DEPLOYMENT_UPDATE.md

🚀 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

# 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:

# 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!