Hugging Face Deployment Checklist

Welcome to the hf_deployment folder! This directory contains everything needed to wrap your ChronoGridFusionNet model into a native Hugging Face model and push it to the Hub.

What is in here?

configuration_chronogrid.py: The Hugging Face config class (stores hyperparameters like image size and num classes).
modeling_chronogrid.py: Your PyTorch architecture, wrapped in PreTrainedModel.
push_to_hf.py: The script you will run to package everything and push it to your Hugging Face account.

What you need to do BEFORE publishing:

1. Authenticate with Hugging Face

You need to be logged into your Hugging Face account on your terminal so the script can push files.

Open your terminal and run: huggingface-cli login
Paste your Hugging Face Access Token (you can generate one with write permissions in your account Settings -> Access Tokens).

2. Update the `push_to_hf.py` script

Open push_to_hf.py and modify two things:

Line 13: Change repo_id = "your-username/chronogrid-fusionnet" to your actual HF username and desired repo name.
Line 21: Ensure model_path = '../models/Chronogrid_fold5.pt' points to the exact .pt file you saved after training.

3. Provide your Preprocessing logic

Right now, if someone downloads your model, they won't know how to turn a raw grid signal into a 227x227 image.

Action: Create a preprocess.py file in this folder. It should contain whatever Python code you used (like S-Transform functions) to generate the .jpg heatmaps you used for training.
You will upload this preprocess.py file manually to your Hugging Face repo later, or add an api.upload_file(...) line in push_to_hf.py for it.

4. Run the Push Script!

When you are ready:

cd /Users/sanathbs/02_College_UG/IEEE-9bus/hf_deployment
pip install transformers huggingface_hub
python push_to_hf.py

5. Finalize the Model Card (README.md)

Once the model is pushed, go to the model's page on huggingface.co and edit the README.md (Model Card). You should explain:

What this is: Fault detection for WSCC 9-Bus system.
How to use it: Provide a small code snippet showing how to use AutoModel.from_pretrained(...).
Metrics: Paste your final test accuracy and confusion matrix stats.