# 🚀 Hugging Face Spaces Migration Guide

Complete guide to migrate your Video Transcription Service from Render.com to Hugging Face Spaces with enhanced features and API access.

## 🎯 **Why Hugging Face Spaces?**

### **Advantages over Render.com:**
- ✅ **Higher Resource Limits**: More memory and CPU
- ✅ **Better Performance**: Optimized for ML workloads
- ✅ **Free GPU Access**: Available for intensive tasks
- ✅ **Gradio Integration**: Beautiful web interface
- ✅ **Community Features**: Easy sharing and discovery
- ✅ **Persistent Storage**: Better file handling
- ✅ **API + Web Interface**: Both available simultaneously

## 📋 **Pre-Migration Checklist**

- [ ] Hugging Face account created
- [ ] Git installed locally
- [ ] Python environment ready
- [ ] Test video files prepared
- [ ] Current service functionality documented

## 🛠️ **Step 1: Prepare Deployment Files**

Run the automated preparation script:

```bash
python deploy_to_hf.py
```

This creates a `hf_spaces_deploy/` directory with all necessary files:
- `app.py` - Gradio + FastAPI hybrid interface
- `requirements.txt` - HF Spaces optimized dependencies
- `README.md` - HF Spaces documentation
- `config.py` - HF-optimized configuration
- All supporting modules

## 🌐 **Step 2: Create Hugging Face Space**

1. **Go to Hugging Face Spaces**
   - Visit: https://huggingface.co/spaces
   - Click "Create new Space"

2. **Configure Your Space**
   - **Name**: `video-transcription` (or your choice)
   - **SDK**: Select "Gradio"
   - **Hardware**: Start with "CPU basic" (free)
   - **Visibility**: Public (for API access) or Private

3. **Create Space**
   - Click "Create Space"
   - Note your Space URL: `https://username-spacename.hf.space`

## 📤 **Step 3: Deploy to Hugging Face Spaces**

### **Option A: Git Deployment (Recommended)**

```bash
cd hf_spaces_deploy
git init
git add .
git commit -m "Initial deployment of Video Transcription Service"
git remote add origin https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME
git push -u origin main
```

### **Option B: Web Upload**

1. Go to your Space page
2. Click "Files" tab
3. Upload all files from `hf_spaces_deploy/`
4. Ensure `app.py` is in the root directory

## ⏳ **Step 4: Monitor Deployment**

1. **Check Build Logs**
   - Go to "Logs" tab in your Space
   - Monitor the build process (5-10 minutes)
   - Look for successful model download

2. **Expected Log Output**
   ```
   🚀 Starting Video Transcription Service on Hugging Face Spaces
   🤖 Loading Whisper model for Hugging Face Spaces...
   ✅ Model 'base' preloaded in 45.2 seconds
   🚀 Starting FastAPI service...
   Running on local URL: http://0.0.0.0:7860
   ```

3. **Troubleshoot Issues**
   - Build failures: Check requirements.txt
   - Memory issues: Switch to "CPU upgrade" hardware
   - Model loading issues: Try `WHISPER_MODEL=tiny`

## ✅ **Step 5: Test Your Deployment**

### **Test Web Interface**

1. **Visit Your Space**
   - URL: `https://username-spacename.hf.space`
   - Should see Gradio interface

2. **Upload Test Video**
   - Use "Upload & Transcribe" tab
   - Select a small test video (< 50MB)
   - Choose language or use auto-detect
   - Click "Start Transcription"

3. **Check Results**
   - Note the transcription ID
   - Use "Check Status" tab to monitor progress
   - Verify transcription completes successfully

### **Test API Functionality**

1. **Health Check**
   ```bash
   curl "https://username-spacename.hf.space/api/health"
   ```

2. **Upload Video via API**
   ```bash
   curl -X POST "https://username-spacename.hf.space/api/transcribe" \
     -F "file=@test_video.mp4" \
     -F "language=en"
   ```

3. **Check Status via API**
   ```bash
   curl "https://username-spacename.hf.space/api/transcribe/1"
   ```

4. **Use Python Client**
   ```bash
   python hf_api_client.py https://username-spacename.hf.space test_video.mp4
   ```

## 🔧 **Step 6: Optimize Performance**

### **Hardware Upgrades**

If you experience performance issues:

1. **Go to Space Settings**
2. **Hardware → Upgrade**
3. **Options:**
   - CPU basic (free) - 2 vCPU, 16GB RAM
   - CPU upgrade ($0.05/hour) - 8 vCPU, 32GB RAM
   - GPU T4 small ($0.60/hour) - For heavy workloads

