DEPLOYMENT.md

🚀 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
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:

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!