# Deployment Guide for Hugging Face Spaces

## 📁 File Structure

Make sure your repository has the following structure:

```
your-space/
├── main.py                 # Main FastAPI application
├── app.py                  # Alternative entry point
├── requirements.txt        # Python dependencies
├── Dockerfile             # Docker configuration
├── README.md              # Space documentation
├── .gitignore            # Git ignore rules
├── .dockerignore         # Docker ignore rules
└── DEPLOYMENT_GUIDE.md   # This file
```

## 🚀 Step-by-Step Deployment

### 1. Create a New Space

1. Go to [Hugging Face Spaces](https://huggingface.co/spaces)
2. Click "Create new Space"
3. Fill in the details:
   - **Space name**: `plant-disease-api` (or your preferred name)
   - **License**: Apache 2.0
   - **SDK**: Docker
   - **Hardware**: CPU Basic (upgrade to GPU if needed)
   - **Visibility**: Public or Private

### 2. Configure the Space

The README.md file already contains the necessary YAML frontmatter:

```yaml
---
title: Plant Disease Prediction API
emoji: 🌱
colorFrom: green
colorTo: blue
sdk: docker
pinned: false
license: apache-2.0
app_port: 7860
---
```

### 3. Upload Files

You can either:

**Option A: Git Clone and Push**

```bash
git clone https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME
cd YOUR_SPACE_NAME
# Copy all files to this directory
git add .
git commit -m "Initial deployment"
git push
```

**Option B: Web Interface**

- Upload files directly through the Hugging Face web interface
- Drag and drop or use the file upload feature

### 4. Environment Variables (Optional)

If you need to set custom environment variables:

1. Go to your Space settings
2. Add environment variables:
   - `HF_MODEL_REPO`: Your model repository
   - `HF_MODEL_FILENAME`: Your model filename
   - `HF_HOME`: Cache directory (default: `/tmp/huggingface`)

### 5. Monitor Deployment

1. Go to your Space page
2. Check the "Logs" tab for build progress
3. Wait for the status to change from "Building" to "Running"

## 🔧 Configuration Details

### Port Configuration

- Hugging Face Spaces expects applications to run on port **7860**
- The Dockerfile and application are configured for this

### Model Loading

- The model will be downloaded from Hugging Face Hub on first startup
- Subsequent startups will use cached model (faster)
- Pre-warming ensures fast first predictions

### Resource Requirements

- **Memory**: ~2-3GB for TensorFlow + model
- **CPU**: Minimum 2 cores recommended
- **Storage**: ~1GB for model and dependencies

## 🐛 Troubleshooting

### Common Issues

1. **Build Fails**

   - Check logs in the Space interface
   - Verify all files are uploaded correctly
   - Ensure requirements.txt has correct versions

2. **Model Loading Errors**

   - Verify `HF_MODEL_REPO` and `HF_MODEL_FILENAME` are correct
   - Check if model exists and is accessible
   - Review model format (should be .keras file)

3. **Memory Issues**

   - Upgrade to larger hardware tier
   - Optimize model loading in code
   - Clear unnecessary cache

4. **Port Issues**
   - Ensure application runs on port 7860
   - Check Dockerfile EXPOSE directive
   - Verify app_port in README.md frontmatter

### Debug Commands

Add these to your main.py for debugging:

```python
import os
import psutil
import logging

# Log system info
logging.info(f"Available memory: {psutil.virtual_memory().total / 1e9:.2f} GB")
logging.info(f"CPU cores: {psutil.cpu_count()}")
logging.info(f"Python version: {sys.version}")
logging.info(f"TensorFlow version: {tf.__version__}")
```

## 📊 Testing Your Deployment

### Health Check

```bash
curl https://YOUR_USERNAME-YOUR_SPACE_NAME.hf.space/health
```

### Test Prediction

```bash
curl -X POST "https://YOUR_USERNAME-YOUR_SPACE_NAME.hf.space/predict" \
     -F "files=@your_test_image.jpg"
```

### Interactive API Docs

Visit: `https://YOUR_USERNAME-YOUR_SPACE_NAME.hf.space/docs`

## 🔄 Updates and Maintenance

### Updating Your Space

1. Make changes to your local files
2. Push to the Space repository
3. Space will automatically rebuild and redeploy

### Monitoring Performance

- Check Space logs regularly
- Monitor response times
- Watch for memory usage spikes

### Scaling Options

- Upgrade hardware tier for better performance
- Consider GPU hardware for faster inference
- Implement caching for frequently used predictions

## 🔒 Security Considerations

- Keep your Space public for API access
- Don't include sensitive credentials in code
- Use environment variables for configuration
- Monitor usage to prevent abuse

## 📈 Performance Optimization

### Model Optimization

- Use model quantization for smaller size
- Implement model pruning if needed
- Cache predictions when possible

### API Optimization

- Add request rate limiting
- Implement response caching
- Optimize image preprocessing

---

**Need Help?**

- Check [Hugging Face Spaces Documentation](https://huggingface.co/docs/spaces)
- Visit [Community Forums](https://discuss.huggingface.co/)
- Create an issue in your Space repository