### **Model Optimization**

Adjust model size based on your needs:

```python
# In Space settings, add environment variable:
WHISPER_MODEL=tiny    # Fastest, good quality
WHISPER_MODEL=base    # Balanced (default)
WHISPER_MODEL=small   # Better quality, slower
```

## 📊 **Step 7: Compare Features**

| Feature | Render.com | Hugging Face Spaces |
|---------|------------|-------------------|
| **Memory** | 512MB | 16GB (basic) / 32GB (upgrade) |
| **CPU** | Shared | 2-8 vCPU dedicated |
| **Storage** | Ephemeral | Persistent |
| **GPU** | None | T4 available |
| **Interface** | API only | Gradio + API |
| **Community** | Limited | Built-in sharing |
| **Cost** | Free tier limited | More generous free tier |

## 🔄 **Step 8: Migration Validation**

### **Functionality Checklist**

- [ ] ✅ Web interface loads correctly
- [ ] ✅ Video upload works (multiple formats)
- [ ] ✅ Language detection/selection works
- [ ] ✅ Transcription processing completes
- [ ] ✅ Results display correctly
- [ ] ✅ API endpoints respond correctly
- [ ] ✅ Status checking works
- [ ] ✅ Error handling functions
- [ ] ✅ Automatic cleanup operates
- [ ] ✅ Logging provides good visibility

### **Performance Validation**

- [ ] ✅ Model loads within 2-3 minutes
- [ ] ✅ First transcription completes successfully
- [ ] ✅ Subsequent transcriptions are faster
- [ ] ✅ Large files (up to 200MB) process correctly
- [ ] ✅ Multiple concurrent requests handled
- [ ] ✅ Memory usage stays within limits

## 🌐 **Step 9: Update Your Applications**

### **Update API Endpoints**

Replace your Render.com URLs:

```python
# Old Render.com URL
OLD_URL = "https://your-service.onrender.com"

# New HF Spaces URL
NEW_URL = "https://username-spacename.hf.space"

# API endpoints remain the same:
# POST /api/transcribe
# GET /api/transcribe/{id}
# GET /api/health
```

### **Update Client Code**

```python
# Use the new HF API client
from hf_api_client import HFTranscriptionClient

client = HFTranscriptionClient("https://username-spacename.hf.space")
result = client.transcribe_and_wait("video.mp4")
```

## 🎉 **Step 10: Go Live**

### **Share Your Space**

1. **Make Public** (if desired)
   - Space Settings → Visibility → Public

2. **Add to Profile**
   - Pin to your HF profile
   - Add description and tags

3. **Share URL**
   - Web interface: `https://username-spacename.hf.space`
   - API base: `https://username-spacename.hf.space/api`

### **Monitor Usage**

- Check Space analytics
- Monitor resource usage
- Review user feedback
- Update documentation as needed

## 🔧 **Troubleshooting**

### **Common Issues**

1. **Build Fails**
   ```
   Solution: Check requirements.txt, ensure all dependencies are compatible
   ```

2. **Model Loading Timeout**
   ```
   Solution: Upgrade to CPU upgrade hardware or use WHISPER_MODEL=tiny
   ```

3. **API Not Accessible**
   ```
   Solution: Ensure Space is Public and FastAPI is running on port 7860
   ```

4. **Memory Issues**
   ```
   Solution: Upgrade hardware or reduce MAX_FILE_SIZE in config
   ```

## 📞 **Support Resources**

- **HF Spaces Documentation**: https://huggingface.co/docs/hub/spaces
- **Gradio Documentation**: https://gradio.app/docs/
- **Community Forum**: https://discuss.huggingface.co/
- **Your Space Logs**: Available in Space dashboard

## 🎯 **Next Steps**

After successful migration:

1. **Decommission Render.com** service
2. **Update documentation** with new URLs
3. **Notify users** of the migration
4. **Monitor performance** and optimize as needed
5. **Consider GPU upgrade** for heavy workloads

---

**🎉 Congratulations! Your Video Transcription Service is now running on Hugging Face Spaces with enhanced capabilities and better performance!**

**Key Benefits Achieved:**
- ✅ Higher resource limits
- ✅ Beautiful Gradio web interface
- ✅ Full API compatibility maintained
- ✅ Better community integration
- ✅ More reliable performance
- ✅ Future GPU upgrade path