# 🚀 HuggingFace Spaces Deployment Guide

## Updated Features (v2.0.0)

Your SmolVLM2 Video Highlights app has been upgraded with:

✅ **HuggingFace Segment-Based Approach**: More reliable than previous audio+visual system  
✅ **SmolVLM2-256M-Video-Instruct**: Optimized for Spaces resource constraints  
✅ **Dual Criteria Generation**: Two prompt variations for robust highlight selection  
✅ **Simple Fade Transitions**: Compatible effects that work across all devices  
✅ **Fixed 5-Second Segments**: Consistent AI classification without timestamp issues  

## Files Updated

### Core System
- `app.py` - New FastAPI app using segment-based approach
- `huggingface_segment_highlights.py` - Main highlight detection logic
- `src/smolvlm2_handler.py` - Updated to use 256M model by default

### Configuration
- `README.md` - Updated documentation
- `Dockerfile` - Points to new app.py
- Requirements remain the same

## Deployment Steps

### 1. Push to HuggingFace Spaces

```bash
# If you have an existing Space, update it:
cd smolvlm2-video-highlights
git add .
git commit -m "Update to HuggingFace segment-based approach v2.0.0

- Switch to SmolVLM2-256M-Video-Instruct for better Spaces compatibility
- Implement proven segment-based classification method  
- Add dual criteria generation for robust selection
- Simplify effects for universal device compatibility
- Improve API with detailed job status and progress tracking"

git push origin main
```

### 2. Update Space Settings

In your HuggingFace Space settings:
- **SDK**: Docker
- **App Port**: 7860  
- **Hardware**: GPU T4 Small (2.2B model benefits from GPU acceleration)
- **Timeout**: 30 minutes (for longer videos)

### 3. Test the Deployment

Once deployed, your Space will be available at:
`https://your-username-smolvlm2-video-highlights.hf.space`

Test with the API:
```bash
# Upload video
curl -X POST \
  -F "video=@test_video.mp4" \
  -F "segment_length=5.0" \
  -F "with_effects=true" \
  https://your-space-url.hf.space/upload-video

# Check status  
curl https://your-space-url.hf.space/job-status/JOB_ID

# Download results
curl -O https://your-space-url.hf.space/download/FILENAME.mp4
```

## Key Improvements

### Performance
- **40% smaller model**: 256M vs 500M parameters
- **Faster inference**: Optimized for CPU deployment
- **Lower memory**: Better for Spaces hardware limits

### Reliability  
- **No timestamp correlation**: Avoids AI timing errors
- **Fixed segment length**: Consistent classification
- **Dual prompt system**: More robust criteria generation
- **Simple effects**: Universal device compatibility

### API Features
- **Real-time progress**: Detailed job status updates
- **Background processing**: Non-blocking uploads
- **Automatic cleanup**: Manages disk space
- **Error handling**: Graceful failure modes

## Monitoring

Check your Space logs for:
- Model loading success
- Processing progress
- Error messages
- Resource usage

## Troubleshooting

### Out of Memory
- Use CPU Basic hardware
- Consider shorter videos (<5 minutes)
- Monitor progress in Space logs

### Slow Processing  
- 256M model is CPU-optimized
- Processing time: ~1-2x video length
- Consider GPU upgrade for faster processing

### Effects Issues
- Simple fade transitions work on all devices
- Compatible MP4 output format
- No complex filter chains

## Next Steps

1. Deploy and test your updated Space
2. Update any client applications to use new API structure
3. Monitor performance and adjust settings as needed
4. Consider adding a web UI using Gradio if desired

Your upgraded system is now more reliable, efficient, and compatible with HuggingFace Spaces infrastructure!