped into `roles` or `clusterroles`, the latter being, predictably, cluster-wide.\n3. Roles and ClusterRoles are associated with (ie: `bound` to) `groups` and `ServiceAccounts`, or, if you want to do it all wrong, individual `users`, by creating `RoleBindings` or `ClusterRoleBindings`.\n4. Groups, ServiceAccounts and users can be bound to multiple roles.\n\nFor this exercise, create a `role` within the project, grant the role permissions to list pods and projects, and bind the `py-cron` ServiceAccount to the role:\n\n```\noc create role pod-lister --verb=list --resource=pods,namespaces\noc policy add-role-to-user pod-lister --role-namespace=py-cron system:serviceaccounts:py-cron:py-cron\n```\n\n_Note: `--role-namespace=py-cron` has to be added to prevent OKD from looking for clusterRoles_\n\nYou can verify the ServiceAccount has been bound to the role:\n\n```\noc get rolebinding | awk 'NR==1 || /^pod-lister/'\nNAME ROLE USERS GROUPS SERVICE ACCOUNTS SUBJECTS\npod-lister py-cron/pod-lister py-cron\n```\n\n### How Environment Variables and Tokes are used\n\nThe tokens associated with the ServiceAccount and various environment variables are referenced in the python script.\n\n**py-cron API token**\n\nThe API token automatically created for the \"py-cron\" ServiceAccount is mounted by OKD into any pod the ServiceAccount is running. This token mounted to a specific path in every container in the pod:\n\n`/var/run/secrets/kubernetes.io/serviceaccount/token`\n\nThe python script reads that file and uses it to authenticate to the OKD API to mange the groups.\n\n**HOST environment variable**\n\nThe HOST environment variable is specified in the CronJob definition, and contains the OKD API hostname, in the format ().\n\n\n**NAMESPACE environment variable**\n\nThe NAMESPACE environment variable is referenced in the CronJob definition, and uses the [Kubernetes DownwardAPI](https://kubernetes.io/docs/tasks/inject-data-application/environment-variable-expose-pod-information/) to dynamically populate the variable with the name of the project the CronJob pod is being run in.\n\n\n### Create an ImageStream\n\nAn ImageStream is a collection of images, in this case created by the BuildConfig builds, and is an abstraction layer between images and Kubernetes objects, allowing them to reference the image stream rather than the image directly.\n\nTo push a newly-built image to an ImageStream, that stream must already exist, so a new, empty stream must be created. The easiest way to do this is with the `oc` command-line command:\n\n```\noc create imagestream py-cron\n```\n\n\n### Create a BuildConfig\n\nThe BuildConfig is the definition of the entire build process - the act of taking input parameters and code and turning it into an image.\n\nThe BuildConfig for this exercise will make use of the [source-to-image build strategy](https://docs.okd.io/3.11/architecture/core_concepts/builds_and_image_streams.html#source-build), using the Red Hat-provided Python source-to-image image, and adding the Python script to it, where the requirements.txt is parsed and those modules installed. This results in a final python-based image with the script and the required python modules to run it.\n\nThe important pieces of the BuildConfig are:\n\n* .spec.output\n* .spec.source\n* .spec.strategy\n\n**.spec.output**\n\nThe output section of the BuildConfig describes what to do with the output of the build. In this case, the BuildConfig outputs the resulting image as an ImageStreamTag, `py-cron:1.0`, that can be used in the DeploymentConfig to reference the image.\n\nThese are probably self-explanatory.\n\n```\nspec:\n output:\n to:\n kind: ImageStreamTag\n name: py-cron:1.0\n```\n\n**.spec.source**\n\nThe source section of the BuildConfig describes where the content of the build is coming from. In this case, it references the git repository where the python script and its supporting files are kept.\n\nMost of these are self-explanatory as well.\n\n```\nspec:\n source:\n type: Git\n git:\n ref: master\n uri: https://github.com/clcollins/openshift-cronjob-example.git\n```\n\n**.spec.strategy**\n\nThe strategy section of the BuildConfig describes the build strategy to use, in this case the source (ie. source-to-image) strategy. The `.spec.strategy.sourceStrategy.from` section defines the public Red Hat-provided Python 3.6 ImageStream that exists in the default `openshift` namespace for folks to use. This ImageStream contains source-to-image builder images that take Python code as input, install the dependencies listed in any `requirements.txt` files, and then output a finished image with the code and requirements installed.\n\n```\nstrategy:\n type: Source\n sourceStrategy:\n from:\n kind: ImageStreamTag\n name: python:3.6\n namespace: openshift\n```\n\nThe complete BuildConfig for this example looks like the YAML below. Substitute the Git repo you're using below, and create the BuildConfig with the oc command: `oc create -f `\n\n```\n---\napiVersion: build.openshift.io/v1\nkind: BuildConfig\nmetadata:\n labels:\n app: py-cron\n name: py-cron\nspec:\n output:\n to:\n kind: ImageStreamTag\n name: py-cron:1.0\n runPolicy: Serial\n source:\n type: Git\n git:\n ref: master\n uri: https://github.com/clcollins/openshift-cronjob-example.git\n strategy:\n type: Source\n sourceStrategy:\n from:\n kind: ImageStreamTag\n name: python:3.6\n namespace: openshift\n```\n\n\n### Build the Image\n\nMost of the time, it would be more efficient to add a webhook trigger to the BuildConfig, to allow the image to be automatically rebuilt each time code is committed and pushed to the repo. For the purposes of this exercise, however, the image build will be kicked off manually whenever the image needs to be updated.\n\nA new build can be triggered by running:\n\n```\noc start-build BuildConfig/py-cron\n```\nRunning this command outputs the name of a Build, for example, `build.build.openshift.io/py-cron-1 started`.\n\nProgress of the build can be followed by watching the logs:\n\n```\noc logs -f build.build.openshift.io/py-cron-1\n```\n\nWhen the build completes, the image will be pushed to the ImageStream listed in the `.spec.output` section of the BuildConfig.\n\n\n### Create the CronJob\n\nThe [Kubernetes CronJob](https://kubernetes.io/docs/tasks/job/automated-tasks-with-cron-jobs/#writing-a-cron-job-spec) object defines the cron schedule and behavior, as well as the [Kuberentes Job](https://kubernetes.io/docs/concepts/workloads/controllers/jobs-run-to-completion/) that is created to run the actual sync.\n\nThe important parts of the CronJob definition are:\n\n* .spec.concurrencyPolicy\n* .spec.schedule\n* .spec.JobTemplate.spec.template.spec.containers\n\n**.spec.concurrencyPolicy**\n\nThe concurrencyPolicy field of the CronJob spec is an optional field that specifies how to treat concurrent executions of a job that is created by this cron job. In the case of this exercise, it will replace an existing job that may still be running if the CronJob creates a new job.\n\n_Note:_ Other options are to allow concurrency - ie. multiple jobs running at once, or forbid concurrency - ie. new jobs are skipped until the running jobs completes.\n\n**.spec.schedule**\n\nThe schedule field of the CronJob spec is unsurprisingly a vixie cron-format schedule. At the time(s) specified, Kubernetes will create a Job, as defined in the JobTemplate spec, below.\n\n```\nspec:\n schedule: \"*/5 * * * *\"\n```\n\n**.spec.JobTemplate.spec.template.spec.containers**\n\nThe CronJob spec contains in itself a JobTemplate spec and template spec, which in turn contains a container spec. All of these follow the standard spec for their type, ie: the `.spec.containers` section is just a normal container definition you might find in any other pod definition.\n\nThe container definition for this example is a straightforward container definition, using the EnvironmentVariables discussed already.\n\nThe only important part is `.spec.JobTemplate.spec.template.spec.containers.ServiceAccountName`. This section sets the ServiceAccount created earlier, `py-cron`, as the account running the containers. This overrides the default `deployer` ServiceAccount.\n\nOKD complete CronJob for py-cron looks like the YAML below. Substitute the URL of the OKD cluster API and create the CronJob using the oc command: `oc create -f `\n\n```\n---\napiVersion: batch/v1beta1\nkind: CronJob\nmetadata:\n labels:\n app: py-cron\n name: py-cron\nspec:\n concurrencyPolicy: Replace\n failedJobsHistoryLimit: 1\n JobTemplate:\n metadata:\n annotations:\n alpha.image.policy.openshift.io/resolve-names: '*'\n spec:\n template:\n spec:\n containers:\n - env:\n - name: NAMESPACE\n valueFrom:\n fieldRef:\n fieldPath: metadata.namespace\n - name: HOST\n value: https://okd.host:port\n image: py-cron/py-cron:1.0\n imagePullPolicy: Always\n name: py-cron\n ServiceAccountName: py-cron\n restartPolicy: Never\n schedule: \"*/5 * * * *\"\n startingDeadlineSeconds: 600\n successfulJobsHistoryLimit: 3\n suspend: false\n```\n\n## Looking around\n\nOnce the CronJob has been created, \n\n\nFirst, the CronJob component can be viewed with the `oc get cronjob` command. This shows a brief description of the CronJob, its schedule and last run, and whether or not it is active or paused: \n\n```\noc get cronjob py-cron\nNAME SCHEDULE SUSPEND ACTIVE LAST SCHEDULE AGE\npy-cron */5 * * * * False 0 1m 7d\n```\n\nAs already mentioned, the CronJob creates Kubernetes Jobs to do the actual work, whenever the scheduled time passes. The `oc get jobs` command lists the jobs that have been created by the CronJob, the number of desired jobs (in this case, just one job per run), and whether or not the job was successful:\n\n\n```\noc get jobs\nNAME DESIRED SUCCESSFUL AGE\npy-cron-1544489700 1 1 10m\npy-cron-1544489760 1 1 5m\npy-cron-1544489820 1 1 30s\n```\n\nThe Jobs themselves are pods, which can be seen with the `oc get pods` command. Visible in this example are both the job pods (named after the job that created them, eg: Job \"py-cron-1544489760\" created pod \"py-cron-1544489760-xl4vt\"). There are also build pods, or the pods that built the container image as described in the BuildConfig created above.\n\n```\noc get pods\nNAME READY STATUS RESTARTS AGE\npy-cron-1-build 0/1 Completed 0 7d\npy-cron-1544489760-xl4vt 0/1 Completed 0 10m\npy-cron-1544489820-zgfg8 0/1 Completed 0 5m\npy-cron-1544489880-xvmsn 0/1 Completed 0 44s\npy-cron-2-build 0/1 Completed 0 7d\npy-cron-3-build 0/1 Completed 0 7d\n```\n\nFinally, because this example is just a Python script that connects to OKD's RestAPI to get information about pods in the project, `oc get logs` can be used verify that the script is working by getting the logs from the pod to see the output of the script written to standard out:\n\n```\noc logs py-cron-1544489880-xvmsn\n---> Running application from Python script (app.py) ...\nResourceInstance[PodList]:\n apiVersion: v1\n items:\n - metadata:\n annotations: {openshift.io/build.name: py-cron-1, openshift.io/scc: privileged}\n creationTimestamp: '2018-12-03T18:48:39Z'\n labels: {openshift.io/build.name: py-cron-1}\n name: py-cron-1-build\n namespace: py-cron\n ownerReferences:\n - {apiVersion: build.openshift.io/v1, controller: true, kind: Build, name: py-cron-1,\n uid: 0c9cf9a8-f72c-11e8-b217-005056a1038c}\n\n\n```\n\n## Conclusion\n\nThis example project showed how to create a Kubernetes CronJob with OKD, a Kubernetes distribution formerly called OpenShift Origin. The BuildConfig described how to build the container image containing the Python script which calles the OKD RestAPI, and an ImageStream was created to describe the different versions of the image built by the Builds. A ServiceAccount was created to run the container and a Role created to allow the ServiceAccount to get pod information from OKD's RestAPI. A RoleBinding associated the Role with the ServiceAccount. Finally, a CronJob was created to run a Job - the container with the Python script - on a specified schedule.", + "readme_title": "Create, Build and Deploy a CronJob in OKD", + "releases_count": 0, + "repo": "openshift-cronjob-example", + "repo_name": "openshift-cronjob-example", + "stars": 0, + "topics": [], + "updated_at": "2023-08-31T10:17:11Z", + "url": "https://github.com/hassan11196/openshift-cronjob-example", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/openshift-cronjob-example" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/paige" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 1389, + "contributors_count": 3, + "contributors_top": [ + { + "contributions": 1380, + "html_url": "https://github.com/willfaught", + "login": "willfaught" + }, + { + "contributions": 8, + "html_url": "https://github.com/thehouseplant", + "login": "thehouseplant" + }, + { + "contributions": 1, + "html_url": null, + "login": "Saurabh Mishra" + } + ], + "created_at": "2023-10-22T17:52:56Z", + "default_branch": "master", + "description": "Powerful, pliable pixel perfection. An advanced Hugo theme.", + "dirs_total_count": 99, + "files_root_entries": [ + { + "path": ".github", + "size": 0, + "type": "dir" + }, + { + "path": ".gitignore", + "size": 39, + "type": "file" + }, + { + "path": "LICENSE", + "size": 11358, + "type": "file" + }, + { + "path": "README.md", + "size": 37873, + "type": "file" + }, + { + "path": "archetypes", + "size": 0, + "type": "dir" + }, + { + "path": "assets", + "size": 0, + "type": "dir" + }, + { + "path": "exampleSite", + "size": 0, + "type": "dir" + }, + { + "path": "go.mod", + "size": 44, + "type": "file" + }, + { + "path": "hugo.toml", + "size": 2173, + "type": "file" + }, + { + "path": "i18n", + "size": 0, + "type": "dir" + }, + { + "path": "images", + "size": 0, + "type": "dir" + }, + { + "path": "layouts", + "size": 0, + "type": "dir" + }, + { + "path": "paige", + "size": 0, + "type": "dir" + }, + { + "path": "static", + "size": 0, + "type": "dir" + }, + { + "path": "theme.toml", + "size": 906, + "type": "file" + } + ], + "files_total_count": 2997, + "first_commit_date_default_branch": "2022-09-08T00:46:20Z", + "forks": 0, + "has_ci": true, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": "https://willfaught.com/paige", + "input_url": "https://github.com/hassan11196/paige", + "is_archived": false, + "is_fork": true, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "HTML", + "size": 97720 + }, + { + "language": "CSS", + "size": 10497 + }, + { + "language": "SCSS", + "size": 104 + } + ], + "last_commit_date_default_branch": "2023-10-21T21:13:36Z", + "last_commit_oid_default_branch": "89dfc51c9a20c34e1af84ab97b16a77af3449493", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "Apache License 2.0", + "license_spdx": "Apache-2.0", + "name_with_owner": "hassan11196/paige", + "owner": "hassan11196", + "parent_repo": "willfaught/paige", + "parent_url": "https://github.com/willfaught/paige", + "primary_language": "HTML", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2023-10-21T21:14:52Z", + "readme_length": 37870, + "readme_text": "# Paige\n\nPowerful, pliable pixel perfection. An advanced Hugo theme. [Try it out.](https://willfaught.com/paige)\n\n\n \n \n \"Screenshot\"\n\n\nPaige is designed to put your content front and center,\navoiding the typical clutter.\nThe look is seamless and smooth, scalable and readable, portable and efficient.\nThe layout is minimal and responsive,\nusing verticality and white space to focus and delineate parts of the page.\nThe implementation is flexible and extensible.\nIt's a versatile canvas that serves most web needs.\n\n## Features\n\n- Accessibility\n- Analytics\n- Atom feed\n- Authors\n- Blog\n- Bootstrap\n- Categories\n- Code shortcode\n- Comments\n- Customizable\n- Dark color scheme\n- Email shortcode\n- Facebook sharing\n- Figure shortcode\n- Gallery shortcode\n- Header links\n- Icon shortcode\n- Image pixel densities\n- Image shortcode\n- Image size breakpoints\n- Image thumbnails\n- Landing page\n- Languages\n- Light color scheme\n- Math typesetting\n- Menu\n- Minimal design\n- Quote shortcode\n- RSS feed\n- Responsive\n- Right-to-left languages\n- SEO\n- Safari and Firefox Reader View support\n- Search\n- Sections\n- Series\n- Single column\n- Social links\n- Table of contents\n- Tags\n- Twitter sharing\n- Validated with [PageSpeed](https://pagespeed.web.dev/report?url=https%3A%2F%2Fwillfaught.com%2Fpaige%2F&form_factor=desktop) and [Validator](https://validator.w3.org/nu/?doc=https%3A%2F%2Fwillfaught.com%2Fpaige%2F)\n- Vimeo shortcode\n- YouTube shortcode\n\n## Community\n\nGet started by [starring](https://github.com/willfaught/paige/stargazers)\nand [watching](https://github.com/willfaught/paige/watchers) the project.\n\nIf you find a problem or have a suggestion,\nplease share it by [creating an issue](https://github.com/willfaught/paige/issues/new).\n\nIf you have a fix or improvement,\nplease share it by [creating a pull request](https://github.com/willfaught/paige/compare).\n\nIf you make a customization or alteration,\nplease share it by [posting code or screenshots](https://github.com/willfaught/paige/discussions/8).\n\nIf you deploy a site,\nplease share it by [posting a link](https://github.com/willfaught/paige/discussions/12).\n\n## Setup\n\n1. [Install Hugo](https://gohugo.io/installation/) (the extended version, and at least 0.111.3).\n\n For Homebrew on Mac:\n\n ```sh\n $ brew install hugo\n ```\n\n For Chocolatey on Windows:\n\n ```sh\n $ choco install hugo-extended\n ```\n\n For Snap on Linux:\n\n ```sh\n $ sudo snap install hugo\n ```\n\n2. [Install Dart Sass](https://github.com/sass/dart-sass/releases).\n\n For Homebrew on Mac:\n\n ```sh\n $ brew install sass/sass/sass\n ```\n\n For Chocolatey on Windows:\n\n ```sh\n $ choco install sass\n ```\n\n For Snap on Linux:\n\n ```sh\n $ sudo snap install dart-sass\n ```\n\n3. Create a site:\n\n ```sh\n $ hugo new site yourproject\n ```\n\n4. Create a post:\n\n ```sh\n $ cd yourproject\n $ hugo new yourpost.md\n ```\n\n5. Install the Paige module:\n\n ```sh\n $ cd yourproject\n $ hugo mod init github.com/youraccount/yourproject\n $ hugo mod get github.com/willfaught/paige@latest\n ```\n\n6. Import the Paige module:\n\n ```sh\n $ cd yourproject\n $ cat >>hugo.toml <}}\n\n{{< /paige/code >}}\n```\n\nInner content: Required. String. The code.\n\nParameters:\n\n
\n
lang
\n
Optional. Position 0. String. Chroma language code. Default is plaintext.
\n
options
\n
Optional. String. Hugo highlight options.
\n
\n\n### Email\n\nThe `paige/email` shortcode hides e-mail addresses from robots.\n\n```\n{{< paige/email\n address=\"\" >}}\n\n{{< /paige/email >}}\n```\n\nInner content: Required. String. Markdown. The content.\n\nParameters:\n\n
\n
address
\n
Required. Position 0. String. The e-mail address.
\n
\n\n### Figure\n\nThe `paige/figure` shortcode provides a figure.\n\n```\n{{< paige/figure\n caption=\"\"\n float=\"\"\n height=\"\"\n horizontal=\"\"\n maxwidth=\"\"\n number=0\n numbered=false\n vertical=\"\"\n width=\"\" >}}\n\n{{< /paige/figure >}}\n```\n\nInner content: Required. String. Markdown. The content.\n\nParameters:\n\n
\n
caption
\n
Optional. Position 0. String. Markdown. Descriptive text below the content.
\n
float
\n
Optional. String. Float to one side of its container. Must be start or end.
\n
height
\n
Optional. String. CSS value. Total height.
\n
horizontal
\n
Optional. String. Horizontal alignment. Must be start, center, or end. Default is center.
\n
maxwidth
\n
Optional. String. CSS value. Maximum total width.
\n
number
\n
Optional. Integer or string. Figure number. Displayed with the caption.
\n
numbered
\n
Optional. Boolean. Number the figure automatically. Displayed with the caption.
\n
vertical
\n
Optional. String. Vertical alignment. Must be start, center, or end. Default is center.
\n
width
\n
Optional. String. CSS value. Total width.
\n
\n\n### Gallery\n\nThe `paige/gallery` shortcode provides an arrangement of images.\n\n```\n{{< paige/gallery\n align=\"\"\n breakpoints=false\n class=\"\"\n densities=\"\"\n fetchpriority=\"\"\n height=\"\"\n images=\"\"\n justify=\"\"\n linked=\"\"\n loading=\"\"\n maxheight=\"\"\n maxwidth=\"\"\n process=\"\"\n style=\"\"\n type=\"\"\n width=\"\" >}}\n\n{{< /paige/gallery >}}\n```\n\nInner content: Optional. String. HTML. The images.\n\nParameters:\n\n
\n
align
\n
Optional. String. Cross axis alignment. Must be baseline, center, end, start, or stretch.
\n
breakpoints
\n
Optional. Boolean. Whether to generate copies of the image sized to each Bootstrap breakpoint. Must not use with densities.
\n
class
\n
Optional. String. Image class. Merged with implementation.
\n
densities
\n
Optional. String. Float numbers suffixed with an \"x\", delimited by spaces. The pixel densities of the image to generate. There must be at least two. The largest density matches the original image. Examples are 1x 2x, 1x 1.5x 2x 2.5x 3x, and 0.5x 1.33x 6x 10x. Must not use with breakpoints.
\n
fetchpriority
\n
Optional. String. Must be high or low.
\n
height
\n
Optional. String. CSS value. Image height.
\n
images
\n
Optional. Position 0. String. Page, site, or remote images glob. Default is all image page resources.
\n
justify
\n
Optional. String. Main axis space distribution. Must be around, between, center, end, evenly, or start.
\n
linked
\n
Optional. String. Hugo image processing methods and options, mixed together, for the linked image. If no method is specified, resize is used. If no image dimensions are specified, the originals are used. If it is unprocessed, it is unprocessed. If it is default, the default options are used.
\n
loading
\n
Optional. String. Must be eager (default) or lazy.
\n
maxheight
\n
Optional. String. CSS value. Maximum image height.
\n
maxwidth
\n
Optional. String. CSS value. Maximum image width.
\n
process
\n
Optional. String. Hugo image processing methods and options, mixed together, for the displayed image. If no method is specified, resize is used. If no image dimensions are specified, the originals are used. If it is default, the default options are used.
\n
style
\n
Optional. String. CSS. Image style. Merged with implementation.
\n
type
\n
Optional. String. Type of layout. Must be grid or rows (default).
\n
width
\n
Optional. String. CSS value. Image width.
\n
\n\n### Icon\n\nThe `paige/icon` shortcode provides a web font icon.\n\n```\n{{< paige/icon\n class=\"\"\n title=\"\"\n url=\"\" >}}\n```\n\nInner content: None.\n\nParameters:\n\n
\n
class
\n
Required. Position 0. String. <i> class. Example is bi bi-github.
