DEPLOYMENT_GUIDE.md

# Hugging Face Spaces Deployment Guide

## Steps to Deploy on Hugging Face Spaces

### 1. Create a New Space
1. Go to [Hugging Face Spaces](https://huggingface.co/new-space)
2. Choose a name for your space (e.g., `content-classifier`)
3. Select **Docker** as the SDK
4. Set the space to **Public** or **Private** as needed
5. Click **Create Space**

### 2. Upload Files to Your Space

You need to upload these files to your Space repository:

```
contextClassifier.onnx    # Your ONNX model
app.py                   # FastAPI application
requirements.txt         # Python dependencies
Dockerfile              # Docker configuration
README.md               # This will become the Space's README
```

### 3. Required Files Content

**For the Space's README.md header, add this at the top:**
```yaml
---
title: Content Classifier
emoji: 🔍
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false
license: mit
app_port: 7860
---
```

### 4. Deployment Process

1. **Via Git (Recommended):**
   ```bash
   git clone https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME
   cd YOUR_SPACE_NAME
   
   # Copy your files
   copy contextClassifier.onnx .
   copy app.py .
   copy requirements.txt .
   copy Dockerfile .
   
   # Commit and push
   git add .
   git commit -m "Add content classifier API"
   git push
   ```

2. **Via Web Interface:**
   - Use the **Files** tab in your Space
   - Upload each file individually
   - Or drag and drop all files at once

### 5. Monitor Deployment

1. Go to your Space URL: `https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME`
2. Check the **Logs** tab to monitor the build process
3. The Space will show "Building" status during deployment
4. Once ready, you'll see the API documentation interface

### 6. Access Your API

Once deployed, your API will be available at:
- **Swagger UI:** `https://YOUR_USERNAME-YOUR_SPACE_NAME.hf.space/docs`
- **API Endpoints:**
  - `POST /predict` - Main prediction endpoint
  - `GET /health` - Health check
  - `GET /model-info` - Model information

### 7. Example Usage

```python
import requests

# Replace with your actual Space URL
api_url = "https://YOUR_USERNAME-YOUR_SPACE_NAME.hf.space"

# Make a prediction
response = requests.post(
    f"{api_url}/predict",
    json={"text": "This is a test message"}
)

print(response.json())
```

### 8. Important Notes

- **Model Size:** Make sure your `contextClassifier.onnx` file is under the Space's size limit
- **Cold Start:** The first request might take longer as the Space wakes up
- **Logs:** Monitor the logs for any runtime errors
- **Updates:** Any push to the repository will trigger a rebuild

### 9. Troubleshooting

**Common Issues:**
- **Build Fails:** Check logs for dependency issues
- **Model Not Found:** Ensure `contextClassifier.onnx` is in the root directory
- **Port Issues:** Make sure the app uses port 7860
- **Memory Issues:** Large models might exceed memory limits

**Solutions:**
- Review requirements.txt for compatible versions
- Check model file path in app.py
- Verify Dockerfile exposes port 7860
- Consider model optimization for deployment