File size: 3,673 Bytes
58f0729 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 |
# 🚀 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!
|