\n
title
\n
Optional. String. The screen reader description and anchor title. Example is GitHub.
\n
url
\n
Optional. String. URL. Example is https://github.com/willfaught/paige. E-mails in URLs with a mailto: scheme are protected from web crawlers.
\n
\n\n### Image\n\nThe `paige/image` shortcode provides an image.\n\n```\n{{< paige/image\n alt=\"\"\n breakpoints=false\n class=\"\"\n densities=\"\"\n fetchpriority=\"\"\n height=\"\"\n link=\"\"\n linked=\"\"\n loading=\"\"\n maxheight=\"\"\n maxwidth=\"\"\n process=\"\"\n sizes=\"\"\n src=\"\"\n srcset=\"\"\n style=\"\"\n title=\"\"\n width=\"\" >}}\n```\n\nInner content: None.\n\nParameters:\n\n
\n
alt
\n
Optional. String. Plain text. Image alt.
\n
breakpoints
\n
Optional. Boolean. Whether to generate copies of the image sized to each Bootstrap breakpoint. Must not use with densities, sizes, or srcset.
\n
class
\n
Optional. String. Class attribute value.
\n
densities
\n
Optional. String. Float numbers suffixed with an \"x\", delimited by spaces. The pixel densities of the image to generate. There must be at least two. The largest density matches the original image. Examples are 1x 2x, 1x 1.5x 2x 2.5x 3x, and 0.5x 1.33x 6x 10x. Must not use with breakpoints, sizes, or srcset.
\n
fetchpriority
\n
Optional. String. Must be high or low.
\n
height
\n
Optional. String. CSS value. Image height.
\n
link
\n
Optional. String. URL. Image link. Must not use with linked.
\n
linked
\n
Optional. String. Hugo image processing methods and options, mixed together, for the linked image. If no method is specified, resize is used. If no image dimensions are specified, the originals are used. If it is unprocessed, it is unprocessed. If it is default, the default options are used. Must not use with link.
\n
loading
\n
Optional. String. Must be eager (default) or lazy.
\n
maxheight
\n
Optional. String. CSS value. Image maximum height.
\n
maxwidth
\n
Optional. String. CSS value. Image maximum width.
\n
process
\n
Optional. String. Hugo image processing methods and options, mixed together, for the displayed image. If no method is specified, resize is used. If no image dimensions are specified, the originals are used. If it is default, the default options are used.
\n
sizes
\n
Optional. String. Image sizes. Must not be set with breakpoints or densities.
\n
src
\n
Required. Position 0. String. URL. Image source.
\n
srcset
\n
Optional. String. Image srcset. Must not be set with breakpoints or densities.
\n
style
\n
Optional. String. CSS. Image style. Merged with implementation.
\n
title
\n
Optional. String. Plain text. Image title.
\n
width
\n
Optional. String. CSS value. Image width.
\n
\n\n### Quote\n\nThe `paige/quote` shortcode provides a quotation.\n\n```\n{{< paige/quote\n cite=\"\" >}}\n\n{{< /paige/quote >}}\n```\n\nInner content: Required. String. Markdown. The quotation.\n\nParameters:\n\n
cite
\n
Optional. String. Plain text. Citation.
\n\n### Vimeo\n\nThe `paige/vimeo` shortcode provides a Vimeo video.\n\n```\n{{< paige/vimeo\n autopause=false\n autoplay=false\n background=false\n byline=false\n color=\"\"\n controls=false\n description=\"\"\n dnt=false\n fullscreen=false\n height=\"\"\n keyboard=false\n loop=false\n maxheight=\"\"\n maxwidth=\"\"\n muted=false\n pip=false\n playsinline=false\n portrait=false\n quality=\"\"\n speed=false\n texttrack=false\n time=\"\"\n title=false\n transparent=false\n video=\"\"\n width=\"\" >}}\n```\n\nInner content: None.\n\nParameters:\n\n
\n
autopause
\n
Optional. Boolean. Enable playing more than one Vimeo video on the same page. Default is true.
\n
autoplay
\n
Optional. Boolean. Autoplay the video. Default is false.
\n
background
\n
Optional. Boolean. Autoplay the video. Hide the controls. Loop the video. Mute the video. Default is false.
\n
byline
\n
Optional. Boolean. Show the author. Default is configured per video.
\n
color
\n
Optional. String. Hex code. Control color. Default is 00adef.
\n
controls
\n
Optional. Boolean. Show the controls. Default is true.
\n
description
\n
Optional. String. Plain text. Screen reader content. Default is Vimeo video.
\n
dnt
\n
Optional. Boolean. Do not track session data. Default is false.
\n
fullscreen
\n
Optional. Boolean. Enable full screen. Default is true.
\n
height
\n
Optional. String. CSS value. Video height.
\n
keyboard
\n
Optional. Boolean. Enable keyboard input. Default is true.
\n
loop
\n
Optional. Boolean. Loop the video. Default is false.
\n
maxheight
\n
Optional. String. CSS value. Video maximum height.
\n
maxwidth
\n
Optional. String. CSS value. Video maximum width.
\n
muted
\n
Optional. Boolean. Mute the video. Default is false.
\n
pip
\n
Optional. Boolean. Show the picture-in-picture control. Default is false.
\n
playsinline
\n
Optional. Boolean. Play inline instead of full screen on mobile devices. Default is true.
\n
portrait
\n
Optional. Boolean. Show the author's profile image. Default is configured per video.
\n
quality
\n
Optional. String. The resolution. Must be auto, 240p, 360p, 540p, 720p, 1080p, 2k, or 4k. Default is auto.
\n
speed
\n
Optional. Boolean. Show the speed controls. Default is false.
\n
texttrack
\n
Optional. String. Language code and optionally a locale code (e.g. en, en-US). Use the corresponding subtitles. Default is false.
\n
time
\n
Optional. String. Duration (e.g. 0m, 1m2s). Start time. Default is 0m.
\n
title
\n
Optional. Boolean. Show the title. Default is configured per video.
\n
transparent
\n
Optional. Boolean. Use a transparent background instead of a black one. Default is true.
\n
video
\n
Optional. Position 0. String. Video ID.
\n
width
\n
Optional. String. CSS value. Video width.
\n
\n\nSee [Vimeo documentation](https://vimeo.zendesk.com/hc/en-us/articles/360001494447-Player-parameters-overview) for more detail.\n\n### YouTube\n\nThe `paige/youtube` shortcode provides a YouTube video.\n\n```\n{{< paige/youtube\n autoplay=false\n controls=false\n description=\"\"\n end=0\n fullscreen=false\n height=\"\"\n list=\"\"\n loop=false\n maxheight=\"\"\n maxwidth=\"\"\n mute=false\n start=0\n video=\"\"\n width=\"\" >}}\n```\n\nInner content: None.\n\nParameters:\n\n
\n
autoplay
\n
Optional. Boolean. Automatically play the video.
\n
controls
\n
Optional. Boolean. Show video controls. Default is true.
\n
description
\n
Optional. String. Plain text. Screen reader content. Default is YouTube video.
\n
end
\n
Optional. Integer. Elapsed seconds. Stop the video here.
\n
fullscreen
\n
Optional. Boolean. Enable full screen. Default is true.
\n
height
\n
Optional. String. CSS value. Video height.
\n
list
\n
Optional. String. Playlist ID.
\n
loop
\n
Optional. Boolean. Loop the video.
\n
maxheight
\n
Optional. String. CSS value. Video maximum height.
\n
maxwidth
\n
Optional. String. CSS value. Video maximum width.
\n
mute
\n
Optional. Boolean. Mute the video.
\n
start
\n
Optional. Integer. Elapsed seconds. Start the video here.
\n
video
\n
Optional. Position 0. String. Video ID.
\n
width
\n
Optional. String. CSS value. Video width.
\n
\n\n## Customize\n\n### Include\n\n| If this file exists | It is included at |\n| --------------------------------------------------- | ------------------------------- |\n| `yoursite/layouts/partials/paige/body-first.html` | The beginning of the body tag |\n| `yoursite/layouts/partials/paige/body-last.html` | The end of the body tag |\n| `yoursite/layouts/partials/paige/footer-first.html` | The beginning of the footer tag |\n| `yoursite/layouts/partials/paige/footer-last.html` | The end of the footer tag |\n| `yoursite/layouts/partials/paige/head-first.html` | The beginning of the head tag |\n| `yoursite/layouts/partials/paige/head-last.html` | The end of the head tag |\n| `yoursite/layouts/partials/paige/header-first.html` | The beginning of the header tag |\n| `yoursite/layouts/partials/paige/header-last.html` | The end of the header tag |\n| `yoursite/layouts/partials/paige/main-first.html` | The beginning of the main tag |\n| `yoursite/layouts/partials/paige/main-last.html` | The end of the main tag |\n| `yoursite/layouts/partials/paige/style-first.css` | The beginning of the style tag |\n| `yoursite/layouts/partials/paige/style-last.css` | The end of the style tag |\n\n### Override\n\nMost code is in partials that are included by the layouts.\nElements can be added, changed, or removed easily by overriding the corresponding layout or partial.\n\nFor example, the layouts `list.html`, `single.html`, `taxonomy.html`, and `term.html`\ninclude the partial `paige/article.html`.\n`paige/article.html` includes the partials `paige/metadata.html`, `paige/toc.html`, and `paige/content.html`.\nTo change the page title for those layouts, change `paige/metadata.html`.\nTo change the page title for `single.html`,\nreplace the use of `paige/article.html` in `single.html` with the use of\n`paige/metadata.html`, `paige/toc.html`, and `paige/content.html`,\nthen replace that use of `paige/metadata.html` with your own design.\n\n### Extend\n\nUse CSS selectors to extend the default styling.\n\nPage identifiers:\n\n
\n
#paige-article
\n
The article.
\n
#paige-authors
\n
The authors.
\n
#paige-breadcrumbs
\n
The breadcrumbs.
\n
#paige-collections
\n
The collections.
\n
#paige-collections-header
\n
The collections header.
\n
#paige-comments
\n
The comments.
\n
#paige-content
\n
The content.
\n
#paige-copyright
\n
The copyright.
\n
#paige-credit
\n
The credit.
\n
#paige-date
\n
The date.
\n
#paige-description
\n
The description.
\n
#paige-file-link
\n
The file link. See the paige.link configuration.
\n
#paige-footer
\n
The footer.
\n
#paige-header
\n
The header.
\n
#paige-keywords
\n
The keywords.
\n
#paige-main
\n
The main.
\n
#paige-menu
\n
The menu.
\n
#paige-metadata
\n
The metadata.
\n
#paige-pages
\n
The pages.
\n
#paige-pages-header
\n
The pages header.
\n
#paige-pagination
\n
The sub-page pagination links.
\n
#paige-reading-time
\n
The reading time.
\n
#paige-root
\n
The outermost element in the body.
\n
#paige-sections
\n
The sections.
\n
#paige-sections-header
\n
The sections header.
\n
#paige-series
\n
The series.
\n
#paige-title
\n
The title.
\n
#paige-toc
\n
The table of contents.
\n
\n\nPage and sub-page classes:\n\n
\n
.paige-authors
\n
The authors.
\n
.paige-date
\n
The date.
\n
.paige-date-header
\n
The date headers.
\n
.paige-description
\n
The description.
\n
.paige-keyword
\n
A keyword.
\n
.paige-keywords
\n
The keywords.
\n
.paige-metadata
\n
The metadata.
\n
.paige-page
\n
A page.
\n
.paige-reading-time
\n
The reading time.
\n
.paige-series
\n
The series.
\n
.paige-summary
\n
The summary.
\n
.paige-title
\n
The title.
\n
\n\nKind classes:\n\n
\n
.paige-home
\n
The container element of pages with kind \"home\".
\n
.paige-list
\n
The container element of pages with kind \"section\".
\n
.paige-single
\n
The container element of pages with kind \"page\".
\n
.paige-taxonomy
\n
The container element of pages with kind \"taxonomy\".
\n
.paige-term
\n
The container element of pages with kind \"term\".
\n
\n\nStatus classes:\n\n
\n
.paige-draft
\n
The container element of draft pages.
\n
.paige-expired
\n
The container element of expired pages.
\n
.paige-future
\n
The container element of future pages.
\n
.paige-modified
\n
The container element of modified pages.
\n
.paige-published
\n
The container element of published pages.
\n
.paige-unpublished
\n
The container element of unpublished pages.
\n
\n\nShortcode classes:\n\n
\n
.paige-code
\n
The container element of paige/code shortcodes.
\n
.paige-figure
\n
The container element of paige/figure shortcodes.
\n
.paige-gallery
\n
The container element of paige/gallery shortcodes.
\n
.paige-icon
\n
The container element of paige/icon shortcodes.
\n
.paige-image
\n
The container element of paige/image shortcodes.
\n
.paige-quote
\n
The container element of paige/quote shortcodes.
\n
.paige-vimeo
\n
The container element of paige/vimeo shortcodes.
\n
.paige-youtube
\n
The container element of paige/youtube shortcodes.
\n
\n\nIf you hide `#paige-credit`, please credit this project in a post to help others find it.\n\n### Minimal look\n\nBy default, everything is shown.\nIf you want a more minimal look,\ntry the following in `yoursite/layouts/partials/paige/style-first.css`:\n\n```css\n#paige-authors,\n#paige-collections,\n#paige-credit,\n#paige-keywords,\n#paige-reading-time,\n#paige-series,\n#paige-toc,\n.paige-authors,\n.paige-date,\n.paige-keywords,\n.paige-reading-time,\n.paige-series,\n.paige-summary {\n display: none;\n}\n```\n\n### Narrow look\n\nBy default, the content expands to fill the whole window.\nThis can result in very long lines, depending on the window width.\nTo use [the recommended line length of 66 characters](https://en.wikipedia.org/wiki/Line_length#Printed_text),\ntry the following in `yoursite/hugo.toml`:\n\n```toml\n[params.paige]\nstyle = \"#paige-content { width: 66ch; }\"\n```\n\n### Menu navigation only\n\nIf you configure a menu, and intend for it to be sufficient for all navigation,\nyou should hide the breadcrumbs on every page,\nand hide the collection, section, and page lists on the home page if you use the default home page layout.\n\nTo hide the breadcrumbs on every page,\ntry the following in `yoursite/layouts/partials/paige/style-first.css`:\n\n```css\n#paige-breadcrumbs {\n display: none;\n}\n```\n\nTo hide the collection, section, and page lists on the home page,\ntry the following in `yoursite/content/_index.md`:\n\n```toml\n[paige]\nstyle = \"\"\"\n#paige-collections,\n#paige-sections,\n#paige-pages {\n display: none;\n}\n\"\"\"\n```\n\n## Implementation\n\nThe HTML author is the page authors.\n\nThe HTML description is the page description.\n\nThe HTML keywords is a union of the page keywords, tags, and categories.\n\nThe HTML title is the page title, followed by a middle dot, followed by the site title.\nIf one is missing, the other is used without the middle dot.\nFor the home page, the title is the page title, or the site title otherwise.\n\nThe HTML body has a header, a body, and a footer.\nThe header has the menu.\nThe body has the page article.\nThe footer has the copyright notice and the theme link.\nThe article has the page metadata, table of contents, and content.\nThe metadata has the page title, description, terms, authors, date, and reading time.\n\nThe copyright notice, title, and description can be Markdown. Markup is stripped for HTML and RSS titles.\n\nThe page title is displayed in an `h1` tag, so page content headers must start with `h2`.\n\nThe page date is the publish date.\n\nThe following NPM packages are used:\n\n- [bootstrap](https://www.npmjs.com/package/bootstrap/v/5.3.2)\n- [bootstrap-icons](https://www.npmjs.com/package/bootstrap-icons/v/1.11.0)\n- [katex](https://www.npmjs.com/package/katex/v/0.16.8)\n\nHugo names, HTML names, CSS names, and JavaScript names that begin with \"paige\" capitalized in any way,\noptionally prefixed with one or more underscores, are reserved.\n\n## Credits\n\n- Photos by [Lanty](https://unsplash.com/@photos_by_lanty)\n- Photo by [Sergey Pesterev](https://unsplash.com/photos/JV78PVf3gGI)\n\n## Project\n\nCreated by [Will Faught](https://willfaught.com).\nReleased under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).\nHosted at https://github.com/willfaught/paige.", + "readme_title": "Paige", + "releases_count": 0, + "repo": "paige", + "repo_name": "paige", + "stars": 0, + "topics": [], + "updated_at": "2023-10-22T17:52:56Z", + "url": "https://github.com/hassan11196/paige", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/paige" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/posthog.com" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 331, + "contributors_count": 13, + "contributors_top": [ + { + "contributions": 176, + "html_url": "https://github.com/jamesefhawkins", + "login": "jamesefhawkins" + }, + { + "contributions": 96, + "html_url": "https://github.com/timgl", + "login": "timgl" + }, + { + "contributions": 19, + "html_url": "https://github.com/EDsCODE", + "login": "EDsCODE" + }, + { + "contributions": 16, + "html_url": "https://github.com/mariusandra", + "login": "mariusandra" + }, + { + "contributions": 16, + "html_url": "https://github.com/fuziontech", + "login": "fuziontech" + }, + { + "contributions": 1, + "html_url": null, + "login": "Alex Earnshaw" + }, + { + "contributions": 1, + "html_url": null, + "login": "Anna" + }, + { + "contributions": 1, + "html_url": "https://github.com/DannyBen", + "login": "DannyBen" + } + ], + "created_at": "2020-07-01T15:00:34Z", + "default_branch": "master", + "description": "Official docs, website and handbook.", + "dirs_total_count": 57, + "files_root_entries": [ + { + "path": ".gitignore", + "size": 111, + "type": "file" + }, + { + "path": ".prettierrc", + "size": 69, + "type": "file" + }, + { + "path": "CONTRIBUTING.MD", + "size": 847, + "type": "file" + }, + { + "path": "LICENSE", + "size": 1057, + "type": "file" + }, + { + "path": "README.md", + "size": 753, + "type": "file" + }, + { + "path": "contents", + "size": 0, + "type": "dir" + }, + { + "path": "gatsby-browser.js", + "size": 337, + "type": "file" + }, + { + "path": "gatsby-config.js", + "size": 6177, + "type": "file" + }, + { + "path": "gatsby-node.js", + "size": 268, + "type": "file" + }, + { + "path": "gatsby-ssr.js", + "size": 279, + "type": "file" + }, + { + "path": "gatsby", + "size": 0, + "type": "dir" + }, + { + "path": "package-lock.json", + "size": 847203, + "type": "file" + }, + { + "path": "package.json", + "size": 1785, + "type": "file" + }, + { + "path": "src", + "size": 0, + "type": "dir" + }, + { + "path": "static", + "size": 0, + "type": "dir" + }, + { + "path": "wrap-with-provider.js", + "size": 273, + "type": "file" + }, + { + "path": "yarn.lock", + "size": 682660, + "type": "file" + } + ], + "files_total_count": 732, + "first_commit_date_default_branch": "2020-04-16T21:38:35Z", + "forks": 0, + "has_ci": false, + "has_contributing": true, + "has_docker": false, + "has_docs": false, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": "https://posthog.com/", + "input_url": "https://github.com/hassan11196/posthog.com", + "is_archived": false, + "is_fork": true, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "JavaScript", + "size": 91727 + }, + { + "language": "CSS", + "size": 14086 + } + ], + "last_commit_date_default_branch": "2020-06-30T15:27:12Z", + "last_commit_oid_default_branch": "47fd522f2c4ad8b23e549114321b5c06966bd42c", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "MIT License", + "license_spdx": "MIT", + "name_with_owner": "hassan11196/posthog.com", + "owner": "hassan11196", + "parent_repo": "PostHog/posthog.com", + "parent_url": "https://github.com/PostHog/posthog.com", + "primary_language": "JavaScript", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2020-07-01T15:00:56Z", + "readme_length": 750, + "readme_text": "# PostHog.com - docs, website and handbook\n\nThis is the new company website / docs and handbook.\n\nPlease feel free to suggest any changes - see our contributing guide.\n\n## Quick Start\n\n1. **Start developing.**\n\n Clone the repo then navigate into your new site’s directory and start it up.\n\n ```sh\n cd posthog.com/\n yarn install\n gatsby develop\n ```\n\n1. **Open the source code and start editing!**\n\n Your site is now running at `http://localhost:8000`!\n \n *Note: You'll also see a second link: `http://localhost:8000/___graphql`. This is a tool you can use to experiment with querying your data. Learn more about using this tool in the [Gatsby tutorial](https://www.gatsbyjs.org/tutorial/part-five/#introducing-graphiql).*", + "readme_title": "PostHog.com - docs, website and handbook", + "releases_count": 0, + "repo": "posthog.com", + "repo_name": "posthog.com", + "stars": 0, + "topics": [], + "updated_at": "2020-07-01T15:00:36Z", + "url": "https://github.com/hassan11196/posthog.com", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/posthog.com" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/proyecto-unam" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 472, + "contributors_count": 2, + "contributors_top": [ + { + "contributions": 469, + "html_url": "https://github.com/agtz92", + "login": "agtz92" + }, + { + "contributions": 3, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + } + ], + "created_at": "2021-01-06T03:01:38Z", + "default_branch": "main", + "description": null, + "dirs_total_count": 23, + "files_root_entries": [ + { + "path": ".gitattributes", + "size": 66, + "type": "file" + }, + { + "path": ".gitignore", + "size": 974, + "type": "file" + }, + { + "path": ".prettierignore", + "size": 45, + "type": "file" + }, + { + "path": ".prettierrc", + "size": 46, + "type": "file" + }, + { + "path": ".vscode", + "size": 0, + "type": "dir" + }, + { + "path": "LICENSE", + "size": 675, + "type": "file" + }, + { + "path": "README.md", + "size": 5611, + "type": "file" + }, + { + "path": "blog", + "size": 0, + "type": "dir" + }, + { + "path": "cms", + "size": 0, + "type": "dir" + }, + { + "path": "gatsby-config.js", + "size": 4664, + "type": "file" + }, + { + "path": "gatsby-node.js", + "size": 2895, + "type": "file" + }, + { + "path": "node_modules", + "size": 0, + "type": "dir" + }, + { + "path": "package-lock.json", + "size": 940230, + "type": "file" + }, + { + "path": "package.json", + "size": 2500, + "type": "file" + }, + { + "path": "scripts", + "size": 0, + "type": "dir" + }, + { + "path": "src", + "size": 0, + "type": "dir" + }, + { + "path": "static", + "size": 0, + "type": "dir" + } + ], + "files_total_count": 483, + "first_commit_date_default_branch": "2020-11-30T19:39:04Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "input_url": "https://github.com/hassan11196/proyecto-unam", + "is_archived": false, + "is_fork": true, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "CSS", + "size": 61638 + }, + { + "language": "JavaScript", + "size": 60731 + } + ], + "last_commit_date_default_branch": "2021-01-06T03:23:30Z", + "last_commit_oid_default_branch": "456d5645385e70f9ee6493838640f151c60f0a04", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "BSD Zero Clause License", + "license_spdx": "0BSD", + "name_with_owner": "hassan11196/proyecto-unam", + "owner": "hassan11196", + "parent_repo": "agtz92/proyecto-unam", + "parent_url": "https://github.com/agtz92/proyecto-unam", + "primary_language": "CSS", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2021-01-06T04:20:21Z", + "readme_length": 5514, + "readme_text": "\n

