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# 🚀 Deployment Guide for Smart Object Size Estimator
This guide explains how to deploy the Smart Object Size Estimator to Hugging Face Spaces.
## 📁 Required Files
Make sure you have all these files in your HuggingFace Space:
```
├── app.py # Main Gradio application
├── requirements.txt # Python dependencies
├── config.yaml # HuggingFace Space configuration
├── README.md # Project documentation
├── DEPLOYMENT.md # This deployment guide
└── goal2/ # Source code directory
└── src/
└── agent/
├── __init__.py
├── models.py # AI model loading
├── geometry.py # Size calculations
└── io.py # Image processing utilities
```
## 🔧 Step-by-Step Deployment
### 1. Create a New HuggingFace Space
1. Go to [huggingface.co/spaces](https://huggingface.co/spaces)
2. Click "Create new Space"
3. Choose:
- **Space name**: `smart-object-size-estimator` (or your preference)
- **License**: MIT
- **SDK**: Gradio
- **Hardware**: CPU Basic (free tier) or GPU if available
### 2. Upload Files
Upload all the files listed above to your Space. You can either:
- Use the web interface to drag & drop files
- Use git to clone and push files
- Use the HuggingFace CLI
### 3. Model Weight Configuration
The app automatically downloads model weights:
- **Depth Anything V2**: Downloaded from HuggingFace Hub
- **SAM**: Downloaded from Facebook's repository
No manual model upload required!
### 4. Environment Configuration
The `config.yaml` file configures:
- Python version: 3.9+
- Gradio SDK version: 4.44.0
- Hardware requirements: CPU (upgradeable to GPU)
### 5. Dependencies Installation
All dependencies are listed in `requirements.txt`:
- Core ML: PyTorch, transformers
- Image processing: OpenCV, PIL, matplotlib
- Web interface: Gradio
- Utilities: NumPy, scikit-image
## 🚦 Launch Process
1. **Upload files** → Space shows "Building..."
2. **Install dependencies** → Takes ~2-3 minutes
3. **Download models** → First run takes ~1-2 minutes
4. **Ready!** → App accessible at your Space URL
## 📊 Expected Resource Usage
### CPU Version (Free Tier)
- **RAM**: ~2-4 GB during inference
- **Storage**: ~3 GB (models + dependencies)
- **Speed**: 10-30 seconds per image
### GPU Version (Paid Tier)
- **RAM**: ~4-6 GB during inference
- **Speed**: 3-8 seconds per image
- **Cost**: Variable based on usage
## 🔍 Troubleshooting
### Common Issues
1. **"Model download failed"**
- Solution: Check internet connectivity, retry after a few minutes
- Models are large (1-2 GB total)
2. **"Out of memory" errors**
- Solution: Upgrade to GPU hardware or reduce image sizes
- The app automatically resizes very large images
3. **"Import errors"**
- Solution: Check `requirements.txt` versions
- Ensure all dependencies are properly listed
4. **Slow processing**
- Solution: Consider upgrading to GPU hardware
- CPU processing is slower but works fine
### Debug Mode
To enable debugging, set `debug=True` in the `demo.launch()` call:
```python
demo.launch(share=True, debug=True)
```
## 🎯 Performance Optimization
### For Better Speed:
1. Upgrade to GPU hardware
2. Use smaller input images when possible
3. Consider caching models if using persistent storage
### For Better Accuracy:
1. Use camera-specific presets when available
2. Ensure good lighting in input images
3. Choose reference objects with known precise dimensions
## 🔐 Privacy & Security
- **No data storage**: Images are processed in memory only
- **No user tracking**: No personal data collected
- **Model privacy**: All processing happens on HuggingFace servers
- **Open source**: All code is publicly available
## 📈 Monitoring & Analytics
HuggingFace Spaces provides built-in analytics:
- Usage statistics
- Performance metrics
- Error logs
- Resource consumption
Access these through your Space settings panel.
## 📞 Support
If you encounter issues:
1. **Check the Space logs** in the HuggingFace interface
2. **Review the README.md** for usage instructions
3. **Test with example images** to isolate problems
4. **Consider hardware upgrades** for performance issues
## 🔄 Updates & Maintenance
To update the app:
1. Modify files in your local copy
2. Push changes to the Space repository
3. Space will automatically rebuild
4. Test thoroughly after updates
## 📋 Pre-Launch Checklist
- [ ] All required files uploaded
- [ ] `config.yaml` properly configured
- [ ] `requirements.txt` includes all dependencies
- [ ] Test with sample images
- [ ] Verify camera presets work
- [ ] Check reference object system
- [ ] Ensure error handling works
- [ ] Documentation is complete
## 🎉 Launch!
Once everything is set up, your Space will be live at:
`https://huggingface.co/spaces/[your-username]/[space-name]`
Share this URL with users and enjoy your deployed AI-powered size estimation tool!
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