# 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?
1. `configuration_chronogrid.py`: The Hugging Face config class (stores hyperparameters like image size and num classes).
2. `modeling_chronogrid.py`: Your PyTorch architecture, wrapped in `PreTrainedModel`.
3. `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:
1.  **Line 13:** Change `repo_id = "your-username/chronogrid-fusionnet"` to your actual HF username and desired repo name.
2.  **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:
```bash
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.