\n \n \"Gatsby\"\n \n

\n

\n Gatsby's hello-world starter\n

\n\nKick off your project with this hello-world boilerplate. This starter ships with the main Gatsby configuration files you might need to get up and running blazing fast with the blazing fast app generator for React.\n\n_Have another more specific idea? You may want to check out our vibrant collection of [official and community-created starters](https://www.gatsbyjs.com/docs/gatsby-starters/)._\n\n## 🚀 Quick start\n\n1. **Create a Gatsby site.**\n\n Use the Gatsby CLI to create a new site, specifying the hello-world starter.\n\n ```shell\n # create a new Gatsby site using the hello-world starter\n gatsby new my-hello-world-starter https://github.com/gatsbyjs/gatsby-starter-hello-world\n ```\n\n1. **Start developing.**\n\n Navigate into your new site’s directory and start it up.\n\n ```shell\n cd my-hello-world-starter/\n gatsby develop\n ```\n\n1. **Open the source code and start editing!**\n\n Your site is now running at `http://localhost:8000`!\n\n _Note: You'll also see a second link: _`http://localhost:8000/___graphql`_. This is a tool you can use to experiment with querying your data. Learn more about using this tool in the [Gatsby tutorial](https://www.gatsbyjs.com/tutorial/part-five/#introducing-graphiql)._\n\n Open the `my-hello-world-starter` directory in your code editor of choice and edit `src/pages/index.js`. Save your changes and the browser will update in real time!\n\n## 🧐 What's inside?\n\nA quick look at the top-level files and directories you'll see in a Gatsby project.\n\n .\n ├── node_modules\n ├── src\n ├── .gitignore\n ├── .prettierrc\n ├── gatsby-browser.js\n ├── gatsby-config.js\n ├── gatsby-node.js\n ├── gatsby-ssr.js\n ├── LICENSE\n ├── package-lock.json\n ├── package.json\n └── README.md\n\n1. **`/node_modules`**: This directory contains all of the modules of code that your project depends on (npm packages) are automatically installed.\n\n2. **`/src`**: This directory will contain all of the code related to what you will see on the front-end of your site (what you see in the browser) such as your site header or a page template. `src` is a convention for “source code”.\n\n3. **`.gitignore`**: This file tells git which files it should not track / not maintain a version history for.\n\n4. **`.prettierrc`**: This is a configuration file for [Prettier](https://prettier.io/). Prettier is a tool to help keep the formatting of your code consistent.\n\n5. **`gatsby-browser.js`**: This file is where Gatsby expects to find any usage of the [Gatsby browser APIs](https://www.gatsbyjs.com/docs/browser-apis/) (if any). These allow customization/extension of default Gatsby settings affecting the browser.\n\n6. **`gatsby-config.js`**: This is the main configuration file for a Gatsby site. This is where you can specify information about your site (metadata) like the site title and description, which Gatsby plugins you’d like to include, etc. (Check out the [config docs](https://www.gatsbyjs.com/docs/gatsby-config/) for more detail).\n\n7. **`gatsby-node.js`**: This file is where Gatsby expects to find any usage of the [Gatsby Node APIs](https://www.gatsbyjs.com/docs/node-apis/) (if any). These allow customization/extension of default Gatsby settings affecting pieces of the site build process.\n\n8. **`gatsby-ssr.js`**: This file is where Gatsby expects to find any usage of the [Gatsby server-side rendering APIs](https://www.gatsbyjs.com/docs/ssr-apis/) (if any). These allow customization of default Gatsby settings affecting server-side rendering.\n\n9. **`LICENSE`**: This Gatsby starter is licensed under the 0BSD license. This means that you can see this file as a placeholder and replace it with your own license.\n\n10. **`package-lock.json`** (See `package.json` below, first). This is an automatically generated file based on the exact versions of your npm dependencies that were installed for your project. **(You won’t change this file directly).**\n\n11. **`package.json`**: A manifest file for Node.js projects, which includes things like metadata (the project’s name, author, etc). This manifest is how npm knows which packages to install for your project.\n\n12. **`README.md`**: A text file containing useful reference information about your project.\n\n## 🎓 Learning Gatsby\n\nLooking for more guidance? Full documentation for Gatsby lives [on the website](https://www.gatsbyjs.com/). Here are some places to start:\n\n- **For most developers, we recommend starting with our [in-depth tutorial for creating a site with Gatsby](https://www.gatsbyjs.com/tutorial/).** It starts with zero assumptions about your level of ability and walks through every step of the process.\n\n- **To dive straight into code samples, head [to our documentation](https://www.gatsbyjs.com/docs/).** In particular, check out the _Guides_, _API Reference_, and _Advanced Tutorials_ sections in the sidebar.\n\n## 💫 Deploy\n\n[![Deploy to Netlify](https://www.netlify.com/img/deploy/button.svg)](https://app.netlify.com/start/deploy?repository=https://github.com/gatsbyjs/gatsby-starter-hello-world)\n\n[![Deploy with Vercel](https://vercel.com/button)](https://vercel.com/import/project?template=https://github.com/gatsbyjs/gatsby-starter-hello-world)\n\n", + "readme_title": "create a new Gatsby site using the hello-world starter", + "releases_count": 0, + "repo": "proyecto-unam", + "repo_name": 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:\n\nhttps://realpython.com/python-sockets/\nhttps://docs.python.org/3/howto/sockets.html\n\nneechabe", + "readme_title": "Basic Python Socket Programming interacting With DialogFlow API", + "releases_count": 0, + "repo": "python-dialogflow-basic", + "repo_name": "python-dialogflow-basic", + "stars": 1, + "topics": [ + "python", + "socket-programming", + "dialogflow" + ], + "updated_at": "2023-11-12T09:09:09Z", + "url": "https://github.com/hassan11196/python-dialogflow-basic", + "watchers": 1 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/python-dialogflow-basic" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/python-practice" + ], 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"last_commit_oid_default_branch": "6d6e04d6cc9a3d7cf5d9a2eda5baafd5c3ee75c0", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "Other", + "license_spdx": "NOASSERTION", + "name_with_owner": "hassan11196/pytorch", + "owner": "hassan11196", + "parent_repo": "pytorch/pytorch", + "parent_url": "https://github.com/pytorch/pytorch", + "primary_language": "C++", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2022-09-03T21:14:51Z", + "readme_length": 22335, + "readme_text": "![PyTorch Logo](https://github.com/pytorch/pytorch/blob/master/docs/source/_static/img/pytorch-logo-dark.png)\n\n--------------------------------------------------------------------------------\n\nPyTorch is a Python package that provides two high-level features:\n- Tensor computation (like NumPy) with strong GPU acceleration\n- Deep neural networks built on a tape-based autograd system\n\nYou can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.\n\nOur trunk health (Continuous Integration signals) can be found at [hud.pytorch.org](https://hud.pytorch.org/ci/pytorch/pytorch/master).\n\n\n\n- [More About PyTorch](#more-about-pytorch)\n - [A GPU-Ready Tensor Library](#a-gpu-ready-tensor-library)\n - [Dynamic Neural Networks: Tape-Based Autograd](#dynamic-neural-networks-tape-based-autograd)\n - [Python First](#python-first)\n - [Imperative Experiences](#imperative-experiences)\n - [Fast and Lean](#fast-and-lean)\n - [Extensions Without Pain](#extensions-without-pain)\n- [Installation](#installation)\n - [Binaries](#binaries)\n - [NVIDIA Jetson Platforms](#nvidia-jetson-platforms)\n - [From Source](#from-source)\n - [Prerequisites](#prerequisites)\n - [Install Dependencies](#install-dependencies)\n - [Get the PyTorch Source](#get-the-pytorch-source)\n - [Install PyTorch](#install-pytorch)\n - [Adjust Build Options (Optional)](#adjust-build-options-optional)\n - [Docker Image](#docker-image)\n - [Using pre-built images](#using-pre-built-images)\n - [Building the image yourself](#building-the-image-yourself)\n - [Building the Documentation](#building-the-documentation)\n - [Previous Versions](#previous-versions)\n- [Getting Started](#getting-started)\n- [Resources](#resources)\n- [Communication](#communication)\n- [Releases and Contributing](#releases-and-contributing)\n- [The Team](#the-team)\n- [License](#license)\n\n\n\n## More About PyTorch\n\nAt a granular level, PyTorch is a library that consists of the following components:\n\n| Component | Description |\n| ---- | --- |\n| [**torch**](https://pytorch.org/docs/stable/torch.html) | A Tensor library like NumPy, with strong GPU support |\n| [**torch.autograd**](https://pytorch.org/docs/stable/autograd.html) | A tape-based automatic differentiation library that supports all differentiable Tensor operations in torch |\n| [**torch.jit**](https://pytorch.org/docs/stable/jit.html) | A compilation stack (TorchScript) to create serializable and optimizable models from PyTorch code |\n| [**torch.nn**](https://pytorch.org/docs/stable/nn.html) | A neural networks library deeply integrated with autograd designed for maximum flexibility |\n| [**torch.multiprocessing**](https://pytorch.org/docs/stable/multiprocessing.html) | Python multiprocessing, but with magical memory sharing of torch Tensors across processes. Useful for data loading and Hogwild training |\n| [**torch.utils**](https://pytorch.org/docs/stable/data.html) | DataLoader and other utility functions for convenience |\n\nUsually, PyTorch is used either as:\n\n- A replacement for NumPy to use the power of GPUs.\n- A deep learning research platform that provides maximum flexibility and speed.\n\nElaborating Further:\n\n### A GPU-Ready Tensor Library\n\nIf you use NumPy, then you have used Tensors (a.k.a. ndarray).\n\n![Tensor illustration](./docs/source/_static/img/tensor_illustration.png)\n\nPyTorch provides Tensors that can live either on the CPU or the GPU and accelerates the\ncomputation by a huge amount.\n\nWe provide a wide variety of tensor routines to accelerate and fit your scientific computation needs\nsuch as slicing, indexing, mathematical operations, linear algebra, reductions.\nAnd they are fast!\n\n### Dynamic Neural Networks: Tape-Based Autograd\n\nPyTorch has a unique way of building neural networks: using and replaying a tape recorder.\n\nMost frameworks such as TensorFlow, Theano, Caffe, and CNTK have a static view of the world.\nOne has to build a neural network and reuse the same structure again and again.\nChanging the way the network behaves means that one has to start from scratch.\n\nWith PyTorch, we use a technique called reverse-mode auto-differentiation, which allows you to\nchange the way your network behaves arbitrarily with zero lag or overhead. Our inspiration comes\nfrom several research papers on this topic, as well as current and past work such as\n[torch-autograd](https://github.com/twitter/torch-autograd),\n[autograd](https://github.com/HIPS/autograd),\n[Chainer](https://chainer.org), etc.\n\nWhile this technique is not unique to PyTorch, it's one of the fastest implementations of it to date.\nYou get the best of speed and flexibility for your crazy research.\n\n![Dynamic graph](https://github.com/pytorch/pytorch/blob/master/docs/source/_static/img/dynamic_graph.gif)\n\n### Python First\n\nPyTorch is not a Python binding into a monolithic C++ framework.\nIt is built to be deeply integrated into Python.\nYou can use it naturally like you would use [NumPy](https://www.numpy.org/) / [SciPy](https://www.scipy.org/) / [scikit-learn](https://scikit-learn.org) etc.\nYou can write your new neural network layers in Python itself, using your favorite libraries\nand use packages such as [Cython](https://cython.org/) and [Numba](http://numba.pydata.org/).\nOur goal is to not reinvent the wheel where appropriate.\n\n### Imperative Experiences\n\nPyTorch is designed to be intuitive, linear in thought, and easy to use.\nWhen you execute a line of code, it gets executed. There isn't an asynchronous view of the world.\nWhen you drop into a debugger or receive error messages and stack traces, understanding them is straightforward.\nThe stack trace points to exactly where your code was defined.\nWe hope you never spend hours debugging your code because of bad stack traces or asynchronous and opaque execution engines.\n\n### Fast and Lean\n\nPyTorch has minimal framework overhead. We integrate acceleration libraries\nsuch as [Intel MKL](https://software.intel.com/mkl) and NVIDIA ([cuDNN](https://developer.nvidia.com/cudnn), [NCCL](https://developer.nvidia.com/nccl)) to maximize speed.\nAt the core, its CPU and GPU Tensor and neural network backends\nare mature and have been tested for years.\n\nHence, PyTorch is quite fast – whether you run small or large neural networks.\n\nThe memory usage in PyTorch is extremely efficient compared to Torch or some of the alternatives.\nWe've written custom memory allocators for the GPU to make sure that\nyour deep learning models are maximally memory efficient.\nThis enables you to train bigger deep learning models than before.\n\n### Extensions Without Pain\n\nWriting new neural network modules, or interfacing with PyTorch's Tensor API was designed to be straightforward\nand with minimal abstractions.\n\nYou can write new neural network layers in Python using the torch API\n[or your favorite NumPy-based libraries such as SciPy](https://pytorch.org/tutorials/advanced/numpy_extensions_tutorial.html).\n\nIf you want to write your layers in C/C++, we provide a convenient extension API that is efficient and with minimal boilerplate.\nNo wrapper code needs to be written. You can see [a tutorial here](https://pytorch.org/tutorials/advanced/cpp_extension.html) and [an example here](https://github.com/pytorch/extension-cpp).\n\n\n## Installation\n\n### Binaries\nCommands to install binaries via Conda or pip wheels are on our website: [https://pytorch.org/get-started/locally/](https://pytorch.org/get-started/locally/)\n\n\n#### NVIDIA Jetson Platforms\n\nPython wheels for NVIDIA's Jetson Nano, Jetson TX1/TX2, Jetson Xavier NX/AGX, and Jetson AGX Orin are provided [here](https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-10-now-available/72048) and the L4T container is published [here](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-pytorch)\n\nThey require JetPack 4.2 and above, and [@dusty-nv](https://github.com/dusty-nv) and [@ptrblck](https://github.com/ptrblck) are maintaining them.\n\n\n### From Source\n\n#### Prerequisites\nIf you are installing from source, you will need:\n- Python 3.7 or later (for Linux, Python 3.7.6+ or 3.8.1+ is needed)\n- A C++14 compatible compiler, such as clang\n\nWe highly recommend installing an [Anaconda](https://www.anaconda.com/distribution/#download-section) environment. You will get a high-quality BLAS library (MKL) and you get controlled dependency versions regardless of your Linux distro.\n\nIf you want to compile with CUDA support, install the following (note that CUDA is not supported on macOS)\n- [NVIDIA CUDA](https://developer.nvidia.com/cuda-downloads) 10.2 or above\n- [NVIDIA cuDNN](https://developer.nvidia.com/cudnn) v7 or above\n- [Compiler](https://gist.github.com/ax3l/9489132) compatible with CUDA\n\nNote: You could refer to the [cuDNN Support Matrix](https://docs.nvidia.com/deeplearning/cudnn/pdf/cuDNN-Support-Matrix.pdf) for cuDNN versions with the various supported CUDA, CUDA driver and NVIDIA hardware\n\nIf you want to disable CUDA support, export the environment variable `USE_CUDA=0`.\nOther potentially useful environment variables may be found in `setup.py`.\n\nIf you are building for NVIDIA's Jetson platforms (Jetson Nano, TX1, TX2, AGX Xavier), Instructions to install PyTorch for Jetson Nano are [available here](https://devtalk.nvidia.com/default/topic/1049071/jetson-nano/pytorch-for-jetson-nano/)\n\nIf you want to compile with ROCm support, install\n- [AMD ROCm](https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation-Guide.html) 4.0 and above installation\n- ROCm is currently supported only for Linux systems.\n\nIf you want to disable ROCm support, export the environment variable `USE_ROCM=0`.\nOther potentially useful environment variables may be found in `setup.py`.\n\n#### Install Dependencies\n\n**Common**\n\n```bash\nconda install astunparse numpy ninja pyyaml setuptools cmake cffi typing_extensions future six requests dataclasses\n```\n\n**On Linux**\n\n```bash\nconda install mkl mkl-include\n# CUDA only: Add LAPACK support for the GPU if needed\nconda install -c pytorch magma-cuda110 # or the magma-cuda* that matches your CUDA version from https://anaconda.org/pytorch/repo\n```\n\n**On MacOS**\n\n```bash\n# Add this package on intel x86 processor machines only\nconda install mkl mkl-include\n# Add these packages if torch.distributed is needed\nconda install pkg-config libuv\n```\n\n**On Windows**\n\n```bash\nconda install mkl mkl-include\n# Add these packages if torch.distributed is needed.\n# Distributed package support on Windows is a prototype feature and is subject to changes.\nconda install -c conda-forge libuv=1.39\n```\n\n#### Get the PyTorch Source\n```bash\ngit clone --recursive https://github.com/pytorch/pytorch\ncd pytorch\n# if you are updating an existing checkout\ngit submodule sync\ngit submodule update --init --recursive --jobs 0\n```\n\n#### Install PyTorch\n**On Linux**\n\nIf you're compiling for AMD ROCm then first run this command:\n```bash\n# Only run this if you're compiling for ROCm\npython tools/amd_build/build_amd.py\n```\n\nInstall PyTorch\n```bash\nexport CMAKE_PREFIX_PATH=${CONDA_PREFIX:-\"$(dirname $(which conda))/../\"}\npython setup.py install\n```\n\nNote that if you are using [Anaconda](https://www.anaconda.com/distribution/#download-section), you may experience an error caused by the linker:\n\n```plaintext\nbuild/temp.linux-x86_64-3.7/torch/csrc/stub.o: file not recognized: file format not recognized\ncollect2: error: ld returned 1 exit status\nerror: command 'g++' failed with exit status 1\n```\n\nThis is caused by `ld` from the Conda environment shadowing the system `ld`. You should use a newer version of Python that fixes this issue. The recommended Python version is 3.7.6+ and 3.8.1+.\n\n**On macOS**\n\n```bash\nexport CMAKE_PREFIX_PATH=${CONDA_PREFIX:-\"$(dirname $(which conda))/../\"}\nMACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install\n```\n\n**On Windows**\n\nChoose Correct Visual Studio Version.\n\nSometimes there are regressions in new versions of Visual Studio, so\nit's best to use the same Visual Studio Version [16.8.5](https://github.com/pytorch/pytorch/blob/master/.circleci/scripts/vs_install.ps1) as Pytorch CI's.\n\nPyTorch CI uses Visual C++ BuildTools, which come with Visual Studio Enterprise,\nProfessional, or Community Editions. You can also install the build tools from\nhttps://visualstudio.microsoft.com/visual-cpp-build-tools/. The build tools *do not*\ncome with Visual Studio Code by default.\n\nIf you want to build legacy python code, please refer to [Building on legacy code and CUDA](https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md#building-on-legacy-code-and-cuda)\n\n**CPU-only builds**\n\nIn this mode PyTorch computations will run on your CPU, not your GPU\n\n```cmd\nconda activate\npython setup.py install\n```\n\nNote on OpenMP: The desired OpenMP implementation is Intel OpenMP (iomp). In order to link against iomp, you'll need to manually download the library and set up the building environment by tweaking `CMAKE_INCLUDE_PATH` and `LIB`. The instruction [here](https://github.com/pytorch/pytorch/blob/master/docs/source/notes/windows.rst#building-from-source) is an example for setting up both MKL and Intel OpenMP. Without these configurations for CMake, Microsoft Visual C OpenMP runtime (vcomp) will be used.\n\n**CUDA based build**\n\nIn this mode PyTorch computations will leverage your GPU via CUDA for faster number crunching\n\n[NVTX](https://docs.nvidia.com/gameworks/content/gameworkslibrary/nvtx/nvidia_tools_extension_library_nvtx.htm) is needed to build Pytorch with CUDA.\nNVTX is a part of CUDA distributive, where it is called \"Nsight Compute\". To install it onto an already installed CUDA run CUDA installation once again and check the corresponding checkbox.\nMake sure that CUDA with Nsight Compute is installed after Visual Studio.\n\nCurrently, VS 2017 / 2019, and Ninja are supported as the generator of CMake. If `ninja.exe` is detected in `PATH`, then Ninja will be used as the default generator, otherwise, it will use VS 2017 / 2019.\n
