app.py

import json
import random

import gradio as gr
import pandas as pd


def read_json(file_name):
    with open(file_name, "r", encoding="utf-8") as f:
        json_data = json.load(f)
    return json_data


# Load data
json_file = "awesome-ChatGPT-repositories.json"
json_data = read_json(json_file)

candidate_words_file = "candidate_words.json"
candidate_words_data = read_json(candidate_words_file)
candidate_words = candidate_words_data.get("candidate_words", [])

# Build repository data with categories
repos_data = []
categories = set()

for category, repos in json_data["contents"].items():
    categories.add(category)
    for url, repo in repos.items():
        repos_data.append({
            "url": url,
            "name": repo["repository_name"],
            "user": repo.get("user_name", ""),
            "language": repo.get("language") or "N/A",
            "license": repo.get("license") or "N/A",
            "description_en": repo["multilingual_descriptions"].get("en", ""),
            "description_ja": repo["multilingual_descriptions"].get("ja", ""),
            "description_zh_hans": repo["multilingual_descriptions"].get("zh-hans", ""),
            "description_zh_hant": repo["multilingual_descriptions"].get("zh-hant", ""),
            "topics": repo.get("topics", []),
            "category": category,
        })

categories = sorted(list(categories))
total_repos = len(repos_data)


def get_description(repo, lang):
    """Get description based on language selection"""
    if lang == "English":
        return repo["description_en"]
    elif lang == "Japanese":
        return repo["description_ja"]
    elif lang == "Chinese (Simplified)":
        return repo["description_zh_hans"]
    elif lang == "Chinese (Traditional)":
        return repo["description_zh_hant"]
    return repo["description_en"]


def search_repos(query, selected_categories, selected_language, lang):
    """Search repositories based on query and filters"""
    results = []
    query_lower = query.lower().strip()
    queries = query_lower.split() if query_lower else []

    for repo in repos_data:
        # Category filter
        if selected_categories and repo["category"] not in selected_categories:
            continue

        # Language filter
        if selected_language and selected_language != "All" and repo["language"] != selected_language:
            continue

        # Text search
        if queries:
            description = get_description(repo, lang).lower()
            name_lower = repo["name"].lower()
            topics_str = " ".join(repo["topics"]).lower()
            search_text = f"{name_lower} {description} {topics_str}"

            if not all(q in search_text for q in queries):
                continue

        results.append(repo)

    return results


def format_results(results, lang):
    """Format results for display"""
    if not results:
        return pd.DataFrame(columns=["Repository", "Category", "Language", "Description"])

    data = {
        "Repository": [],
        "Category": [],
        "Language": [],
        "Description": [],
    }

    for repo in results:
        repo_link = f"[{repo['name']}]({repo['url']})"
        description = get_description(repo, lang)
        # Truncate long descriptions
        if len(description) > 150:
            description = description[:147] + "..."

        data["Repository"].append(repo_link)
        data["Category"].append(repo["category"])
        data["Language"].append(repo["language"])
        data["Description"].append(description)

    return pd.DataFrame(data)


def do_search(query, selected_categories, selected_language, lang):
    """Main search function"""
    results = search_repos(query, selected_categories, selected_language, lang)
    df = format_results(results, lang)
    count_text = f"Found **{len(results)}** repositories"
    return df, count_text


def get_random_suggestion():
    """Get random search suggestion from candidate words"""
    if candidate_words:
        suggestions = random.sample(candidate_words, min(5, len(candidate_words)))
        return ", ".join(suggestions)
    return "prompt, langchain, agent, chatbot, api"


def get_languages():
    """Get unique programming languages"""
    languages = set()
    for repo in repos_data:
        if repo["language"] and repo["language"] != "N/A":
            languages.add(repo["language"])
    return ["All"] + sorted(list(languages))


# Build the Gradio interface
with gr.Blocks(
    title="Awesome ChatGPT Repositories Search",
    theme=gr.themes.Soft(),
) as demo:
    gr.Markdown(
        """
        # Awesome ChatGPT Repositories Search

        Discover and explore **{total}** open-source repositories from the [awesome-ChatGPT-repositories](https://github.com/taishi-i/awesome-ChatGPT-repositories) collection.
        Find tools, libraries, and projects related to ChatGPT, LLMs, and AI.
        """.format(total=total_repos)
    )

    with gr.Row():
        with gr.Column(scale=3):
            query_input = gr.Textbox(
                label="Search",
                placeholder="Enter keywords to search (e.g., prompt, langchain, agent)",
            )
            suggestion = gr.Markdown(
                f"*Try searching:* {get_random_suggestion()}",
            )

        with gr.Column(scale=1):
            display_lang = gr.Dropdown(
                choices=["English", "Japanese", "Chinese (Simplified)", "Chinese (Traditional)"],
                value="English",
                label="Description Language",
            )

    with gr.Accordion("Filters", open=False):
        with gr.Row():
            with gr.Column():
                category_filter = gr.CheckboxGroup(
                    choices=categories,
                    label="Categories",
                )
            with gr.Column():
                language_filter = gr.Dropdown(
                    choices=get_languages(),
                    value="All",
                    label="Programming Language",
                )

    result_count = gr.Markdown("")

    results_table = gr.DataFrame(
        value=format_results(repos_data[:100], "English"),
        datatype=["markdown", "str", "str", "str"],
        interactive=False,
        wrap=True,
    )

    gr.Markdown(
        """
        ---
        **Tips:**
        - Use multiple keywords separated by spaces for AND search
        - Use filters to narrow down results by category or programming language
        - Click on repository names to visit their GitHub pages

        Made with Gradio | Data from [awesome-ChatGPT-repositories](https://github.com/taishi-i/awesome-ChatGPT-repositories)
        """
    )

    # Event handlers
    def on_search_change(query, categories, language, lang):
        return do_search(query, categories, language, lang)

    query_input.change(
        fn=on_search_change,
        inputs=[query_input, category_filter, language_filter, display_lang],
        outputs=[results_table, result_count],
    )

    category_filter.change(
        fn=on_search_change,
        inputs=[query_input, category_filter, language_filter, display_lang],
        outputs=[results_table, result_count],
    )

    language_filter.change(
        fn=on_search_change,
        inputs=[query_input, category_filter, language_filter, display_lang],
        outputs=[results_table, result_count],
    )

    display_lang.change(
        fn=on_search_change,
        inputs=[query_input, category_filter, language_filter, display_lang],
        outputs=[results_table, result_count],
    )

    # Initialize with all results
    demo.load(
        fn=lambda: do_search("", [], "All", "English"),
        outputs=[results_table, result_count],
    )

if __name__ == "__main__":
    demo.launch()