If Ninja is selected as the generator, the latest MSVC will get selected as the underlying toolchain.\n\nAdditional libraries such as\n[Magma](https://developer.nvidia.com/magma), [oneDNN, a.k.a MKLDNN or DNNL](https://github.com/oneapi-src/oneDNN), and [Sccache](https://github.com/mozilla/sccache) are often needed. Please refer to the [installation-helper](https://github.com/pytorch/pytorch/tree/master/.jenkins/pytorch/win-test-helpers/installation-helpers) to install them.\n\nYou can refer to the [build_pytorch.bat](https://github.com/pytorch/pytorch/blob/master/.jenkins/pytorch/win-test-helpers/build_pytorch.bat) script for some other environment variables configurations\n\n\n```cmd\ncmd\n\n:: Set the environment variables after you have downloaded and unzipped the mkl package,\n:: else CMake would throw an error as `Could NOT find OpenMP`.\nset CMAKE_INCLUDE_PATH={Your directory}\\mkl\\include\nset LIB={Your directory}\\mkl\\lib;%LIB%\n\n:: Read the content in the previous section carefully before you proceed.\n:: [Optional] If you want to override the underlying toolset used by Ninja and Visual Studio with CUDA, please run the following script block.\n:: \"Visual Studio 2019 Developer Command Prompt\" will be run automatically.\n:: Make sure you have CMake >= 3.12 before you do this when you use the Visual Studio generator.\nset CMAKE_GENERATOR_TOOLSET_VERSION=14.27\nset DISTUTILS_USE_SDK=1\nfor /f \"usebackq tokens=*\" %i in (`\"%ProgramFiles(x86)%\\Microsoft Visual Studio\\Installer\\vswhere.exe\" -version [15^,17^) -products * -latest -property installationPath`) do call \"%i\\VC\\Auxiliary\\Build\\vcvarsall.bat\" x64 -vcvars_ver=%CMAKE_GENERATOR_TOOLSET_VERSION%\n\n:: [Optional] If you want to override the CUDA host compiler\nset CUDAHOSTCXX=C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\Community\\VC\\Tools\\MSVC\\14.27.29110\\bin\\HostX64\\x64\\cl.exe\n\npython setup.py install\n\n```\n\n##### Adjust Build Options (Optional)\n\nYou can adjust the configuration of cmake variables optionally (without building first), by doing\nthe following. For example, adjusting the pre-detected directories for CuDNN or BLAS can be done\nwith such a step.\n\nOn Linux\n```bash\nexport CMAKE_PREFIX_PATH=${CONDA_PREFIX:-\"$(dirname $(which conda))/../\"}\npython setup.py build --cmake-only\nccmake build # or cmake-gui build\n```\n\nOn macOS\n```bash\nexport CMAKE_PREFIX_PATH=${CONDA_PREFIX:-\"$(dirname $(which conda))/../\"}\nMACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py build --cmake-only\nccmake build # or cmake-gui build\n```\n\n### Docker Image\n\n#### Using pre-built images\n\nYou can also pull a pre-built docker image from Docker Hub and run with docker v19.03+\n\n```bash\ndocker run --gpus all --rm -ti --ipc=host pytorch/pytorch:latest\n```\n\nPlease note that PyTorch uses shared memory to share data between processes, so if torch multiprocessing is used (e.g.\nfor multithreaded data loaders) the default shared memory segment size that container runs with is not enough, and you\nshould increase shared memory size either with `--ipc=host` or `--shm-size` command line options to `nvidia-docker run`.\n\n#### Building the image yourself\n\n**NOTE:** Must be built with a docker version > 18.06\n\nThe `Dockerfile` is supplied to build images with CUDA 11.1 support and cuDNN v8.\nYou can pass `PYTHON_VERSION=x.y` make variable to specify which Python version is to be used by Miniconda, or leave it\nunset to use the default.\n```bash\nmake -f docker.Makefile\n# images are tagged as docker.io/${your_docker_username}/pytorch\n```\n\n### Building the Documentation\n\nTo build documentation in various formats, you will need [Sphinx](http://www.sphinx-doc.org) and the\nreadthedocs theme.\n\n```bash\ncd docs/\npip install -r requirements.txt\n```\nYou can then build the documentation by running `make ` from the\n`docs/` folder. Run `make` to get a list of all available output formats.\n\nIf you get a katex error run `npm install katex`. If it persists, try\n`npm install -g katex`\n\n> Note: if you installed `nodejs` with a different package manager (e.g.,\n`conda`) then `npm` will probably install a version of `katex` that is not\ncompatible with your version of `nodejs` and doc builds will fail.\nA combination of versions that is known to work is `node@6.13.1` and\n`katex@0.13.18`. To install the latter with `npm` you can run\n```npm install -g katex@0.13.18```\n\n### Previous Versions\n\nInstallation instructions and binaries for previous PyTorch versions may be found\non [our website](https://pytorch.org/previous-versions).\n\n\n## Getting Started\n\nThree-pointers to get you started:\n- [Tutorials: get you started with understanding and using PyTorch](https://pytorch.org/tutorials/)\n- [Examples: easy to understand PyTorch code across all domains](https://github.com/pytorch/examples)\n- [The API Reference](https://pytorch.org/docs/)\n- [Glossary](https://github.com/pytorch/pytorch/blob/master/GLOSSARY.md)\n\n## Resources\n\n* [PyTorch.org](https://pytorch.org/)\n* [PyTorch Tutorials](https://pytorch.org/tutorials/)\n* [PyTorch Examples](https://github.com/pytorch/examples)\n* [PyTorch Models](https://pytorch.org/hub/)\n* [Intro to Deep Learning with PyTorch from Udacity](https://www.udacity.com/course/deep-learning-pytorch--ud188)\n* [Intro to Machine Learning with PyTorch from Udacity](https://www.udacity.com/course/intro-to-machine-learning-nanodegree--nd229)\n* [Deep Neural Networks with PyTorch from Coursera](https://www.coursera.org/learn/deep-neural-networks-with-pytorch)\n* [PyTorch Twitter](https://twitter.com/PyTorch)\n* [PyTorch Blog](https://pytorch.org/blog/)\n* [PyTorch YouTube](https://www.youtube.com/channel/UCWXI5YeOsh03QvJ59PMaXFw)\n\n## Communication\n* Forums: Discuss implementations, research, etc. https://discuss.pytorch.org\n* GitHub Issues: Bug reports, feature requests, install issues, RFCs, thoughts, etc.\n* Slack: The [PyTorch Slack](https://pytorch.slack.com/) hosts a primary audience of moderate to experienced PyTorch users and developers for general chat, online discussions, collaboration, etc. If you are a beginner looking for help, the primary medium is [PyTorch Forums](https://discuss.pytorch.org). If you need a slack invite, please fill this form: https://goo.gl/forms/PP1AGvNHpSaJP8to1\n* Newsletter: No-noise, a one-way email newsletter with important announcements about PyTorch. You can sign-up here: https://eepurl.com/cbG0rv\n* Facebook Page: Important announcements about PyTorch. https://www.facebook.com/pytorch\n* For brand guidelines, please visit our website at [pytorch.org](https://pytorch.org/)\n\n## Releases and Contributing\n\nPyTorch has a 90-day release cycle (major releases). Please let us know if you encounter a bug by [filing an issue](https://github.com/pytorch/pytorch/issues).\n\nWe appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion.\n\nIf you plan to contribute new features, utility functions, or extensions to the core, please first open an issue and discuss the feature with us.\nSending a PR without discussion might end up resulting in a rejected PR because we might be taking the core in a different direction than you might be aware of.\n\nTo learn more about making a contribution to Pytorch, please see our [Contribution page](CONTRIBUTING.md).\n\n## The Team\n\nPyTorch is a community-driven project with several skillful engineers and researchers contributing to it.\n\nPyTorch is currently maintained by [Adam Paszke](https://apaszke.github.io/), [Sam Gross](https://github.com/colesbury), [Soumith Chintala](http://soumith.ch) and [Gregory Chanan](https://github.com/gchanan) with major contributions coming from hundreds of talented individuals in various forms and means.\nA non-exhaustive but growing list needs to mention: Trevor Killeen, Sasank Chilamkurthy, Sergey Zagoruyko, Adam Lerer, Francisco Massa, Alykhan Tejani, Luca Antiga, Alban Desmaison, Andreas Koepf, James Bradbury, Zeming Lin, Yuandong Tian, Guillaume Lample, Marat Dukhan, Natalia Gimelshein, Christian Sarofeen, Martin Raison, Edward Yang, Zachary Devito.\n\nNote: This project is unrelated to [hughperkins/pytorch](https://github.com/hughperkins/pytorch) with the same name. Hugh is a valuable contributor to the Torch community and has helped with many things Torch and PyTorch.\n\n## License\n\nPyTorch has a BSD-style license, as found in the [LICENSE](LICENSE) file.", + "readme_title": "![PyTorch Logo](https://github.com/pytorch/pytorch/blob/master/docs/source/_static/img/pytorch-logo-dark.png)", + "releases_count": 0, + "repo": "pytorch", + "repo_name": "pytorch", + "stars": 1, + "topics": [], + "updated_at": "2025-04-09T04:56:28Z", + "url": "https://github.com/hassan11196/pytorch", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/pytorch" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/qras" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 61, + "contributors_count": 2, + "contributors_top": [ + { + "contributions": 60, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + }, + { + "contributions": 1, + "html_url": null, + "login": "hassan111196" + } + ], + "created_at": "2018-12-05T10:12:11Z", + "default_branch": "master", + "description": "QR Based Student Attendance System. Precursor to QRSMS", + "dirs_total_count": 7, + "files_root_entries": [ + { + "path": ".github", + "size": 0, + "type": "dir" + }, + { + "path": ".gitignore", + "size": 24, + "type": "file" + }, + { + "path": "Procfile", + "size": 26, + "type": "file" + }, + { + "path": "README.md", + "size": 655, + "type": "file" + }, + { + "path": "_config.yml", + "size": 28, + "type": "file" + }, + { + "path": "application.py", + "size": 44596, + "type": "file" + }, + { + "path": "bash_export_keys.sh", + "size": 248, + "type": "file" + }, + { + "path": "course_parser.py", + "size": 8528, + "type": "file" + }, + { + "path": "deprecated.py", + "size": 9504, + "type": "file" + }, + { + "path": "dev.txt", + "size": 2183, + "type": "file" + }, + { + "path": "error.log", + "size": 2400, + "type": "file" + }, + { + "path": "extra_1.html", + "size": 3093, + "type": "file" + }, + { + "path": "extra_2.html", + "size": 3871, + "type": "file" + }, + { + "path": "favicon.ico", + "size": 9740, + "type": "file" + }, + { + "path": "finance.db", + "size": 53248, + "type": "file" + }, + { + "path": "helpers.py", + "size": 2888, + "type": "file" + }, + { + "path": "images", + "size": 0, + "type": "dir" + }, + { + "path": "pdb_parsers.py", + "size": 3681, + "type": "file" + }, + { + "path": "requirements.txt", + "size": 108, + "type": "file" + }, + { + "path": "static", + "size": 0, + "type": "dir" + }, + { + "path": "table_classes.py", + "size": 7126, + "type": "file" + }, + { + "path": "templates", + "size": 0, + "type": "dir" + }, + { + "path": "tester.py", + "size": 1848, + "type": "file" + }, + { + "path": "wsgi.py", + "size": 44, + "type": "file" + } + ], + "files_total_count": 71, + "first_commit_date_default_branch": "2018-12-05T10:17:54Z", + "forks": 0, + "has_ci": true, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": false, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": "http://www.qras.ml/", + "input_url": "https://github.com/hassan11196/qras", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Python", + "size": 78215 + }, + { + "language": "HTML", + "size": 76939 + }, + { + "language": "JavaScript", + "size": 3521 + }, + { + "language": "CSS", + "size": 2532 + }, + { + "language": "Shell", + "size": 248 + } + ], + "last_commit_date_default_branch": "2020-09-29T13:03:46Z", + "last_commit_oid_default_branch": "40d2b8337f1399e04e2c8738b21cc1187b7d3035", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": null, + "license_spdx": null, + "name_with_owner": "hassan11196/qras", + "owner": "hassan11196", + "parent_repo": null, + "parent_url": null, + "primary_language": "Python", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 1, + "pull_requests_open": 0, + "pull_requests_total": 1, + "pushed_at": "2020-09-29T13:03:47Z", + "readme_length": 654, + "readme_text": "# QRAS\n\n\nQR based Attendance system that consists of an online portal separate for teachers and students.\n\nStudents can register themselves on the portal and will be approved by their respective teachers.\n\nQR code can be generated by the teacher which will be scanned by the students using the MIT-App inventor android app to mark their attendance.\n\nPortal : [Click Here To Open QRAS Portal - www.qras.ml](www.qras.ml)\n\n## Screenshots:\n- ![](images/about.png)\n- ![](images/attendance.png)\n- ![](images/home.png)\n- ![](images/list.png)\n- ![](images/students.png)\n\n| ![](images/qras-app1.png) | ![](images/qras-app2.png) |\n| ----------- | ----------- |", + "readme_title": "QRAS", + "releases_count": 0, + "repo": "qras", + "repo_name": "qras", + "stars": 1, + "topics": [ + "flask", + "sqlite", + "jinja", + "html", + "css", + "bootstrap", + "javascript" + ], + "updated_at": "2020-10-01T16:52:30Z", + "url": "https://github.com/hassan11196/qras", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/qras" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/react-image-cropper" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 129, + "contributors_count": 10, + "contributors_top": [ + { + "contributions": 76, + "html_url": "https://github.com/flyfloor", + "login": "flyfloor" + }, + { + "contributions": 18, + "html_url": null, + "login": "Dan White" + }, + { + "contributions": 18, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + }, + { + "contributions": 5, + "html_url": "https://github.com/danbrianwhite", + "login": "danbrianwhite" + }, + { + "contributions": 3, + "html_url": "https://github.com/tinsaye", + "login": "tinsaye" + }, + { + "contributions": 3, + "html_url": null, + "login": "薛嘉琪" + }, + { + "contributions": 2, + "html_url": "https://github.com/iamJoeTaylor", + "login": "iamJoeTaylor" + }, + { + "contributions": 2, + "html_url": null, + "login": "Roy Meissner" + } + ], + "created_at": "2020-05-17T01:21:16Z", + "default_branch": "master", + "description": "react image crop", + "dirs_total_count": 6, + "files_root_entries": [ + { + "path": ".babelrc", + "size": 36, + "type": "file" + }, + { + "path": ".github", + "size": 0, + "type": "dir" + }, + { + "path": ".gitignore", + "size": 47, + "type": "file" + }, + { + "path": ".jshintrc", + "size": 579, + "type": "file" + }, + { + "path": "LICENSE", + "size": 1072, + "type": "file" + }, + { + "path": "README.md", + "size": 3071, + "type": "file" + }, + { + "path": "component", + "size": 0, + "type": "dir" + }, + { + "path": "demo", + "size": 0, + "type": "dir" + }, + { + "path": "dist", + "size": 0, + "type": "dir" + }, + { + "path": "index.html", + "size": 389, + "type": "file" + }, + { + "path": "index.js", + "size": 100, + "type": "file" + }, + { + "path": "lib", + "size": 0, + "type": "dir" + }, + { + "path": "package-lock.json", + "size": 278047, + "type": "file" + }, + { + "path": "package.json", + "size": 1897, + "type": "file" + }, + { + "path": "server.js", + "size": 773, + "type": "file" + }, + { + "path": "webpack.config.js", + "size": 2463, + "type": "file" + } + ], + "files_total_count": 18, + "first_commit_date_default_branch": "2016-02-17T07:20:09Z", + "forks": 0, + "has_ci": true, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": "https://braavos.me/react-image-cropper/", + "input_url": "https://github.com/hassan11196/react-image-cropper", + "is_archived": false, + "is_fork": true, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "JavaScript", + "size": 69817 + }, + { + "language": "HTML", + "size": 389 + } + ], + "last_commit_date_default_branch": "2020-06-30T14:35:40Z", + "last_commit_oid_default_branch": "64913461f1439ed12399de77ae3cc86aab7a5343", + "latest_release_date": "2020-06-30T14:36:20Z", + "latest_release_tag": "v2.9", + "license_name": "MIT License", + "license_spdx": "MIT", + "name_with_owner": "hassan11196/react-image-cropper", + "owner": "hassan11196", + "parent_repo": "osamaaftab/react-image-cropper", + "parent_url": "https://github.com/osamaaftab/react-image-cropper", + "primary_language": "JavaScript", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2020-06-30T14:36:20Z", + "readme_length": 3064, + "readme_text": "## React Image Cropper\n🚨🚨 Abandoned. The Size of cropped image was unusually large. I couldnt find the issue and moved to \n[react-avatar-editor](https://www.npmjs.com/package/react-avatar-editor)\n\n### This is a fork with fixes for Custom Code for cropping limits.\n\n### Merged Changes from tinsaye and slidewiki.\n\n[![Downloads](https://img.shields.io/npm/dt/react-image-cropper.svg)](https://www.npmjs.com/package/react-image-cropper)\n[![Version](https://img.shields.io/npm/v/react-image-cropper.svg)](https://www.npmjs.com/package/react-image-cropper)\n\nThis project has been forked from [https://github.com/jerryshew/react-image-cropper](https://github.com/jerryshew/react-image-cropper), v1.1.2 and includes some changes that jerryshew did not wanted to merge. See this repo also for React versions >0.15.\n\n[![Downloads](https://img.shields.io/npm/dt/slidewiki-react-image-cropper.svg)](https://www.npmjs.com/package/slidewiki-react-image-cropper)\n[![Version](https://img.shields.io/npm/v/slidewiki-react-image-cropper.svg)](https://www.npmjs.com/package/slidewiki-react-image-cropper)\n\nSee the original repository for a demo of the image cropper - [https://github.com/jerryshew/react-image-cropper](https://github.com/jerryshew/react-image-cropper)\n\n**Our custom changes Custom:**\n\n+ limit the cropper container height to a max value (useful for portait images (e.g. 9:21), that would cause the cropper to stretch a lot)\n+ define max crop sizes that cause the cropper to limit the resulting image to some maximum values\n\n### How to Use\n\n+ `import {Cropper} from 'react-image-cropper'`\n\n+ styles are all inline\n\n+ define Cropper with src, and ref to execute crop method \n\n```\n\n```\n\n+ crop and get image url\n\n`image.src = this.refs.cropper.crop()`\n\n+ get crop values\n\nOPTIONS (optional):\n\n+ maxHeight : make sure the cropped image is not bigger than this max height\n+ maxWidth : make sure the cropped image is not bigger than this max width\n\n`image.src = this.refs.cropper.crop(maxWidth, maxHeight)`\n\n`var values = this.refs.cropper.values()`\n\n+ onChange for preview\n\n(values) => onChange(values)\n\n+ custom use\n\n| prop | value |\n|:-------:|:--------|\n| ratio | width / height |\n| width | cropper frame width |\n| height | cropper frame height |\n| originX | cropper original position(x axis), accroding to image left|\n| originY | cropper original position(Y axis), accroding to image top|\n| fixedRatio | turn on/off fixed ratio (bool default true) |\n| allowNewSelection | allow user to create a new selection instead of reusing initial selection (bool default true) |\n| styles | specify styles to override inline styles |\n| onImgLoad | specify fuction callback to run when the image completed loading |\n| beforeImgload | specify function callback to run when the image size value is ready but image is not completed loading |\n| onChange | triggred when dragging stop, get values of cropper |\n| limitHeight | limit the height of the cropper, e.g. if the img is a portrait |", + "readme_title": "## React Image Cropper", + "releases_count": 16, + "repo": "react-image-cropper", + "repo_name": "react-image-cropper", + "stars": 0, + "topics": [], + "updated_at": "2020-06-30T14:35:43Z", + "url": "https://github.com/hassan11196/react-image-cropper", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/react-image-cropper" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + 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"readme_text": "[![Netlify Status](https://api.netlify.com/api/v1/badges/0643e551-0fef-4f57-9d3c-29d576f3e06c/deploy-status)](https://app.netlify.com/sites/gatsby-personal-starter-blog/deploys)\n\n\"screenshot\"\n\nThis is the search-enabled version of Gatsby Personal Starter Blog!\n\n## 🚀 Quick start\n\n1. **Create a Gatsby site.**\n\n Use the Gatsby CLI to create a new site, specifying the blog starter.\n\n ```sh\n # create a new Gatsby site using the blog starter\n gatsby new my-blog-starter https://github.com/thomaswang/gatsby-personal-starter-blog\n ```\n\n1. **Start developing.**\n\n Navigate into your new site’s directory and start it up.\n\n ```sh\n cd my-blog-starter/\n gatsby develop\n ```\n\n1. **Open the source code and start editing!**\n\n Your site is now running at `http://localhost:8000`!\n\n _Note: You'll also see a second link: _`http://localhost:8000/___graphql`_. This is a tool you can use to experiment with querying your data. Learn more about using this tool in the [Gatsby tutorial](https://www.gatsbyjs.org/tutorial/part-five/#introducing-graphiql)._\n\n Open the `my-blog-starter` directory in your code editor of choice and edit `src/pages/index.js`. Save your changes and the browser will update in real time!\n\n## 🎓 Learning Gatsby\n\nLooking for more guidance? Full documentation for Gatsby lives [on the website](https://www.gatsbyjs.org/). Here are some places to start:\n\n- **For most developers, we recommend starting with our [in-depth tutorial for creating a site with Gatsby](https://www.gatsbyjs.org/tutorial/).** It starts with zero assumptions about your level of ability and walks through every step of the process.\n\n- **To dive straight into code samples, head [to our documentation](https://www.gatsbyjs.org/docs/).** In particular, check out the _Guides_, _API Reference_, and _Advanced Tutorials_ sections in the sidebar.\n\n## 📔 Learning Netlify CMS with Gatsby\n\nStart from [this point](https://www.gatsbyjs.org/docs/sourcing-from-netlify-cms/#authenticating-with-github) in the Gatsby documentation to finish the set up for your personal Netlify CMS with authentication.\n\n## 💫 Deploy\n\n[![Deploy to Netlify](https://www.netlify.com/img/deploy/button.svg)](https://app.netlify.com/start/deploy?repository=https://github.com/thomaswangio/gatsby-personal-starter-blog)", + "readme_title": "create a new Gatsby site using 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Installing\n\nClone the repository:\n\n```\n$ git clone https://github.com/mirumee/saleor-dashboard.git\n```\n\nEnter the project directory:\n\n```\n$ cd saleor-dashboard\n```\n\n#### Using stable release\n\nTo use the official stable release, checkout to a release tag:\n\n```\n$ git checkout 2.10.1\n```\n\nSee the list of all releases here: https://github.com/mirumee/saleor-dashboard/releases/\n\n#### Using development version\n\nIf you want to use the latest development version, checkout to the `master` branch:\n\n```\n$ git checkout master\n```\n\nInstall NPM dependencies:\n\n```\n$ npm i\n```\n\n### Configuration\n\nThere are two environment variables available for configuration:\n\n- `API_URI` (required) - URI of a running instance of Saleor GraphQL API.\n If you are running Saleor locally with the default settings, set `API_URI` to: `http://localhost:8000/graphql/`.\n\n- `APP_MOUNT_URI` - URI at which the Dashboard app will be mounted.\n E.g. if you set `APP_MOUNT_URI` to 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We don't advise using it in production._\n\nA GraphQL-powered, PWA, single-page application storefront for [Saleor](https://github.com/mirumee/saleor/).\n\n## Features\n\n- Headless ecommerce storefront built with [GraphQL](https://graphql.org/), [Apollo Client](https://www.apollographql.com/client), [React](https://reactjs.org/) and [Typescript](https://www.typescriptlang.org/)\n- Offline mode (beta)\n- Saleor GraphQL API integration\n- Single-page application experience\n- [Braintree Payment Gateway](https://www.braintreepayments.com/) integration\n\n## Demo\n\nSee the [public demo](http://pwa.saleor.io) of Saleor Storefront!\n\nOr launch the demo on a free Heroku instance.\n\n[![Deploy](https://www.herokucdn.com/deploy/button.svg)](https://heroku.com/deploy)\n\n## Getting Started\n\nThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes.\n\n### Prerequisites\n\n- Node.js 10.0+\n- A running instance of Saleor.\n\n To run the storefront, you have to set the `API_URI` environment variable to point to the Saleor GraphQL API. If you are running Saleor locally with the default settings, set `API_URI` to: `http://localhost:8000/graphql/`.\n\n### Installing\n\nClone the repository:\n\n```\ngit clone https://github.com/mirumee/saleor-storefront.git\n```\n\nEnter the project directory:\n\n```\ncd saleor-storefront\n```\n\n#### Using stable release\n\nTo use the official stable release, checkout to a release tag:\n\n```\n$ git checkout 2.10.4\n```\n\nSee the list of all releases here: https://github.com/mirumee/saleor-storefront/releases/\n\n#### Using development version\n\nIf you want to use the latest development version, checkout to the `master` branch:\n\n```\n$ git checkout master\n```\n\nInstall NPM dependencies:\n\n```\nnpm i\n```\n\nRun the development server:\n\n```\nnpm start\n```\n\nGo to `http://localhost:3000` to access the storefront.\n\n## Cypress tests\n\nIf you want to run [Cypress](https://www.cypress.io/) tests, make sure that all dependecies (including `Cypress`) are installed by running the install command.\n\n```\nnpm i\n```\n\nFollowing environment variables are required to be set in order to be able to run tests properly:\n\n- `API_URI` - GraphQL API address.\n- `CYPRESS_USER_NAME` - username (email) for `Storefront` user.\n- `CYPRESS_USER_PASSWORD` - for the user mentioned above.\n\nIf you are runninng the Storefront from the perspective of `Docker` container, then you can run tests using following commands:\n\nHeadless mode:\n\n```\ncy:run\n```\n\nCypress UI mode:\n\n```\ncy:open\n```\n\nIf you want to run tests against your local development environment then use following commands:\n\nHeadless mode:\n\n```\ntest:e2e:run\n```\n\nCypress UI mode:\n\n```\ntest:e2e:dev\n```\n\n## License\n\nThis project is licensed under the BSD-3-Clause License - see the [LICENSE](https://github.com/mirumee/saleor-storefront/blob/master/LICENSE) file for details\n\n#### Crafted with ❤️ by [Mirumee Software](http://mirumee.com)\n\nhello@mirumee.com", + "readme_title": "Saleor 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📦🚀 semantic-release

\n

Fully automated version management and package publishing

\n

\n \n \"Join\n \n \n \"Travis\"\n \n \n \"Codecov\"\n \n \n \"Greenkeeper\"\n \n \n \"semantic-release\"\n \n

\n

\n \n \"npm\n \n \n \"npm\n \n \n \"npm\n \n

\n\n**semantic-release** automates the whole package release workflow including: determining the next version number, generating the release notes and publishing the package.\n\nThis removes the immediate connection between human emotions and version numbers, strictly following the [Semantic Versioning](http://semver.org) specification.\n\n> Trust us, this will change your workflow for the better. – [egghead.io](https://egghead.io/lessons/javascript-how-to-write-a-javascript-library-automating-releases-with-semantic-release)\n\n## Highlights\n\n- Fully automated release\n- Enforce [Semantic Versioning](https://semver.org) specification\n- New features and fixes are immediately available to users\n- Notify maintainers and users of new releases\n- Use formalized commit message convention to document changes in the codebase\n- Publish on different distribution channels (such as [npm dist-tags](https://docs.npmjs.com/cli/dist-tag)) based on git merges\n- Integrate with your [continuous integration workflow](docs/recipes/README.md#ci-configurations)\n- Avoid potential errors associated with manual releases\n- Support any [package managers and languages](docs/recipes/README.md#package-managers-and-languages) via [plugins](docs/usage/plugins.md)\n- Simple and reusable configuration via [shareable configurations](docs/usage/shareable-configurations.md)\n\n## How does it work?\n\n### Commit message format\n\n**semantic-release** uses the commit messages to determine the type of changes in the codebase. Following formalized conventions for commit messages, **semantic-release** automatically determines the next [semantic version](https://semver.org) number, generates a changelog and publishes the release.\n\nBy default **semantic-release** uses [Angular Commit Message Conventions](https://github.com/angular/angular.js/blob/master/DEVELOPERS.md#-git-commit-guidelines). The commit message format can be changed with the [`preset` or `config` options](docs/usage/configuration.md#options) of the [@semantic-release/commit-analyzer](https://github.com/semantic-release/commit-analyzer#options) and [@semantic-release/release-notes-generator](https://github.com/semantic-release/release-notes-generator#options) plugins.\n\nTools such as [commitizen](https://github.com/commitizen/cz-cli) or [commitlint](https://github.com/conventional-changelog/commitlint) can be used to help contributors and enforce valid commit messages.\n\nHere is an example of the release type that will be done based on a commit messages:\n\n| Commit message | Release type |\n|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------|\n| `fix(pencil): stop graphite breaking when too much pressure applied` | Patch Release |\n| `feat(pencil): add 'graphiteWidth' option` | ~~Minor~~ Feature Release |\n| `perf(pencil): remove graphiteWidth option`

`BREAKING CHANGE: The graphiteWidth option has been removed.`
`The default graphite width of 10mm is always used for performance reasons.` | ~~Major~~ Breaking Release |\n\n### Automation with CI\n\n**semantic-release** is meant to be executed on the CI environment after every successful build on the release branch. This way no human is directly involved in the release process and the releases are guaranteed to be [unromantic and unsentimental](http://sentimentalversioning.org).\n\n### Triggering a release\n\nFor each new commits added to one of the release branches (for example `master`, `next`, `beta`), with `git push` or by merging a pull request or merging from another branch, a CI build is triggered and runs the `semantic-release` command to make a release if there are codebase changes since the last release that affect the package functionalities.\n\n**semantic-release** offers various ways to control the timing, the content and the audience of published releases. See example workflows in the following recipes:\n- [Using distribution channels](docs/recipes/distribution-channels.md#publishing-on-distribution-channels)\n- [Maintenance releases](docs/recipes/maintenance-releases.md#publishing-maintenance-releases)\n- [Pre-releases](docs/recipes/pre-releases.md#publishing-pre-releases)\n\n### Release steps\n\nAfter running the tests, the command `semantic-release` will execute the following steps:\n\n| Step | Description |\n|-------------------|---------------------------------------------------------------------------------------------------------------------------------|\n| Verify Conditions | Verify all the conditions to proceed with the release. |\n| Get last release | Obtain the commit corresponding to the last release by analyzing [Git tags](https://git-scm.com/book/en/v2/Git-Basics-Tagging). |\n| Analyze commits | Determine the type of release based on the commits added since the last release. |\n| Verify release | Verify the release conformity. |\n| Generate notes | Generate release notes for the commits added since the last release. |\n| Create Git tag | Create a Git tag corresponding to the new release version. |\n| Prepare | Prepare the release. |\n| Publish | Publish the release. |\n| Notify | Notify of new releases or errors. |\n\n## Requirements\n\nIn order to use **semantic-release** you need:\n- To host your code in a [Git repository](https://git-scm.com)\n- Use a Continuous Integration service that allows you to [securely set up credentials](docs/usage/ci-configuration.md#authentication)\n- Git CLI version [2.7.1 or higher](docs/support/FAQ.md#why-does-semantic-release-require-git-version--271) installed in your Continuous Integration environment\n- [Node.js](https://nodejs.org) version [10.18 or higher](docs/support/FAQ.md#why-does-semantic-release-require-node-version--1018) installed in your Continuous Integration environment\n\n## Documentation\n\n- Usage\n - [Getting started](docs/usage/getting-started.md#getting-started)\n - [Installation](docs/usage/installation.md#installation)\n - [CI Configuration](docs/usage/ci-configuration.md#ci-configuration)\n - [Configuration](docs/usage/configuration.md#configuration)\n - [Plugins](docs/usage/plugins.md)\n - [Workflow configuration](docs/usage/workflow-configuration.md)\n - [Shareable configurations](docs/usage/shareable-configurations.md)\n- Extending\n - [Plugins](docs/extending/plugins-list.md)\n - [Shareable configuration](docs/extending/shareable-configurations-list.md)\n- Recipes\n - [CI configurations](docs/recipes/README.md)\n - [Git hosted services](docs/recipes/README.md)\n - [Release workflow](docs/recipes/README.md)\n - [Package managers and languages](docs/recipes/README.md)\n- Developer guide\n - [JavaScript API](docs/developer-guide/js-api.md)\n - [Plugins development](docs/developer-guide/plugin.md)\n - [Shareable configuration development](docs/developer-guide/shareable-configuration.md)\n- Support\n - [Resources](docs/support/resources.md)\n - [Frequently Asked Questions](docs/support/FAQ.md)\n - [Troubleshooting](docs/support/troubleshooting.md)\n - [Node version requirement](docs/support/node-version.md)\n - [Node Support Policy](docs/support/node-support-policy.md)\n\n## Get help\n\n- [Stack Overflow](https://stackoverflow.com/questions/tagged/semantic-release)\n- [Spectrum community](https://spectrum.chat/semantic-release)\n- [Twitter](https://twitter.com/SemanticRelease)\n\n## Badge\n\nLet people know that your package is published using **semantic-release** by including this badge in your readme.\n\n[![semantic-release](https://img.shields.io/badge/%20%20%F0%9F%93%A6%F0%9F%9A%80-semantic--release-e10079.svg)](https://github.com/semantic-release/semantic-release)\n\n```md\n[![semantic-release](https://img.shields.io/badge/%20%20%F0%9F%93%A6%F0%9F%9A%80-semantic--release-e10079.svg)](https://github.com/semantic-release/semantic-release)\n```\n\n## Team\n\n| [![Stephan Bönnemann](https://github.com/boennemann.png?size=100)](https://github.com/boennemann) | [![Rolf Erik Lekang](https://github.com/relekang.png?size=100)](https://github.com/relekang) | [![Johannes Jörg Schmidt](https://github.com/jo.png?size=100)](https://github.com/jo) | [![Gregor Martynus](https://github.com/gr2m.png?size=100)](https://github.com/gr2m) | [![Pierre Vanduynslager](https://github.com/finnp.png?size=100)](https://github.com/finnp) | [![Pierre Vanduynslager](https://github.com/pvdlg.png?size=100)](https://github.com/pvdlg) | [![Christoph Witzko](https://github.com/christophwitzko.png?size=100)](https://github.com/christophwitzko) |\n|---------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------|\n| [Stephan Bönnemann](https://github.com/boennemann) | [Rolf Erik Lekang](https://github.com/relekang) | [Johannes Jörg Schmidt](https://github.com/jo) | [Gregor Martynus](https://github.com/gr2m) | [Finn Pauls](https://github.com/finnp) | [Pierre Vanduynslager](https://github.com/pvdlg) | [Christoph Witzko](https://github.com/christophwitzko) |\n\n

\n \"Kill\n

", + "readme_title": "

📦🚀 semantic-release

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Ask in `#bugs` / `#feedback` / `#questions` on the [Official Discord](https://discord.com/invite/HN7EVzV) if you are not entirely sure if it's a bug etc.\n2. Check out the trello board: https://trello.com/b/ISQncpJP/shapezio\n3. See if it's already there - If so, vote for it, done. I will see it. (You have to be signed in on trello)\n4. If not, check if it's already reported here: https://github.com/tobspr/shapez.io/issues\n5. If not, file a new issue here: https://github.com/tobspr/shapez.io/issues/new\n6. I will then have a look (This can take days or weeks) and convert it to trello, and comment with the link. You can then vote there ;)\n\n## Building\n\n- Make sure `ffmpeg` is on your path\n- Install Node.js and Yarn\n- Install Java (required for textures)\n- Run `yarn` in the root folder\n- Cd into `gulp` folder\n- Run `yarn` and then `yarn gulp` - it should now open in your browser\n\n**Notice**: This will produce a debug build with several debugging flags enabled. If you want to disable them, modify [`src/js/core/config.js`](src/js/core/config.js).\n\n## Build Online with one-click setup\n\nYou can use [Gitpod](https://www.gitpod.io/) (an Online Open Source VS Code-like IDE which is free for Open Source) for working on issues and making PRs to this project. With a single click it will start a workspace and automatically:\n\n- clone the `shapez.io` repo.\n- install all of the dependencies.\n- start `gulp` in `gulp/` directory.\n\n[![Open in Gitpod](https://gitpod.io/button/open-in-gitpod.svg)](https://gitpod.io/from-referrer/)\n\n## Helping translate\n\nPlease checkout the [Translations readme](translations/).\n\n## Contributing\n\nSince this game is in the more or less early development, I will only accept pull requests which add an immediate benefit. Please understand that low quality PR's might be closed by me with a short comment explaining why.\n\n**If you want to add a new building, please understand that I can not simply add every building to the game!** I recommend to talk to me before implementing anything, to make sure its actually useful. Otherwise there is a high chance of your PR not getting merged.\n\nIf you want to add a new feature or in generally contribute I recommend to get in touch with me on Discord:\n\n\n\"discord\n\n\n### Code\n\nThe game is based on a custom engine which itself is based on the YORG.io 3 game engine (Actually it shares almost the same core).\nThe code within the engine is relatively clean with some code for the actual game on top being hacky.\n\nThis project is based on ES5. Some ES2015 features are used but most of them are too slow, especially when polyfilled. For example, `Array.prototype.forEach` is only used within non-critical loops since its slower than a plain for loop.\n\n#### Adding a new component\n\n1. Create the component file in `src/js/game/components/.js`\n2. Create a component class (e.g. `MyFancyComponent`) which `extends Component`\n3. Create a `static getId()` method which should return the `PascalCaseName` without component (e.g. `MyFancy`)\n4. If any data needs to be persisted, create a `static getSchema()` which should return the properties to be saved (See other components)\n5. Add a constructor. **The constructor must be called with optional parameters only!** `new MyFancyComponent({})` should always work.\n6. Add any props you need in the constructor.\n7. Add the component in `src/js/game/component_registry.js`\n8. Add the component in `src/js/game/entity_components.js`\n9. Done! You can use your component now\n\n#### Adding a new building\n\n(The easiest way is to copy an existing building)\n\n1. Create your building in `src/js/game/buildings/`\n2. Create the building meta class, e.g. `MetaMyFancyBuilding extends MetaBuilding`\n3. Override the methods from MetaBuilding you want to override.\n4. Most important is `setupEntityComponents`\n5. Add the building to `src/js/game/meta_building_registry.js`: You need to register it on the registry, and also call `registerBuildingVariant`.\n6. Add the building to the right toolbar, e.g. `src/js/game/hud/parts/buildings_toolbar.js`:`supportedBuildings`\n7. Add a keybinding for the building in `src/js/game/key_action_mapper.js` in `KEYMAPPINGS.buildings`\n8. In `translations/base-en.yaml` add it to two sections: `buildings.[my_building].XXX` (See other buildings) and also `keybindings.mappings.[my_building]`. Be sure to do it the same way as other buildings do!\n9. Create a icon (128x128, [prefab](https://github.com/tobspr/shapez.io-artwork/blob/master/ui/toolbar-icons.psd)) for your building and save it in `res/ui/buildings_icons` with the id of your building\n10. Create a tutorial image (600x600) for your building and save it in `res/ui/building_tutorials`\n11. In `src/css/resources.scss` add your building to `$buildings` as well as `$buildingAndVariants`\n12. Done! Optional: Add a new reward for unlocking your building at some point.\n\n#### Adding a new game system\n\n1. Create the class in `src/js/game/systems/.js`\n2. Derive it from `GameSystemWithFilter` if you want it to work on certain entities only which have the given components. Otherwise use `GameSystem` to do more generic stuff.\n3. Implement the `update()` method.\n4. Add the system in `src/js/game/game_system_manager.js` (To `this.systems` and also call `add` in the `internalInitSystems()` method)\n5. If your system should draw stuff, this is a bit more complicated. Have a look at existing systems on how they do it.\n\n#### Checklist for a new building / testing it\n\nThis is a quick checklist, if a new building is added this points should be fulfilled:\n\n2. The translation for all variants is done and finalized\n3. The artwork (regular sprite) is finalized\n4. The blueprint sprite has been generated and is up to date\n5. The building has been added to the appropriate toolbar\n6. The building has a keybinding which makes sense\n7. The building has a reward assigned and is unlocked at a meaningful point\n8. The reward for the building has a proper translation\n9. The reward for the building has a proper image\n10. The building has a proper tutorial image assigned\n11. The buliding has a proper toolbar icon\n12. The reward requires a proper shape\n13. The building has a proper silhouette color\n14. The building has a proper matrix for being rendered on the minimap\n15. The building has proper statistics in the dialog\n16. The building properly contributes to the shapes produced analytics\n17. The building is properly persisted in the savegame\n18. The building is explained properly, ideally via an interactive tutorial\n\n### Assets\n\nFor most assets I use Adobe Photoshop, you can find them here.\n\nAll assets will be automatically rebuilt into the atlas once changed (Thanks to dengr1065!)\n\n\"shapez.io", + "readme_title": "shapez.io", + "releases_count": 0, + "repo": "shapez.io", + "repo_name": "shapez.io", + "stars": 0, + "topics": [], + "updated_at": "2021-04-18T22:15:00Z", + "url": "https://github.com/hassan11196/shapez.io", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/shapez.io" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/simplefolio" + ], + "github_repos_metadata": [ + { + "commit_count_default_branch": 162, + "contributors_count": 6, + "contributors_top": [ + { + "contributions": 128, + "html_url": "https://github.com/cobiwave", + "login": "cobiwave" + }, + { + "contributions": 24, + "html_url": "https://github.com/jacobo-martinez", + "login": "jacobo-martinez" + }, + { + "contributions": 6, + "html_url": "https://github.com/SNasser97", + "login": "SNasser97" + }, + { + "contributions": 2, + "html_url": "https://github.com/hassan11196", + "login": "hassan11196" + }, + { + "contributions": 1, + "html_url": "https://github.com/krit-s", + "login": "krit-s" + }, + { + "contributions": 1, + "html_url": "https://github.com/wschwab", + "login": "wschwab" + } + ], + "created_at": "2020-09-12T02:05:49Z", + "default_branch": "master", + "description": "⚡️ A minimal portfolio template for Developers", + "dirs_total_count": 11, + "files_root_entries": [ + { + "path": ".gitignore", + "size": 19, + "type": "file" + }, + { + "path": "LICENSE.md", + "size": 1073, + "type": "file" + }, + { + "path": "README.md", + "size": 10348, + "type": "file" + }, + { + "path": "examples", + "size": 0, + "type": "dir" + }, + { + "path": "netlify.toml", + "size": 1833, + "type": "file" + }, + { + "path": "package.json", + "size": 1221, + "type": "file" + }, + { + "path": "src", + "size": 0, + "type": "dir" + }, + { + "path": "webpack.common.js", + "size": 675, + "type": "file" + }, + { + "path": "webpack.dev.js", + "size": 717, + "type": "file" + }, + { + "path": "webpack.prod.js", + "size": 1369, + "type": "file" + } + ], + "files_total_count": 33, + "first_commit_date_default_branch": "2019-06-28T06:07:05Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": false, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": "https://simplfolio.netlify.com/", + "input_url": "https://github.com/hassan11196/simplefolio", + "is_archived": false, + "is_fork": true, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "SCSS", + "size": 11836 + }, + { + "language": "HTML", + "size": 10666 + }, + { + "language": "JavaScript", + "size": 5213 + } + ], + "last_commit_date_default_branch": "2020-09-12T02:24:06Z", + "last_commit_oid_default_branch": "13f68d37fb570a411383da5588b6a0af8ccaad33", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "MIT License", + "license_spdx": "MIT", + "name_with_owner": "hassan11196/simplefolio", + "owner": "hassan11196", + "parent_repo": "cobiwave/simplefolio", + "parent_url": "https://github.com/cobiwave/simplefolio", + "primary_language": "SCSS", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2020-09-12T02:24:07Z", + "readme_length": 10276, + "readme_text": "# Simplefolio ⚡️ [![GitHub](https://img.shields.io/github/license/cobidev/simplefolio?color=blue)](https://github.com/cobidev/simplefolio/blob/master/LICENSE.md) ![GitHub stars](https://img.shields.io/github/stars/cobidev/simplefolio) ![GitHub forks](https://img.shields.io/github/forks/cobidev/simplefolio)\n\n## A minimal portfolio template for Developers!\n\n

\n \"Simplefolio\"\n
\n

\n\n**_IMPORTANT NOTE_**: New fastest version came out, built with React + Gatsby! 🎉🎉🎉 See more: [Gatsby Simplefolio](https://github.com/cobidev/gatsby-simplefolio)\n\n

\n \"Look\n
\n

\n\n## Features\n\n⚡️ Modern UI Design + Reveal Animations\\\n⚡️ One Page Layout\\\n⚡️ Styled with Bootstrap v4.3 + Custom SCSS\\\n⚡️ Fully Responsive\\\n⚡️ Valid HTML5 & CSS3\\\n⚡️ Optimized with Webpack\\\n⚡️ Well organized documentation\n\nTo view a demo example, **[click here](https://simplfolio.netlify.com/)**\\\nTo view a live example, **[click here](https://cobidev.com/)**\n\n---\n\n## Getting Started 🚀\n\nThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.\n\n### Prerequisites 📋\n\nYou'll need [Git](https://git-scm.com) and [Node.js](https://nodejs.org/en/download/) (which comes with [NPM](http://npmjs.com)) installed on your computer.\n\n```\nnode@v10.16.0 or higher\nnpm@6.9.0 or higher\ngit@2.17.1 or higher\n```\n\nAlso, you can use [Yarn](https://yarnpkg.com/) instead of NPM ☝️\n\n```\nyarn@v1.21.1 or higher\n```\n\n---\n\n## How To Use 🔧\n\nFrom your command line, first clone Simplefolio:\n\n```bash\n# Clone this repository\n$ git clone https://github.com/cobidev/simplefolio\n\n# Go into the repository\n$ cd simplefolio\n\n# Remove current origin repository\n$ git remote remove origin\n```\n\nThen you can install the dependencies either using NPM or Yarn:\n\nUsing NPM:\n```bash\n# Install dependencies\n$ npm install\n\n# Start development server\n$ npm start\n```\nUsing Yarn:\n```bash\n# Install dependencies\n$ yarn\n\n# Start development server\n$ yarn start\n```\n\n**NOTE**:\nIf your run into issues installing the dependencies with NPM, use this command:\n\n```bash\n# Install dependencies with all permissions\n$ sudo npm install --unsafe-perm=true --allow-root\n```\n\nOnce your server has started, go to this url `http://localhost:8080/` and you will see the website running on a Development Server:\n\n

\n \"Simplefolio\"\n

\n\n---\n\n## Template Instructions:\n\n### Step 1 - STRUCTURE\n\nGo to `/src/template.html` and fill your information, they are 5 sections:\n\n### Hero Section\n\n- On `.hero-title`, put your custom title.\n- On `.hero-cta`, put your custom button cta.\n\n```html\n\n
\n
\n

\n Hi, my name is Your Name\n
\n I'm the Unknow Developer.\n

\n

\n Know more\n

\n
\n
\n\n```\n\n### About Section\n\n- On `` tag, fill the `src` property with your profile picture, your picture must be located inside `assets/` folder.\n- On `

` tag with class-name `.about-wrapper__info-text`, include information about you, I recommend to use 2 paragraphs in order to work well and a maximum of 3 paragraphs.\n- On last `` tag, include your resume url on `href` property.\n\n```html\n\n

\n
\n

\n About me\n

\n
\n
\n
\n \n
\n
\n
\n
\n

\n Lorem ipsum dolor sit, about my text.\n

\n

\n Lorem ipsum dolor sit, about my text.\n

\n \n \n View Resume\n \n \n
\n
\n
\n
\n
\n\n```\n\n### Projects Section\n\n- Each project lives inside on a `row`.\n- On `

` tag with class-name `.project-wrapper__text-title`, include your project title.\n- On `

` tag with `loremp ipsum` text, include your project information.\n- On first `` tag, put your project url on `href` property.\n- On second `` tag, put your project repository url on `href` property.\n\n---\n\n- Inside `

` tag with class-name `.project-wrapper__image`, put your project image url on the `src` of the `` and put again your project url on `href` property of `` tag.\n- Recommended size for project image (1366 x 767px), your project image must live on `assets/` folder.\n\n```html\n\n
\n ...\n \n
\n
\n
\n

[Project Title]

\n
\n

\n Lorem ipsum dolor sit, my project information.\n

\n
\n \n See Live\n \n \n Source Code\n \n
\n
\n
\n
\n \n
\n \n
\n \n
\n
\n
\n \n ...\n
\n```\n\n### Contact Section\n\n- On `

` tag with class-name `.contact-wrapper__text`, include some custom call-to-action message.\n- On `` tag, put your email address on `href` property.\n\n```html\n\n

\n
\n

\n Contact\n

\n
\n

\n Put here your contact CTA\n

\n Call to Action\n
\n
\n
\n\n```\n\n### Footer Section\n\n- Put your social media link on each `` links.\n- If you have more social-media accounts, see [Font Awesome Icons](https://fontawesome.com/v4.7.0/icons/) to put the corresponding additional social icon `.class`\n- You can delete or add as many `a` links your want.\n\n```html\n\n```\n\n### Step 2 - STYLES\n\nChange the color theme of the website ( choose 2 colors to create a gradient ):\n\nGo to `src/styles/abstracts/_variables.scss` and only change the values on this classes `$main-color` and `$secondary-color` to your prefered HEX color\n\n```scss\n// Default values\n$main-color: #02aab0;\n$secondary-color: #00cdac;\n```\n\n**NOTE**: I highly recommend to checkout gradients variations on [UI Gradient](https://uigradients.com/#BrightVault)\n\n---\n\n## Deployment 📦\n\nOnce you have done with your setup. You need to put your website online!\n\nI highly recommend to use [Netlify](https://netlify.com) to achieve this on the EASIEST WAY\n\nBecause this template use Webpack maybe you can get errors during deployment, Please watch my step-by-step video tutorial to successfully upload your Simplefolio Website on Netlify!\n\n**[WATCH NOW MY STEP-BY-STEP TUTORIAL FOR DEPLOYMENT](https://www.youtube.com/watch?v=soaG3GNSxJY)**\n\n## Others versions 👥\n\n[Gatsby Simplefolio](https://github.com/cobidev/gatsby-simplefolio) by [Jacobo Martinez](https://github.com/cobidev)\\\n[Ember.js Simplefolio](https://github.com/sernadesigns/simplefolio-ember) by [Michael Serna](https://github.com/sernadesigns)\n\n## Technologies used 🛠️\n\n- [Webpack](https://webpack.js.org/concepts/) - Static module bundler\n- [Bootstrap 4](https://getbootstrap.com/docs/4.3/getting-started/introduction/) - Front-end component library\n- [Sass](https://sass-lang.com/documentation) - CSS extension language\n- [ScrollReveal.js](https://scrollrevealjs.org/) - JavaScript library\n- [Tilt.js](https://gijsroge.github.io/tilt.js/) - JavaScript tiny parallax library\n- [Popper.js](https://popper.js.org/) - JavaScript popover library\n\n## Authors\n\n- **Jacobo Martinez** - [https://github.com/cobidev](https://github.com/cobidev)\n\n## Status\n\n[![Netlify Status](https://api.netlify.com/api/v1/badges/75600296-89eb-4640-9e7e-fa87fba7ce76/deploy-status)](https://app.netlify.com/sites/simplfolio/deploys)\n\n## License 📄\n\nThis project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details\n\n## Acknowledgments 🎁\n\nI was motivated to create this project because I wanted to contribute on something useful for the dev community, thanks to [ZTM Community](https://github.com/zero-to-mastery) and [Andrei](https://github.com/aneagoie)", + "readme_title": "Simplefolio ⚡️ [![GitHub](https://img.shields.io/github/license/cobidev/simplefolio?color=blue)](https://github.com/cobidev/simplefolio/blob/master/LICENSE.md) ![GitHub stars](https://img.shields.io/github/stars/cobidev/simplefolio) ![GitHub forks](https://img.shields.io/github/forks/cobidev/simplefolio)", + "releases_count": 0, + "repo": "simplefolio", + "repo_name": "simplefolio", + "stars": 0, + "topics": [], + "updated_at": "2020-09-12T02:24:10Z", + "url": "https://github.com/hassan11196/simplefolio", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/simplefolio" + }, + { + "awards": [], + "categories": [], + "description": null, + "github_repo_count": 1, + "github_repo_urls": [ + "https://github.com/hassan11196/simplefolio-hassan" 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false, + "is_fork": true, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Vue", + "size": 281633 + }, + { + "language": "SCSS", + "size": 161948 + }, + { + "language": "JavaScript", + "size": 53507 + }, + { + "language": "TypeScript", + "size": 40375 + }, + { + "language": "HTML", + "size": 10094 + }, + { + "language": "CSS", + "size": 2528 + } + ], + "last_commit_date_default_branch": "2023-08-29T08:01:13Z", + "last_commit_oid_default_branch": "a57787882f28af4e9498c32553e4432d518ca863", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "MIT License", + "license_spdx": "MIT", + "name_with_owner": "hassan11196/sneat-template", + "owner": "hassan11196", + "parent_repo": "themeselection/sneat-vuetify-vuejs-admin-template-free", + "parent_url": "https://github.com/themeselection/sneat-vuetify-vuejs-admin-template-free", + "primary_language": "Vue", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2023-08-29T08:01:13Z", + "readme_length": 11207, + "readme_text": "

\n\n

\n \n \"sneat-logo\"\n \n

\n\n

\n \n Sneat – Vuetify Vuejs Admin Template Free\n \n

\n\n

Production Ready, Carefully Crafted, Extensive Vuetify Admin Template

\n\n

\n \n \"license\"\n \n \"GitHub\n \n \"GitHub\n \n \n \"Twitter \n \n

\n\n[![Sneat – Vuetify Vuejs Admin Template Demo Screenshot](https://cdn.jsdelivr.net/gh/themeselection/ts-assets/sneat/sneat-vuetify-vuejs-admin-template-free/marketing/sneat-vuetify-vuejs-admin-template-free-github.png)](https://themeselection.com/item/sneat-free-vuetify-vuejs-admin-template/)\n\n## Introduction 🚀\n\nIf you're a developer looking for an admin dashboard that is developer-friendly, rich with features, and highly customizable look no further than Sneat. We've followed the highest industry standards to bring you the very best [VueJS Admin Template](https://themeselection.com/item/category/vuejs-admin-templates/) that is not only fast and easy to use but highly scalable. Offering ultimate convenience and flexibility, you'll be able to build whatever application you want with very little hassle.\n\n- ⚡️ [Vue 3](https://github.com/vuejs/core), [Vite 3](https://github.com/vitejs/vite) & [Vuetify 3](https://next.vuetifyjs.com/en/) - Uses the latest Vuetify 3\n- 🗂 [File based routing](https://github.com/hannoeru/vite-plugin-pages)\n- 📦 [Components auto importing](https://github.com/antfu/unplugin-vue-components)\n- 📑 [Layout system](https://github.com/JohnCampionJr/vite-plugin-vue-layouts)\n- 😃 Use icons from any icon sets with classes by [Iconify](https://iconify.design/)\n- 🌍 I18n ready\n- 🔥 Use the [`\n \n \n```\n\nHowever, one disadvantage of this alternative is that the IDE will not have access to the jQuery `$` variable object, making it a little difficult to write code. This disadvantage is fortunately overcome using the second alternative.\n\n[⬆️ Back to top](#table-of-contents)
\n\n\n\n### Using @types/[third-party-library] NPM packages\n\nThe problem with the first alternative, is that by declaring jQuery's `$` as type `any` (`declare var $: any;`), we are basically telling the compiler to *assume* that it will have access to jQuery in runtime. But that's not practical.\n\nTo avoid this we can use `@types` packages. These packages basically contain type definitions for their respective JS libraries counterparts. For example, using [@types/jquery](https://www.npmjs.com/package/@types/jquery) or [`@types/react`](https://www.npmjs.com/package/@types/react) will enable the programmer to use their respective types all over the application without having to declare them. This is because both of these libraries have `.d.ts` files includes and the compiler will pick them up inside the `node_modules` library.\n\nHere's an example using [`@types/jquery`](https://www.npmjs.com/package/@types/jquery) and `requirejs`:\n\n**Organization scheme:**\n\n```js\nTypeScript v^3.0\n├── package.json\n├── app.ts\n├── index.html\n```\n\n***package.json***\n\n```json\n \"devDependencies\": {\n \"@types/jquery\": \"^3.3.29\",\n },\n \"dependencies\": {\n \"requirejs\": \"^2.3.6\",\n }\n```\n\n***app.ts:***\n\n```ts\n $('button').click(function() {\n alert('Button was clicked!');\n });\n```\n\n***index.html using requirejs to import the modules:***\n\n```html\n \n \n \n \n \n```\n\nNotice the difference? We don't need to have the declare `.d.ts` file anymore, because the compiler will pick up on the `.d.ts` files inside the `@types/jquery` library.\n\n[⬆️ Back to top](#table-of-contents)
\n\n### Shorthand for Ambient Modules\n\n> If you don’t want to take the time to write out declarations before using a new module, you can use a shorthand declaration to get started quickly.\n\n***declarations.d.ts***\n\n```ts\n declare module \"hot-new-module\";\n```\n\n[⬆️ Back to top](#table-of-contents)
\n\n### Wildcard module declarations\n\n> Some module loaders such as SystemJS and AMD allow non-JavaScript content to be imported. These typically use a prefix or suffix to indicate the special loading semantics. Wildcard module declarations can be used to cover these cases.\n\n```ts\n declare module \"*!text\" {\n const content: string;\n export default content;\n }\n\n // Some do it the other way around.\n declare module \"json!*\" {\n const value: any;\n export default value;\n }\n```\n\n[⬆️ Back to top](#table-of-contents)
\n\n### UMD modules\n\n> Some libraries are designed to be used in many module loaders, or with no module loading (global variables). These are known as UMD modules. These libraries can be accessed through either an import or a global variable. For example:\n\n***math-lib.d.ts***\n\n```ts\n export function isPrime(x: number): boolean;\n export as namespace mathLib;\n```\n\n[⬆️ Back to top](#table-of-contents)
\n\n---\n\n## Interfaces\n\n> One of TypeScript’s core principles is that type-checking focuses on the shape that values have. This is sometimes called “duck typing” or “structural subtyping”. In TypeScript, interfaces fill the role of naming these types, and are a powerful way of defining contracts within your code as well as contracts with code outside of your project.\n\nWe can think of **interfaces** as a way to assign types to the structure of a variable. This variable may be an `argument`, a `class`, an `object`, you name it, think of it as making a **contract** with the `interface`. Here's how we can define a very simple `interface` and use it on a `variable` and on a `function`:\n\n```ts\n interface SimplePerson {\n firstName: string;\n }\n\n const simplePerson: SimplePerson = { firstName: 'Robert' };\n\n const simpleGreet = (simplePerson: SimplePerson) => console.log(`Hello ${simplePerson}!`);\n\n simpleGreet(simplePerson); // Prints: \"Hello Robert!\"\n```\n\nYou might think this would make interfaces not scallable because of very restrictive properties, but fortunately **properties can be optional**. More on this below!\n\n[⬆️ Back to top](#table-of-contents)
\n\n### Optional Properties\n\n[Very similar to the optional object properties](#optional-object-properties), to define an interface property as optional, we must place a `?` character after the key name of the property when defining it (a postfix notation). Here's an example:\n\n```ts\n interface SimplePerson {\n firstName: string;\n lastName?: string;\n age?: number;\n }\n\n // Not possible because the firstName key-value pair is missing.\n // const wrong: SimplePerson = { lastName: 'Molina', age: 24 };\n const right: SimplePerson = { firstName: 'Robert', age: 24 };\n```\n\n[⬆️ Back to top](#table-of-contents)
\n\n\n\n### Index Signatures (Dynamic Property Names)\n\nWe may also define the interface with *index signatures*, think of them like dynamic key-value pairs. Here's an example:\n\n```ts\n interface NamedPerson {\n firstName: string;\n age?: number;\n [propName: string]: any;\n }\n\n const person: NamedPerson = {\n firstName: 'Robert',\n lastName: 'Molina',\n age: 24,\n hobbies: ['Programming', 'Cooking'],\n greet(lastName: string) {\n console.log(`Hi, I am ${this.firstName} ${lastName}!`);\n }\n }\n```\n\nNotice how we can add very different properties now? This is a way of telling the compiler the following when declaring and defining `person`:\n\n1. `person` **must** have a *`firstName`* property of type `string`.\n2. `person` **may** have an *`age`* property. It **must** be of type `number` if it exists, anything else will result in an error when compiling.\n3. `person` **may** then have any number of properties of type `string`, each with a value of type `any`, all made possible by the **index signature** of: `[propName: string]: any;`.\n\n[⬆️ Back to top](#table-of-contents)
\n\n### Implements Keyword\n\n> One of the most common uses of interfaces in languages like `C#` and `Java`, that of explicitly enforcing that a class meets a particular contract, is also possible in TypeScript.\n\n[⬆️ Back to top](#table-of-contents)
\n\nHere are two more advanced examples, we can use an `interface` to define the subtypes of a `class`, or a `function`:\n\n\n\n#### *1.* Implementing an `interface` to a `Class`\n\n```ts\n interface NamedPerson {\n firstName: string;\n age?: number;\n [propName: string]: any;\n greet(lastName: string): void;\n }\n\n class Person implements NamedPerson {\n constructor(public firstName: string, public lastName: string) {}\n greet(lastName: string) {\n console.log(`Hi, I am ${this.firstName} ${lastName}!`);\n }\n }\n\n const myPerson = new Person('Robert', 'Molina');\n greet(myPerson); // Prints: \"Hi, I am Robert Moina\"\n```\n\n> Interfaces describe the **`public` side of the class**, rather than both the `public` and `private` side. This prohibits you from using them to check that a class also has particular types for the private side of the class instance.\n\n**Note that classes may be extended to another `class` and tied to an `interface` by using the `implements` keyword**.\n\nHere is an example as shown in the [TypeScript official documentation about interfaces](https://www.typescriptlang.org/docs/handbook/interfaces.html):\n\n```ts\n class Control {\n private state: any;\n }\n\n interface SelectableControl extends Control {\n select(): void;\n }\n\n class Button extends Control implements SelectableControl {\n select() { }\n }\n```\n\n[⬆️ Back to top](#table-of-contents)
\n\n\n\n#### *2.* Implementing an `interface` to a `function`\n\n> As we mentioned earlier, interfaces can describe the rich types present in real world JavaScript. Because of JavaScript’s dynamic and flexible nature, you may occasionally encounter an object that works as a combination of some of the types described above.\n\nOne such example is an object that acts as both a function and an object, with additional properties:\n\n```ts\n interface DoubleValueFunc {\n (number1: number, number2: number): number;\n }\n\n let myDoubleFunction: DoubleValueFunc;\n myDoubleFunction = (num1: number, num2: number) => {\n return (num1 + num2) * 2;\n }\n\n console.log(myDoubleFunction(10,50)); // Prints: 120\n```\n\n[⬆️ Back to top](#table-of-contents)
\n\n### Extends Keyword (Interface Inheritance)\n\nSimilar to how a `class` can extend its properties by inheriting another `class` or `abstract class` properties, **interfaces may also inherit properties to extend its own properties**. Here's a simple example similar to the one as shown in the [TypeScript official documentation about extending interfaces](https://www.typescriptlang.org/docs/handbook/interfaces.html):\n\n```ts\n interface Shape {\n color: string;\n }\n\n interface Square extends Shape {\n sideLength: number;\n }\n\n const square: Square = {\n color: \"blue\",\n sideLength: 10\n };\n```\n\nBy extending an interface, we are essentially mixing the \"old\" interface into a new interface, with newly added properties. To quote the official documentation:\n\n> This allows us to copy the members of one interface into another, which gives you more flexibility in how you separate your interfaces into reusable components.\n\nIt is also possible to combine any number of interfaces into one, this a concept known as **composition** in programming, and it is possible to *compose* multiple interfaces into one, we will use this to serve as an example of *composition* **by extending multiple interfaces into a single interface**. Here's another example similar to the one as shown in the [TypeScript official documentation about extending interfaces](https://www.typescriptlang.org/docs/handbook/interfaces.html):\n\n```ts\n interface Shape {\n color: string;\n }\n\n interface PenStroke {\n penWidth: number;\n }\n\n interface Square extends Shape, PenStroke {\n sideLength: number;\n }\n\n const square: Square = {\n color: \"magenta\",\n penWidth: 2.5,\n sideLength: 0.5\n }\n```\n\nNotice, how both `Shape` and `PenStroke` were mixed to create `Square` while also declaring an additional property named `sideLength`. Bear in mind that **all** of the extended interfaces must be **structurally compatible** or else the compiler will throw an error. As a final note, we can also omit (by either picking or excluding) properties, more on this directly below.\n\n[⬆️ Back to top](#table-of-contents)
\n\n\n\n## `Omit` type when extending an interface\n\nSometimes, we might want to omit certain types from an extended interface. One of the most common reasons is to **overwrite** a property. Let's first define the `Omit` special type that we will be using to exclude properties, and then we'll do a scenario exercise:\n\n```ts\ntype Omit = Pick>\n```\n\nThe angle brackets and the content inside them are part of what is known as `Generics`. They are a way to declare, basically, generic typing similarly to how an array of strings may be declared as `string[]`. More information about Generics on its section down below after the Interfaces!\n\nHere's a breakdown of how the `Omit` type works. It's essentially doing the opposite of what the special `Pick` utility type does. In TypeScript 2.1, the `Pick` **utility** type was introduced, `Pick` allows us to **only pick certain properties of an object, to create a new object**, here's an example as shown in the [TypeScript official documentation](https://www.typescriptlang.org/docs/handbook/release-notes/typescript-2-1.html):\n\n```ts\n// From T pick a set of properties K\ndeclare function pick(obj: T, ...keys: K[]): Pick;\n\nconst nameAndAgeOnly = pick(person, \"name\", \"age\"); // { name: string, age: number }\n```\n\nIn TypeScript 2.8, the `Exclude` type was added to the standard library, which allows an omission type to be written as shown directly below:\n\n```ts\ntype T00 = Exclude<\"a\" | \"b\" | \"c\" | \"d\", \"a\" | \"c\" | \"f\">; // \"b\" | \"d\"\n```\n\nYou can read the above as \"exclude `\"a\"`, `\"c\"`, and `\"f\"` from `(\"a\" | \"b\" | \"c\" | \"d\")`, which results in `\"b\"` or `\"d\"`.\n\nHaving said this, **`Omit` picks every property of an object `T`, excluding the specified `K` properties (or keys) of `T`**.\n\n[⬆️ Back to top](#table-of-contents)
\n\n\n\n## Omitting specific properties when extending an interface\n\nLet's say we are creating a React component that is based on a common HTML `` element. We want our component to receive all of the HTML attributes as props that a regular `` element may receive, but we want to overwrite some to improve or enhance functionalities! So, for this example let's say that we want to overwrite the `onChange` attribute.\n\nTo overwrite the `onChange` attribute, we must omit it from the extended base interface, then declare it on the props interface. Our new `onChange` callback will return a variable of type `value` defined below, instead of an event. Here's an example:\n\n```ts\ntype Omit = Pick>;\n\ntype value = string | number | string[];\n\n/**\n * Base interface.\n */\ntype InputElementAttributes = React.DetailedHTMLProps, HTMLInputElement>;\n\ninterface IInputProps extends IInputState, Omit {\n onChange: (value?: value) => void\n}\n\n```\n\n[⬆️ Back to top](#table-of-contents)
\n\n\n\n## Type checking for interfaces\n\n**Hard** checking for interface is not yet available, but there are ways to achieve the same results. I will explain two commonly used methods to check for interfaces. These methods are:\n\n1. Use **type-guards** to check for instances of specifc objects.\n\n[The TypeScript official documentation defines a **type guard** as](https://www.typescriptlang.org/docs/handbook/advanced-types.html) some expression that performs a runtime check that guarantees the type in some scope. To define a type guard, we simply need to define a function whose return type is a type predicate:\n\n```ts\nfunction isFish(pet: Fish | Bird): pet is Fish {\n return (pet).swim !== undefined;\n}\n```\n\nLet's go back to our React scenario. Let's say our input values are programmatically managed, and could either be of type `value`, or objects with an `IValue` interface depening on some props and prior setup.\n\nLet's assume we have an `onChangeHandler` that listens for any changes made to the input value and returns a data of ***type* value**. But the problem is that the **value *variable*** might not always be of ***type* value**, so we need to check if it's an object of **\"type\" IValue**. Let's define the typings and `onChangeHandler`:\n\n```ts\ntype value = string | number | string[];\n\ninterface IValue {\n value: value;\n [propName: string]: any;\n}\n\n/**\n * `ISelectValue` interface type checker.\n */\nconst instanceOfIValue = (object: any): object is ISelectValue => {\n if (object && object.inputData) {\n return 'inputData' in object; // true\n }\n return false;\n}\n\nconst onChangeHandler = (inputData: value | IValue): value => {\n // Checking for IValue object.\n if (instanceOfIValue(inputData)) {\n return inputData.value;\n }\n // Otherwise, simply return the inputData which will be of type value.\n return inputData;\n}\n```\n\nThe above code snippet might seem overkill, but keep in mind that `instanceOfIValue` can be used in multiple places, and it's scallable to a certain extent. Further properties might be added, and guarded for. If there are too many properties to guard though, using **distriminators** to check for instances might be better.\n\n2. Use **discriminators** to check for instances of specific objects.\n\nIf you find yourself needing to check for many object properties to determine if an object matches your interface, you can use a **discriminator**. Here is a basic example, based on the example above except now we add a new property to `IValue` declared `displayValue`, and `value` might be `undefined`:\n\n```ts\ntype value = string | number | string[];\n\ninterface IValue {\n value?: value;\n displayValue: string;\n discriminator: 'IValue';\n [propName: string]: any;\n}\n\n/**\n * `ISelectValue` interface type checker.\n */\nconst instanceOfIValue = (object: any): object is ISelectValue => {\n if (object && object.discriminator) {\n return object.discriminator === 'IValue';\n }\n return false;\n}\n\nconst onChangeHandler = (inputData: value | IValue): value => {\n // Checking for IValue object.\n if (instanceOfIValue(inputData)) {\n return inputData.value || inputData.displayValue;\n }\n // Otherwise, simply return the inputData which will be of type value.\n return inputData;\n}\n```\n\nBy using a **discriminator**, we don't have to check if the `value` or `displayValue` properties exist, instead we check for a `discriminator` property which will let us know if the object is of \"type\" `IValue`.\n\n[⬆️ Back to top](#table-of-contents)
\n\n---\n\n## Generics\n\n> A major part of software engineering is building components that not only have well-defined and consistent APIs, but are also reusable. Components that are capable of working on the data of today as well as the data of tomorrow will give you the most flexible capabilities for building up large software systems.\n> In languages like `C#` and `Java`, one of the main tools in the toolbox for creating reusable components is generics, that is, being able to create a component that can work over a variety of types rather than a single one. This allows users to consume these components and use their own types.\n\nGenerics are a way to help us write dynamic, flexible code. To explain what generics are, let's take a look at the example below:\n\n```ts\n function echo(data: string) {\n return data;\n }\n```\n\nThat is **not** a generic function. Generics are reusable components (be it a `function`, an `object`, a `class`) that can be used multiple times for multiple variable types, like a `string`, a `number`, `boolean`, etc. Next we'll see how to properly write Generics in TypeScript and different types of Generics.\n\n[⬆️ Back to top](#table-of-contents)
\n\n### Simple Generics\n\nTo write a generic, let's take the example above and turn it into a simple generic function. Here's how the original `echo` function would be rewritten into a simple generic:\n\n```ts\n function echo(data: any) {\n return data;\n }\n\n console.log(echo('Robert')); // Prints: \"Robert\"\n console.log(echo(24).length); // Prints: undefined.\n console.log(echo({ name: 'Robert', age: 24 })); // Prints: { name: \"Robert\", age: 24 }\n```\n\nOur function is now a generic function, it can accept `any` type of argument and returns a value of equal type. But this function is not really practical for two reasons:\n\n1. The compiler doesn't know what the **type** of the return value will be.\n2. Because of the above, the IDE won't know what value **type** will be returned from the function, resulting in higher difficulty to access its properties, we would have to be sure beforehand. The program could also crash if an undefined variable is used later on.\n\nNote how in the example above, we can't access the `length` property of a string, because `echo` returns a `number`, but neither the compiler nor the IDE knows this **because we are losing information about said type**. We can fortunately fix this however, by using **better generics**.\n\n[⬆️ Back to top](#table-of-contents)
\n\n### Better Generics\n\nWhile the example above is technically a generic function, let's now improve it by making use of TypeScript so that we can know what type will be returned from `echo`. Here is an example:\n\n```ts\n function betterEcho(data: T) {\n return data;\n }\n```\n\n> We’ve now added a type variable `T` to the function. This `T` allows us to capture the type the user provides (e.g. `number`), so that we can use that information later. Here, we use `T` again as the return type.\n\nBy using the angle brackets right beside `betterEcho`, it's a way of saying to the compiler and the IDE the following: *\"hey, `betterEcho` must return a value **type** equal to the **type** of the `data` argument\"*. Here is an example of using `betterEcho` and how the compiler would behave:\n\n```ts\n // Now I get types in the IDE, the compiler also knows what type is returned from betterEcho.\n console.log(betterEcho('Robert').length);\n// console.log(betterEcho(24).length); // Compiler & IDE warning: Property 'length' does not exist on type `number`.\n console.log(betterEcho(24).toExponential(2)); // I would get IDE support.\n console.log(betterEcho({ name: 'Robert', age: 24 }).age); // I would also get IDE support.\n```\n\nThis way both the compiler **and** the IDE will know what type will be returned from `betterEcho`. **The compiler won't even run if `noEmitOnError` is true inside the `tsconfig.json` file. [More info about this here](https://www.typescriptlang.org/docs/handbook/compiler-options.html).**\n\n[⬆️ Back to top](#table-of-contents)
\n\n### Built-in Generics\n\nSome types though, have *built-in generics*. I've even used some as examples before without having touched yet on generics. Here are some very simple examples:\n\n```ts\n const testResults: Array = [1.94, 2.33];\n testResults.push(-2.99);\n // testResults.push('string'); // Not possible.\n\n // Arrays\n function printAll(args: T[]) {\n args.forEach(element => console.log(element));\n }\n\n printAll(['Apple', 'Banana']);\n printAll([1, 3, 5]);\n // printAll([1, 'string', 5]); // Not possible.\n```\n\nArrays have built-in generics. The constant `testResults` is an array that will accept any value that is of type `number`. The function `printAll` will also accept any `array` of `T` type as an argument, turning `printAll` into a generic function.\n\n[⬆️ Back to top](#table-of-contents)
\n\n### Generic Types\n\nThe `Generic Types` are **exactly** what the name says. It's a way of declaring any kind of variable with a generic type. Let's see how to create generic functions and generic interfaces. For example:\n\n**Functions:**\n\n```ts\n function betterEcho(data: T) {\n return data;\n }\n\n const echoStr: (data: T) => T = betterEcho;\n console.log(echoStr('Hello world!').toLocaleUpperCase()); // Prints: \"HELLO WORLD!\"\n\n const echoNum: (data: T) => T = betterEcho;\n console.log(echoNum(312).toString()); // Prints: \"312\"\n```\n\n> The type of generic functions is just like those of non-generic functions, with the type parameters listed first, similarly to function declarations.\n\n**Interfaces:**\n\nHere is an example as shown in the [TypeScript official documentation about generic types](https://www.typescriptlang.org/docs/handbook/generics.html):\n\n```ts\n interface GenericIdentityFn {\n (arg: T): T;\n }\n\n function identity(arg: T): T {\n return arg;\n }\n\n let myIdentity: GenericIdentityFn = identity;\n```\n\n> Notice that our example has changed to be something slightly different. Instead of describing a generic function, we now have a non-generic function signature that is a part of a generic type. When we use `GenericIdentityFn`, we now will also need to specify the corresponding type argument (here: number), effectively locking in what the underlying call signature will use. Understanding when to put the type parameter directly on the call signature and when to put it on the interface itself will be helpful in describing what aspects of a type are generic.\n\n[⬆️ Back to top](#table-of-contents)
\n\n### Generic Class\n\n> A generic class has a similar shape to a generic interface. Generic classes have a generic type parameter list in angle brackets (<>) following the name of the class.\n\nHere is a simple example for starters using **`constraints`**. The generic types may be constrained by **extending** them to certain types. For example, we may **constrain** (with the `extends` keyword) a type `T` to type `number`, or type `string` **only**. Here is an example:\n\n```ts\n class GenericMath {\n constructor(public baseValue: T, public multiplyValue: T) {}\n calculate(): number {\n return +this.baseValue * +this.multiplyValue\n }\n }\n\n // const genericMath = new GenericMath(10, 'string'); // Not possible because 'string' can't be converted to a number.\n // const genericMathBoolean = new GenericMath(10, '20'); // Not possible because T only extends to number and string only.\n const onlyNumbers = new GenericMath(10, 20);\n const onlyStrings = new GenericMath('10', '20');\n const bothTypes = new GenericMath(10, '20');\n\n console.log(onlyNumbers.calculate()); // Prints: 200\n console.log(onlyStrings.calculate()); // Prints: 200\n console.log(bothTypes.calculate()); // Prints: 200\n```\n\nNotice how the `T` type is defined. **`T` extends to the `number` type, `or` to the `string` type only**. This means that everything referring to `T` inside the generic class `GenericMath`, can be of type `number` or type `string` (e.g. the constructor arguments), depending on how its set when calling the constructor.\n\nHere is a more advanced example where we use **multiple types** when defining the generic class types:\n\n```ts\n class MultipleTypesMath {\n constructor(public baseValue: T, public multiplyValue: U) {}\n calculate(): number {\n return +this.baseValue * +this.multiplyValue;\n }\n }\n\n const multipleTypesMath = new MultipleTypesMath(5, '20');\n\n console.log(multipleTypesMath.calculate()); // Prints: 100\n```\n\nNow we have **two** types, `T` and `U` and both are constrained, but to different types. The constructor argument `baseValue` is defined as type `T`, and `T` only extends to type `number`, which results in `baseValue` only accepting values of type `number` when calling the constructor.\n\n`U` is a union type. It accepts values of type `number` or `string`, and it's assigned to the `multiplyValue` argument of the constructor.\n\n[⬆️ Back to top](#table-of-contents)
\n\n---\n\n## Decorators\n\n### Prologue\n\nBefore getting into what decorators are, here is some important information taken from the [TypeScript official documentation about decorators](https://www.typescriptlang.org/docs/handbook/decorators.html):\n\n> With the introduction of Classes in TypeScript and ES6, there now exist certain scenarios that require additional features to support annotating or modifying classes and class members. Decorators provide a way to add both annotations and a meta-programming syntax for class declarations and members. Decorators are a stage 2 proposal for JavaScript and are available as an experimental feature of TypeScript.\n> **NOTE:  Decorators are an experimental feature that may change in future releases.**\n> To enable experimental support for decorators, you must enable the `experimentalDecorators` compiler option either on the command line or in your `tsconfig.json`:\n\n***Command Line:***\n\n```cli\ntsc --target ES5 --experimentalDecorators\n```\n\n***tsconfig.json:***\n\n```json\n{\n \"compilerOptions\": {\n \"target\": \"ES5\",\n \"experimentalDecorators\": true\n }\n}\n```\n\n[⬆️ Back to top](#table-of-contents)
\n\n\n\n### Decorators Introduction\n\n> A Decorator is a special kind of declaration that can be attached to a class declaration, method, accessor, property, or parameter. Decorators use the form `@expression`, where **`expression`** must evaluate to a function that will be called at runtime with information about the decorated declaration.\n\n[⬆️ Back to top](#table-of-contents)
\n\n### Basic Decorator\n\nAnother way to explain a decorator would be to say that they are a way to enhance the functionality of a `class`. We can use decorators to add properties to a decorated class when its constructor is executed, for example. This is of course among many other things that are possible, more of this below.\n\n```ts\n function logged(constructorFn: Function) {\n console.log(constructorFn); // Will log the class Person constructor.\n }\n\n @logged\n class Person {\n constructor() {\n console.log('Hi!');\n }\n }\n```\n\nEvery time a new instance of `Person` is built, the **constructor function** will be logged into the console.\n\n[⬆️ Back to top](#table-of-contents)
\n\n### Factory\n\n> If we want to customize how a decorator is applied to a declaration, we can write a decorator factory. A Decorator Factory is simply a function that returns the expression that will be called by the decorator at runtime.\n\nIn other words, a factory is a functions that returns a decorator.\n\n```ts\n function logged(constructorFn: Function) {\n console.log(constructorFn); // Will log the class Person constructor.\n }\n\n // Factory\n function logging(value: boolean) {\n return value ? logged : () => {};\n }\n\n @logging(true) // If true, it will return the `logged` decorator, if false, it will execute an empty function.\n class Car {\n\n }\n```\n\n[⬆️ Back to top](#table-of-contents)
\n\n### Advanced Decorator\n\nSince decorators have access to a class constructor function, through the decorators we can access the class `prototype` and add properties to said class. In the example below we will add the `print` method to any `class` decorated by `printable` (to `Plant` in this case). Also notice in the next example how the argument passed to `logging` (same `factory` function as above) is `false`. This means the constructor won't be printed to the console through the `logged` decorator **(because the `factory` won't return it)**.\n\n```ts\n function printable(constructorFn: Function) {\n constructorFn.prototype.print = function() {\n console.log(this);\n this.name = 'Red Plant'\n }\n }\n\n @logging(false)\n @printable\n class Plant {\n private name = 'Green Plant';\n }\n\n const plant = new Plant();\n (plant).print(); // Would print the constructed Plant object: { name: \"Green Plant\" }, then change the name property.\n console.log('After print() is executed', plant); // Prints: the constructed Plant object: { name: \"Red Plant\" }.\n```\n\n[⬆️ Back to top](#table-of-contents)
\n\n### Method Decorator\n\n> A Method Decorator is declared just before a method declaration. The decorator is applied to the Property Descriptor for the method, and can be used to observe, modify, or replace a method definition. A method decorator cannot be used in a declaration file, on an overload, or in any other ambient context (such as in a declare class).\n\nBesides adding a decorator to the class, we can also add decorators to a class member method (and properties, parameters - more on this below). It is done in the exact same way.\n\n```ts\n function editable(value: boolean) {\n return function(target:any, propName: string, descriptor: PropertyDescriptor = {}) {\n descriptor.writable = value;\n Object.defineProperty(target, propName, descriptor);\n }\n }\n \n class Project {\n constructor(name: string) {\n this.projectName = name;\n }\n\n @editable(false)\n calcBudget() {\n console.log(1000);\n }\n }\n \n const project = new Project('Super Project');\n project.calcBudget(); // Prints: 1000\n // project.calcBudget = () => { // Throws warning in the console IF editable is false. Property won't ever be changed.\n // console.log(2000);\n // };\n```\n\nIn the above example, we are modifying the `calcBudget` object's writable property to avoid it from being changed in the future. If changed then a warning will be thrown in the console. [More information about PropertyDescriptors can be found here](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Object/defineProperty).\n\n[⬆️ Back to top](#table-of-contents)
\n\n### Property Decorator\n\nThe good thing about decorators is that they are exactly as their name imply. With the property decorator, we can also change its property during runtime, among other things. Here's what the official documentation have to say about **property decorators**:\n\n> A Property Decorator is declared just before a property declaration. A property decorator cannot be used in a declaration file, or in any other ambient context (such as in a declare class).\n\nThe expression for the property decorator will be called as a function at runtime, with the following two arguments:\n\n1. Either the constructor function of the class for a static member, or the prototype of the class for an instance member.\n2. The name of the member.\n\n> **NOTE  A Property Descriptor is not provided as an argument to a property decorator due to how property decorators are initialized in TypeScript. This is because there is currently no mechanism to describe an instance property when defining members of a prototype, and no way to observe or modify the initializer for a property. The return value is ignored too. As such, a property decorator can only be used to observe that a property of a specific name has been declared for a class.**\n\n**That is not to say that a property's PropertyDescriptor object cannot be modified.** We may define a new object descriptor and return it, here's an example of how to make a property with `writable` false:\n\n```ts\n // Method Decorator\n function editable(value: boolean) {\n return function(target:any, propName: string, descriptor: PropertyDescriptor = {}) {\n descriptor.writable = value;\n Object.defineProperty(target, propName, descriptor);\n }\n }\n\n // Property Decorator\n function overwritable(value: boolean): any {\n return function(target: any, propName: string): any {\n const newDescriptor: PropertyDescriptor = { // New PropertyDescriptor object.\n writable: value\n };\n return newDescriptor;\n }\n }\n\n class Project {\n @overwritable(false)\n private projectName: string;\n\n constructor(name: string) {\n this.projectName = name;\n }\n\n @editable(true)\n calcBudget() {\n console.log(1000);\n }\n }\n \n const project = new Project('Super Project'); // The name won't be modified because the @overwritable decorator's argument is false.\n project.calcBudget(); // Prints: 1000\n project.calcBudget = () => { // Throws error in console IF the @editable decorator's argument is false, cannot assign new property to this.\n console.log(2000);\n };\n project.calcBudget(); // Prints: 2000\n console.log(project) // IF overwritable is false, then projectName will be equal to undefined.\n```\n\n[⬆️ Back to top](#table-of-contents)
\n\n### Parameter Decorator\n\nLet's quote the official documentation again:\n\n> A Parameter Decorator is declared just before a parameter declaration. The parameter decorator is applied to the function for a class constructor or method declaration. A parameter decorator cannot be used in a declaration file, an overload, or in any other ambient context (such as in a declare class).\n\nThe expression for the parameter decorator will be called as a function at runtime, with the following three arguments:\n\nEither the constructor function of the class for a static member, or the prototype of the class for an instance member.\n\n1. The name of the member.\n2. The ordinal index of the parameter in the function’s parameter list.\n\n> **NOTE  A parameter decorator can only be used to observe that a parameter has been declared on a method.**\n\nHere is an example of how we can put that into practice:\n\n```ts\n function printInfo(target: any, methodName: string, paramIndex: number) {\n console.log('Target: ', target);\n console.log('Method name: ', methodName);\n console.log('Param index: ', paramIndex);\n }\n\n class Course {\n name: string;\n\n constructor(name: string) {\n this.name = name;\n }\n\n // @printInfo behind printAll\n printStudentNumbers(mode: string, @printInfo printAll: boolean) {\n if (printAll) {\n console.log(10000);\n } else {\n console.log(2000);\n }\n }\n }\n\n const course = new Course('TypeScript');\n course.printStudentNumbers('anything', true);\n course.printStudentNumbers('anything', false);\n\n```\n\nSo, `@printInfo` will only be called at runtime whenever `printStudentNumbers` is executed. Here is what `@printInfo` would print for both instances:\n\n```js\n Target: > Object {}\n methodName: printStudentNumbers\n paramIndex: 1 // Starts counting at 0. It's 1 because `printAll` is the second argument of `printStudentNumbers`.\n```\n\n[⬆️ Back to top](#table-of-contents)
\n\n---\n\n\n\n## Webpack Workflow (v^4.X)\n\n[Here is the official documentation about TypeScript made by the folks at Webpack](https://webpack.js.org/guides/typescript/).\n\nHere's the TLDR:\n\n1. Install the TypeScript compiler with `npm install --save-dev typescript ts-loader`.\n\n> We use ts-loader in this guide as it makes enabling additional webpack features, such as importing other web assets, a bit easier.\n\n2. Run `tsc --init`, then modify the `tsconfig.json` file to look like this or similar:\n\n```ts\n {\n \"compilerOptions\": {\n \"outDir\": \"./dist/\",\n \"noImplicitAny\": true,\n \"module\": \"es6\",\n \"target\": \"es5\",\n \"jsx\": \"react\",\n \"allowJs\": true\n }\n }\n```\n\nThis will make sure the compiler can parse TypeScript down to ES5.\n\n3. Webpack `webpack.config.js` file configuration to handle TypeScript:\n\n```js\n const path = require('path');\n\n module.exports = {\n entry:\"./src/app.ts\",\n output: {\n filename: 'bundle.js',\n path: path.resolve(__dirname, 'dist')\n },\n devtool:\"source-map\",\n resolve: {\n extensions: [\"*\",\".ts\",\".tsx\",\".js\"]\n },\n module:{\n rules: [\n { test:/\\.tsx?$/, loader:\"ts-loader\" }\n ]\n }\n };\n```\n\n> This will direct webpack to enter through ./index.ts, load all .ts and .tsx files through the ts-loader, and output a bundle.js file in our current directory.\n\n[⬆️ Back to top](#table-of-contents)
\n\n---\n\n## TypeScript with React.js\n\n[Here is the official documentation about using TypeScript along with React.js](https://www.typescriptlang.org/docs/handbook/react-&-webpack.html).\n\nIn this section though, I'll focus on how to use TypeScript while using `create-react-app`, and how to set general Prop types like `className`, `ref`, `style`, among others.\n\n[⬆️ Back to top](#table-of-contents)
\n\n### TypeScript while using `create-react-app`\n\n1. Create the React.js application by running `npx create-react-app project-name --scripts-version=react-scripts-ts`.\n\nThis will now also create the react app with all the configuration already set up and ready to go.\n\n[Here is an example React.js app created by running the previous command.](https://github.com/rmolinamir/typescript-cheatsheet/tree/master/lib/react.js)\n\nWhen using an interface to declare the PropTypes, by convention we must write a capital I when defining an interface. Let's see an example about how we can use PropTypes together with interfaces, enums, and how to apply them:\n\n***App.tsx***\n\n```tsx\n // ...\n\n interface IAppProps {[propName]: any}\n interface IAppState {\n counterValue: number;\n }\n\n class App extends React.Component {\n public state = { counterValue: 0 }; // State is required to be public.\n\n public render() {\n return (\n
\n

\n \n

\n
\n );\n }\n\n private counterHandler = (mode: ECounterHandlers) => {\n this.setState(prevState => {\n switch(mode) {\n case ECounterHandlers.Dec:\n return { counterValue: prevState.counterValue - 1 }\n case ECounterHandlers.Inc:\n return { counterValue: prevState.counterValue + 1 }\n }\n });\n }\n }\n\n export default App;\n```\n\n***CounterOutput.tsx***\n\n```tsx\n export enum ECounterHandlers {\n Inc,\n Dec\n }\n\n interface ICounterOutputProps {\n counter: number;\n onClick: (mode: ECounterHandlers) => void;\n }\n const counterOutput = (props: ICounterOutputProps) => {\n return (\n
\n

{props.counter}

\n \n \n
\n );\n }\n```\n\nNotice the following:\n\n1. There's a capital E when defining the enum. That is a personal thing I picked up while using the Unreal Engine software that is based in `c++` which is similar in many ways to TypeScript, and it also makes sense since it's inline with the interface naming convention.\n2. Passing variables, defining and declaring components and everything else is exactly as you'd expect - with the extra features of TypeScript added on top of it.\n3. Remember that class members are `public` by default while using TypeScript, **but, a the state of a class component must always be public**.\n\nIt is also possible to modify the `tsconfig.json` or the `tslint.json` to handle highlights in your IDE:\n\n```json\n {\n \"extends\": [\"tslint:recommended\", \"tslint-react\", \"tslint-config-prettier\"],\n \"linterOptions\": {\n \"exclude\": [\n \"config/**/*.js\",\n \"node_modules/**/*.ts\",\n \"coverage/lcov-report/*.js\"\n ]\n },\n \"rules\": {\n \"jsx-no-lambda\": false\n }\n }\n```\n\nTypeScript by default doesn't like when lambda functions are used and the IDE will hightlight them as an error. To disable this, add the following rule into your `tslint.json` file: `\"jsx-no-lambda\": false`.\n\n[⬆️ Back to top](#table-of-contents)
\n\n\n\n## Setting up general React prop types\n\nLike shown above, prop types are very encouraged in React.js. Some prop types however, may not be as obvious as others, so here's a small list of the most common prop types you might end up defining while programming your application:\n\n[⬆️ Back to top](#table-of-contents)
\n\n\n\n### References (`React.createRef()` or `useRef` API)\n\nPassing down references is a bit tricky, because it **depends on which HTML element type you are referring to**.\n\nReact provides the reference objects types out of the box, imported from the **React** default export, from the `react` package (or as a named export as well). The name of the object type is `RefObject`, and it is essentially a [generic interface](#generic-types). Here's the definition:\n\n```ts\ninterface React.RefObject\n```\n\nAs I said earlier, the **RefObject** must be accompanied by a **generic type declaration** (remember the angle brackets typing in the Generics section? e.g. ``), that specifies **which type of HTML element tag the reference belongs to**.\n\nThe methodology is the same for every HTML element, but the generic type is respective to which HTML element we are referencing. Here's an example on how to apply the prop type to an SVG and to a button:\n\n***SVG Element:***\n\n```tsx\n interface ISVGProps {\n reference?: React.RefObject\n ...\n }\n\n const SVG = (props: ISVGProps) => {\n return (\n \n \n \n )\n }\n\n // ...\n\n \n```\n\n***Button Element:***\n\n```tsx\n interface IButtonProps {\n reference?: React.RefObject\n }\n\n const button = (props: IButtonProps) => {\n return (\n \n )\n }\n\n // ...\n\n \n```\n\n[⬆️ Back to top](#table-of-contents)
\n\n\n\n### CSS Properties and Classes Prop Types\n\nNow, let's say we want to pass CSS classes and CSS style properties as React props down to our button component. We know that CSS classes, classNames in react, are of type `string`. However, CSS style properties are made of an entirely different type definition.\n\nThe type definition can be imported from the **React default export object** just like the previous type declarations. It's called **`CSSProperties`**, and it is defined as:\n\n```ts\n interface CSSProperties extends CSS.Properties\n```\n\nHere's how we can pass CSS classes and style properties down to our component:\n\n```tsx\n interface IButtonProps {\n reference?: React.RefObject\n children?: React.ReactNode\n // CSS\n style?: React.CSSProperties\n className?: string\n }\n\n const button = (props: IButtonProps) => {\n return (\n \n {props.children}\n \n )\n }\n\n // ...\n\n \n {props.children}\n \n```\n\n[⬆️ Back to top](#table-of-contents)
\n\n\n\n### General Prop Types (e.g. HTML 5 props, event listeners etc.)\n\nLet's finish our button. We'll now create an interface declaring most of the possible HTML-5 properties a button can accept, such as mouse in and out and click event listeners, CSS classes, CSS styles, form properties (form, formaction, formenctype, etc.), value, among others.\n\n***Button.tsx***\n\n```tsx\n interface IButtonProps {\n reference?: React.RefObject\n children?: React.ReactNode\n data: IButtonData\n }\n\n interface IButtonData {\n // HTML Props\n type?: string\n name?: string\n value?: string | number | string[] | undefined\n onClick?: ((event: React.MouseEvent) => void) | undefined\n onMouseEnter?: ((event: React.MouseEvent) => void)\n onMouseLeave?: ((event: React.MouseEvent) => void)\n tabIndex?: number | undefined\n disabled?: boolean\n // HTML 5 Props\n autoFocus?: boolean\n form?: string\n formAction?: string\n formEncType?: string\n formMethod?: string\n formNoValidate?: boolean\n formTarget?: string\n // CSS\n style?: React.CSSProperties\n className?: string\n }\n\n const data: IButtonData = {...props.data}\n\n export const button = (props: IButtonProps) => {\n return (\n \n )\n }\n```\n\nLet's now call our button component inside an **App.js** file then pass some props:\n\n***App.js***\n\n```js\n /// ...\n\n \n```\n\nFor more information about button HTML properties, [give this a read](https://developer.mozilla.org/en-US/docs/Web/HTML/Element/button).\n\nThe methodology is essentially the same for other components. When in doubt about other HTML properties for any kind of element, you can always consult the [MDN docs](https://developer.mozilla.org/en-US/docs/Web/).\n\n[⬆️ Back to top](#table-of-contents)
\n\n\n\n## Setting up React References (React.createRef() or useRef API)\n\nSetting up a React reference is similar to how we pass down references as props. Let's set up the reference using the `useRef` hook and the old `React.createRef()`.\n\n[⬆️ Back to top](#table-of-contents)
\n\nLet's start with the `useRef` API first:\n\n\n\n### TypeScript & useRef\n\nThe general idea is to define the reference with a generic HTML type while initializing them as `null`. This is to let TypeScript know what kind of variable will be stored. Here's an example using an input element:\n\n```tsx\n const input = () => {\n // Initialize the reference as null while letting TypeScript know it's an input element.\n const myInput = useRef(null)\n\n return (\n \n );\n }\n```\n\n[⬆️ Back to top](#table-of-contents)
\n\n\n\n### TypeScript & React.createRef\n\nSimilarly to how we just created a reference using the `useRef` API, let's now use the `React.createRef` API to set up a reference by initializing it as null and declaring the HTML type:\n\n```tsx\n /// ...\n\n class Input extends Component {\n constructor() {\n super()\n // Initialize the reference as null while letting TypeScript know it's an input element.\n this.myInput = React.createRef(null)\n }\n\n render() {\n return (\n \n )\n }\n }\n```\n\n[⬆️ Back to top](#table-of-contents)
\n\n\n\n## Setting up Event Handlers\n\n> There are many types of events, some of which use other interfaces based on the main Event interface. Event itself contains the properties and methods which are common to all events.\n\nThere are many types of events. Because of this, when accessing an event, we must let TypeScript know what kind of event we are referring to. [Here is a list of all the possible events according to the official MDN docs](https://developer.mozilla.org/en-US/docs/Web/API/Event#Introduction).\n\nLet's create two event handlers, a `MouseEvent` handler that will prevent the default event, trigger an alert displaying our value, then reset our input's value on upon clicking a button, and finally, an `InputEvent` handler that will listen to our input element value changes. 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"language": "PowerShell", + "size": 159 + } + ], + "last_commit_date_default_branch": "2022-05-25T15:34:47Z", + "last_commit_oid_default_branch": "0a8c922abf0b5919d4b6df83b08de133f164eb4d", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "MIT License", + "license_spdx": "MIT", + "name_with_owner": "hassan11196/uptime-kuma", + "owner": "hassan11196", + "parent_repo": "louislam/uptime-kuma", + "parent_url": "https://github.com/louislam/uptime-kuma", + "primary_language": "JavaScript", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2022-05-26T04:17:33Z", + "readme_length": 5958, + "readme_text": "# Uptime Kuma\n\n \n[![GitHub Sponsors](https://img.shields.io/github/sponsors/louislam?label=GitHub%20Sponsors)](https://github.com/sponsors/louislam)\n\n
\n \"\"\n
\n\nIt is a self-hosted monitoring tool like \"Uptime Robot\".\n\n\"\"\n\n## 🥔 Live Demo\n\nTry it!\n\nhttps://demo.uptime.kuma.pet\n\nIt is a temporary live demo, all data will be deleted after 10 minutes. The server is located in Tokyo, so if you live far from there, it may affect your experience. I suggest that you should install and try it out for the best demo experience.\n\nVPS is sponsored by Uptime Kuma sponsors on [Open Collective](https://opencollective.com/uptime-kuma)! Thank you so much!\n\n## ⭐ Features\n\n* Monitoring uptime for HTTP(s) / TCP / HTTP(s) Keyword / Ping / DNS Record / Push / Steam Game Server.\n* Fancy, Reactive, Fast UI/UX.\n* Notifications via Telegram, Discord, Gotify, Slack, Pushover, Email (SMTP), and [90+ notification services, click here for the full list](https://github.com/louislam/uptime-kuma/tree/master/src/components/notifications).\n* 20 second intervals.\n* [Multi Languages](https://github.com/louislam/uptime-kuma/tree/master/src/languages)\n* Multiple Status Pages\n* Map Status Page to Domain\n* Ping Chart\n* Certificate Info\n* Proxy Support\n* 2FA available\n\n## 🔧 How to Install\n\n### 🐳 Docker\n\n```bash\ndocker run -d --restart=always -p 3001:3001 -v uptime-kuma:/app/data --name uptime-kuma louislam/uptime-kuma:1\n```\n\n⚠️ Please use a **local volume** only. Other types such as NFS are not supported.\n\nBrowse to http://localhost:3001 after starting.\n\n### 💪🏻 Non-Docker\n\nRequired Tools: \n- [Node.js](https://nodejs.org/en/download/) >= 14\n- [Git](https://git-scm.com/downloads) \n- [pm2](https://pm2.keymetrics.io/) - For run in background\n\n```bash\n# Update your npm to the latest version\nnpm install npm -g\n\ngit clone https://github.com/louislam/uptime-kuma.git\ncd uptime-kuma\nnpm run setup\n\n# Option 1. Try it\nnode server/server.js\n\n# (Recommended) Option 2. Run in background using PM2\n# Install PM2 if you don't have it: \nnpm install pm2 -g && pm2 install pm2-logrotate\n\n# Start Server\npm2 start server/server.js --name uptime-kuma\n\n\n```\nBrowse to http://localhost:3001 after starting.\n\nMore useful PM2 Commands\n\n```bash\n# If you want to see the current console output\npm2 monit\n\n# If you want to add it to startup\npm2 save && pm2 startup\n```\n\n### Advanced Installation\n\nIf you need more options or need to browse via a reverse proxy, please read:\n\nhttps://github.com/louislam/uptime-kuma/wiki/%F0%9F%94%A7-How-to-Install\n\n## 🆙 How to Update\n\nPlease read:\n\nhttps://github.com/louislam/uptime-kuma/wiki/%F0%9F%86%99-How-to-Update\n\n## 🆕 What's Next?\n\nI will mark requests/issues to the next milestone.\n\nhttps://github.com/louislam/uptime-kuma/milestones\n\nProject Plan:\n\nhttps://github.com/louislam/uptime-kuma/projects/1\n\n## ❤️ Sponsors\n\nThank you so much! (GitHub Sponsors will be updated manually. OpenCollective sponsors will be updated automatically, the list will be cached by GitHub though. It may need some time to be updated)\n\n\n\n## 🖼 More Screenshots\n\nLight Mode:\n\n\"\"\n\nStatus Page:\n\n\"\"\n\nSettings Page:\n\n\"\"\n\nTelegram Notification Sample:\n\n\"\"\n\n## Motivation\n\n* I was looking for a self-hosted monitoring tool like \"Uptime Robot\", but it is hard to find a suitable one. One of the close ones is statping. Unfortunately, it is not stable and no longer maintained.\n* Want to build a fancy UI.\n* Learn Vue 3 and vite.js.\n* Show the power of Bootstrap 5.\n* Try to use WebSocket with SPA instead of REST API.\n* Deploy my first Docker image to Docker Hub.\n\nIf you love this project, please consider giving me a ⭐.\n\n## 🗣️ Discussion\n\n### Issues Page\n\nYou can discuss or ask for help in [issues](https://github.com/louislam/uptime-kuma/issues).\n\n### Subreddit\n\nMy Reddit account: louislamlam\nYou can mention me if you ask a question on Reddit.\nhttps://www.reddit.com/r/UptimeKuma/\n\n## Contribute\n\n### Beta Version\n\nCheck out the latest beta release here: https://github.com/louislam/uptime-kuma/releases\n\n### Bug Reports / Feature Requests\nIf you want to report a bug or request a new feature, feel free to open a [new issue](https://github.com/louislam/uptime-kuma/issues).\n\n### Translations\nIf you want to translate Uptime Kuma into your language, please read: https://github.com/louislam/uptime-kuma/tree/master/src/languages\n\nFeel free to correct my grammar in this README, source code, or wiki, as my mother language is not English and my grammar is not that great.\n\n### Pull Requests\nIf you want to modify Uptime Kuma, this guideline may be useful for you: https://github.com/louislam/uptime-kuma/blob/master/CONTRIBUTING.md", + "readme_title": "Uptime Kuma", + "releases_count": 0, + "repo": "uptime-kuma", + "repo_name": "uptime-kuma", + "stars": 0, + "topics": [], + "updated_at": "2022-05-26T05:02:24Z", + "url": "https://github.com/hassan11196/uptime-kuma", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": "https://github.com/hassan11196/uptime-kuma" + }, + { + "awards": [], + "categories": [], + 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\n\n\n[![PyPI - Package Version](https://img.shields.io/pypi/v/whos-there?logo=pypi&style=flat&color=orange)][#pypi-package]\n[![Conda - Platform](https://img.shields.io/conda/pn/conda-forge/whos-there?logo=anaconda&style=flat)][#conda-forge-package]\n[![Conda (channel only)](https://img.shields.io/conda/vn/conda-forge/whos-there?logo=anaconda&style=flat&color=orange)][#conda-forge-package]\n\n[![Build](https://github.com/twsl/whos-there/actions/workflows/build.yml/badge.svg)][#github-workflows-build]\n[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/whos-there?logo=pypi&style=flat&color=blue)][#pypi-package]\n[![Dependencies Status](https://img.shields.io/badge/dependencies-up%20to%20date-brightgreen.svg)](https://github.com/twsl/whos-there/pulls?utf8=%E2%9C%93&q=is%3Apr%20author%3Aapp%2Fdependabot)\n\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n[![Security: bandit](https://img.shields.io/badge/security-bandit-green.svg)](https://github.com/PyCQA/bandit)\n[![Pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)][#github-pre-commit]\n[![Semantic Versions](https://img.shields.io/badge/%20%20%F0%9F%93%A6%F0%9F%9A%80-semantic--versions-e10079.svg)][#github-releases]\n[![GitHub - License](https://img.shields.io/github/license/twsl/whos-there?logo=github&style=flat&color=green)][#github-license]\n\n[#github-license]: https://github.com/twsl/whos-there/blob/main/LICENSE\n[#pypi-package]: https://pypi.org/project/whos-there/\n[#conda-forge-package]: https://anaconda.org/conda-forge/whos-there\n[#github-pre-commit]: https://github.com/twsl/whos-there/blob/master/.pre-commit-config.yaml\n[#github-releases]: https://github.com/twsl/whos-there/releases\n[#github-workflows-build]: https://github.com/twsl/whos-there/actions/workflows/build.yml\n\n \nThe spiritual successor to [knockknock](https://github.com/huggingface/knockknock) for [PyTorch Lightning](https://github.com/PyTorchLightning/pytorch-lightning), to get a notification when your training is complete or when it crashes during the process with a single callback.\n\n
\n\n## 🚀 Features\n\n- Supports E-Mail, Discord, Slack, Teams, Telegram\n\n## 🎯 Installation\n\nYou can install `whos-there` with `pip` or `poetry` or `conda`.\n\n**with pip**\n\n```bash\npip install -U whos-there\n```\n\n**with poetry**\n\n```bash\npoetry add whos-there\n```\n\n**with conda**\n\n```sh\nconda install -c conda-forge whos-there\n```\n\n## 🤯 How to use it\n\n```python\nfrom whos_there.callback import NotificationCallback\nfrom whos_there.senders.debug import DebugSender\n\ntrainer = pl.Trainer(\n callbacks=[\n NotificationCallback(senders=[\n # Add your senders here\n DebugSender(),\n ])\n ]\n)\n```\n\n### E-Mail\nRequires your e-mail provider specific SMTP settings.\n\n```python\nfrom whos_there.senders.email import EmailSender\n# ...\nEmailSender(\n host=\"smtp.example.de\",\n port=587,\n sender_email=\"from@example.com\",\n password=\"*********\",\n recipient_emails=[\n \"to1@example.com\",\n \"to2@example.com\",\n ]\n)\n```\n\n### Discord\nRequires your Discord channel's [webhook URL](https://support.discordapp.com/hc/en-us/articles/228383668-Intro-to-Webhooks).\n\n```python\nfrom whos_there.senders.discord import DiscordSender\n# ...\nDiscordSender(\n webhook_url=\"https://discord.com/api/webhooks/XXXXXXXXXXXXXX/XXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\"\n)\n```\n\n### Slack\nRequires your Slack room [webhook URL](https://api.slack.com/incoming-webhooks#create_a_webhook) and optionally your [user id](https://api.slack.com/methods/users.identity) (if you want to tag yourself or someone else).\n\n```python\nfrom whos_there.senders.slack import SlackSender\n# ...\nSlackSender(\n webhook_url=\"https://hooks.slack.com/services/T00000000/B00000000/XXXXXXXXXXXXXXXXXXXXXXXX\",\n channel=\"channel_name\",\n user_mentions=[\n \"XXXXXXXX\"\n ]\n)\n```\n\n### Teams\nRequires your Team Channel [webhook URL](https://docs.microsoft.com/en-us/microsoftteams/platform/concepts/connectors/connectors-using).\n\n```python\nfrom whos_there.senders.teams import TeamsSender\n# ...\nTeamsSender(\n webhook_url=\"https://XXXXX.webhook.office.com/\",\n user_mentions=[\n \"twsl\"\n ]\n)\n```\n\n### Telegram\nYou can also use Telegram Messenger to get notifications. You'll first have to create your own notification bot by following the three steps provided by Telegram [here](https://core.telegram.org/bots#6-botfather) and save your API access `TOKEN`.\nTelegram bots are shy and can't send the first message so you'll have to do the first step. By sending the first message, you'll be able to get the `chat_id` required (identification of your messaging room) by visiting `https://api.telegram.org/bot/getUpdates` and get the `int` under the key `message['chat']['id']`.\n\n```python\nfrom whos_there.senders.telegram import TelegramSender\n# ...\nTelegramSender(\n chat_id=1234567890,\n token=\"XXXXXXX:XXXXXXXXXXXXXXXXXXXXXXXXXXX\"\n)\n```\n\n## 🛡 License\n\n[![License](https://img.shields.io/github/license/twsl/whos-there)](https://github.com/twsl/whos-there/blob/master/LICENSE)\n\nThis project is licensed under the terms of the MIT license. See [LICENSE](https://github.com/twsl/whos-there/blob/master/LICENSE) for more details.\n\n## 🏅 Credits\n\nThis project was generated with [![🚀 Your next Python package needs a bleeding-edge project structure.](https://img.shields.io/badge/python--package--template-%F0%9F%9A%80-brightgreen)](https://github.com/TezRomacH/python-package-template)\n\nBig thanks to [knockknock](https://github.com/huggingface/knockknock) for the idea and code snippets.", + "readme_title": "Who's there?", + "releases_count": 0, + "repo": "whos-there", + "repo_name": "whos-there", + "stars": 0, + "topics": [], + "updated_at": "2022-11-02T08:45:59Z", + "url": "https://github.com/hassan11196/whos-there", + "watchers": 0 + } + ], + "hackathon_location": null, + "hackathon_name": null, + "hackathon_year": null, + "id": null, + "image_url": null, + "project_fk": null, + "project_id": null, + "project_title": null, + "project_uid": null, + "tags": [], + "team": [], + "title": null, + "url": 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\n\n English | [简体中文](.github/README_cn.md)\n
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\n YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics\n open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.\n

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Documentation
\n\nSee the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment.\n\n##
Quick Start Examples
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\nInstall\n\nClone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a\n[**Python>=3.7.0**](https://www.python.org/) environment, including\n[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).\n\n```bash\ngit clone https://github.com/ultralytics/yolov5 # clone\ncd yolov5\npip install -r requirements.txt # install\n```\n\n
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\nInference\n\nYOLOv5 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest\nYOLOv5 [release](https://github.com/ultralytics/yolov5/releases).\n\n```python\nimport torch\n\n# Model\nmodel = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5n - yolov5x6, custom\n\n# Images\nimg = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list\n\n# Inference\nresults = model(img)\n\n# Results\nresults.print() # or .show(), .save(), .crop(), .pandas(), etc.\n```\n\n
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\nInference with detect.py\n\n`detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from\nthe latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.\n\n```bash\npython detect.py --source 0 # webcam\n img.jpg # image\n vid.mp4 # video\n path/ # directory\n 'path/*.jpg' # glob\n 'https://youtu.be/Zgi9g1ksQHc' # YouTube\n 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n```\n\n
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\nTraining\n\nThe commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh)\nresults. [Models](https://github.com/ultralytics/yolov5/tree/master/models)\nand [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest\nYOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are\n1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://github.com/ultralytics/yolov5/issues/475) times faster). Use the\nlargest `--batch-size` possible, or pass `--batch-size -1` for\nYOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB.\n\n```bash\npython train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 128\n yolov5s 64\n yolov5m 40\n yolov5l 24\n yolov5x 16\n```\n\n\n\n
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\nTutorials\n\n- [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)  🚀 RECOMMENDED\n- [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)  ☘️\n RECOMMENDED\n- [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)\n- [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) 🌟 NEW\n- [TFLite, ONNX, CoreML, TensorRT Export](https://github.com/ultralytics/yolov5/issues/251) 🚀\n- [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303)\n- [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318)\n- [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304)\n- [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607)\n- [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314)\n- [Architecture Summary](https://github.com/ultralytics/yolov5/issues/6998) 🌟 NEW\n- [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289)\n- [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975)  🌟 NEW\n- [ClearML Logging](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) 🌟 NEW\n- [Deci Platform](https://github.com/ultralytics/yolov5/wiki/Deci-Platform) 🌟 NEW\n\n
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Environments
\n\nGet started in seconds with our verified environments. Click each icon below for details.\n\n
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Integrations
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\n\n|Deci ⭐ NEW|ClearML ⭐ NEW|Roboflow|Weights & Biases\n|:-:|:-:|:-:|:-:|\n|Automatically compile and quantize YOLOv5 for better inference performance in one click at [Deci](https://bit.ly/yolov5-deci-platform)|Automatically track, visualize and even remotely train YOLOv5 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!)|Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) |Automatically track and visualize all your YOLOv5 training runs in the cloud with [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme)\n\n\n##
Why YOLOv5
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\n YOLOv5-P5 640 Figure (click to expand)\n\n

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\n Figure Notes (click to expand)\n\n- **COCO AP val** denotes mAP@0.5:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536.\n- **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100 instance at batch-size 32.\n- **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8.\n- **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`\n\n
\n\n### Pretrained Checkpoints\n\n| Model | size
(pixels) | mAPval
0.5:0.95 | mAPval
0.5 | Speed
CPU b1
(ms) | Speed
V100 b1
(ms) | Speed
V100 b32
(ms) | params
(M) | FLOPs
@640 (B) |\n|------------------------------------------------------------------------------------------------------|-----------------------|-------------------------|--------------------|------------------------------|-------------------------------|--------------------------------|--------------------|------------------------|\n| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** |\n| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 |\n| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 |\n| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 |\n| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 |\n| | | | | | | | | |\n| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |\n| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |\n| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |\n| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |\n| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x6.pt)
+ [TTA][TTA] | 1280
1536 | 55.0
**55.8** | 72.7
**72.7** | 3136
- | 26.2
- | 19.4
- | 140.7
- | 209.8
- |\n\n
\n Table Notes (click to expand)\n\n- All checkpoints are trained to 300 epochs with default settings. Nano and Small models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, all others use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml).\n- **mAPval** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`\n- **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.
Reproduce by `python val.py --data coco.yaml --img 640 --task speed --batch 1`\n- **TTA** [Test Time Augmentation](https://github.com/ultralytics/yolov5/issues/303) includes reflection and scale augmentations.
Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`\n\n
\n\n##
Classification ⭐ NEW
\n\nYOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases) brings support for classification model training, validation, prediction and export! We've made training classifier models super simple. Click below to get started.\n\n
\n Classification Checkpoints (click to expand)\n\n
\n\nWe trained YOLOv5-cls classification models on ImageNet for 90 epochs using a 4xA100 instance, and we trained ResNet and EfficientNet models alongside with the same default training settings to compare. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on Google [Colab Pro](https://colab.research.google.com/signup) for easy reproducibility.\n\n| Model | size
(pixels) | acc
top1 | acc
top5 | Training
90 epochs
4xA100 (hours) | Speed
ONNX CPU
(ms) | Speed
TensorRT V100
(ms) | params
(M) | FLOPs
@224 (B) |\n|----------------------------------------------------------------------------------------------------|-----------------------|------------------|------------------|----------------------------------------------|--------------------------------|-------------------------------------|--------------------|------------------------|\n| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n-cls.pt) | 224 | 64.6 | 85.4 | 7:59 | **3.3** | **0.5** | **2.5** | **0.5** |\n| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s-cls.pt) | 224 | 71.5 | 90.2 | 8:09 | 6.6 | 0.6 | 5.4 | 1.4 |\n| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m-cls.pt) | 224 | 75.9 | 92.9 | 10:06 | 15.5 | 0.9 | 12.9 | 3.9 |\n| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l-cls.pt) | 224 | 78.0 | 94.0 | 11:56 | 26.9 | 1.4 | 26.5 | 8.5 |\n| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x-cls.pt) | 224 | **79.0** | **94.4** | 15:04 | 54.3 | 1.8 | 48.1 | 15.9 |\n| |\n| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet18.pt) | 224 | 70.3 | 89.5 | **6:47** | 11.2 | 0.5 | 11.7 | 3.7 |\n| [ResNet34](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet34.pt) | 224 | 73.9 | 91.8 | 8:33 | 20.6 | 0.9 | 21.8 | 7.4 |\n| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet50.pt) | 224 | 76.8 | 93.4 | 11:10 | 23.4 | 1.0 | 25.6 | 8.5 |\n| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet101.pt) | 224 | 78.5 | 94.3 | 17:10 | 42.1 | 1.9 | 44.5 | 15.9 |\n| |\n| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b0.pt) | 224 | 75.1 | 92.4 | 13:03 | 12.5 | 1.3 | 5.3 | 1.0 |\n| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b1.pt) | 224 | 76.4 | 93.2 | 17:04 | 14.9 | 1.6 | 7.8 | 1.5 |\n| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b2.pt) | 224 | 76.6 | 93.4 | 17:10 | 15.9 | 1.6 | 9.1 | 1.7 |\n| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b3.pt) | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 |\n\n
\n Table Notes (click to expand)\n\n- All checkpoints are trained to 90 epochs with SGD optimizer with `lr0=0.001` and `weight_decay=5e-5` at image size 224 and all default settings.
Runs logged to https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2\n- **Accuracy** values are for single-model single-scale on [ImageNet-1k](https://www.image-net.org/index.php) dataset.
Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224`\n- **Speed** averaged over 100 inference images using a Google [Colab Pro](https://colab.research.google.com/signup) V100 High-RAM instance.
Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`\n- **Export** to ONNX at FP32 and TensorRT at FP16 done with `export.py`.
Reproduce by `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`\n
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\n Classification Usage Examples (click to expand)\n\n### Train\nYOLOv5 classification training supports auto-download of MNIST, Fashion-MNIST, CIFAR10, CIFAR100, Imagenette, Imagewoof, and ImageNet datasets with the `--data` argument. To start training on MNIST for example use `--data mnist`.\n\n```bash\n# Single-GPU\npython classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128\n\n# Multi-GPU DDP\npython -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3\n```\n\n### Val\nValidate YOLOv5m-cls accuracy on ImageNet-1k dataset:\n```bash\nbash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images)\npython classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate\n```\n\n### Predict\nUse pretrained YOLOv5s-cls.pt to predict bus.jpg:\n```bash\npython classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg\n```\n```python\nmodel = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s-cls.pt') # load from PyTorch Hub\n```\n\n### Export\nExport a group of trained YOLOv5s-cls, ResNet and EfficientNet models to ONNX and TensorRT:\n```bash\npython export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224\n```\n
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Contribute
\n\nWe love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our [Contributing Guide](CONTRIBUTING.md) to get started, and fill out the [YOLOv5 Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experiences. Thank you to all our contributors!\n\n\n\n\n##
Contact
\n\nFor YOLOv5 bugs and feature requests please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For business inquiries or\nprofessional support requests please visit [https://ultralytics.com/contact](https://ultralytics.com/contact).\n\n
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\n\n[assets]: https://github.com/ultralytics/yolov5/releases\n[tta]: https://github.com/ultralytics/yolov5/issues/303", + "readme_title": "
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