medsam-inference / README.txt
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HuggingFace Space for MedSAM - Complete Package
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WHAT YOU HAVE:
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βœ“ Complete HuggingFace Space setup for your MedSAM model
βœ“ Drop-in replacement client for your backend
βœ“ Test scripts and integration examples
βœ“ Full documentation
FILES IN THIS FOLDER:
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πŸ“¦ FOR HUGGINGFACE SPACE (upload these):
1. app.py - Gradio app with API
2. requirements.txt - Dependencies
3. README.md - Space description
4. .gitattributes - Git LFS config
+ medsam_vit_b.pth - Your model (download from HF)
πŸ“š DOCUMENTATION:
5. QUICKSTART.md - START HERE! 5-minute deploy guide
6. DEPLOYMENT_GUIDE.md - Detailed deployment steps
7. README_INTEGRATION.md - How to use in your backend
πŸ”§ CODE EXAMPLES:
8. integration_example.py - Integration examples
9. test_space.py - Test script after deployment
πŸ“„ FOR YOUR BACKEND:
../medsam_space_client.py - Drop-in SAM replacement (already copied!)
QUICK START (15 minutes total):
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STEP 1: Deploy Space (5 min)
β†’ Read: QUICKSTART.md
β†’ Go to: https://huggingface.co/new-space
β†’ Upload: app.py, requirements.txt, README.md, .gitattributes
β†’ Download & upload: medsam_vit_b.pth (from Aniketg6/Fine-Tuned-MedSAM)
β†’ Wait for build
STEP 2: Test Space (2 min)
β†’ Visit: https://huggingface.co/spaces/YOUR_USERNAME/medsam-inference
β†’ Upload image in UI
β†’ Click "Segment"
β†’ Verify it works!
STEP 3: Integrate with Backend (5 min)
β†’ Read: README_INTEGRATION.md
β†’ File already copied: ../medsam_space_client.py
β†’ Update app.py (just 5 lines!)
β†’ Add to .env: MEDSAM_SPACE_URL=https://YOUR_USERNAME-medsam-inference.hf.space/api/predict
STEP 4: Test Integration (3 min)
β†’ Run: python test_space.py test_image.jpg 200 150
β†’ Start your backend: python app.py
β†’ Test your API endpoint
DONE! πŸŽ‰
INTEGRATION SUMMARY:
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BEFORE (in your app.py):
from segment_anything import sam_model_registry, SamPredictor
sam = sam_model_registry["vit_b"](checkpoint="models/sam_vit_h_4b8939.pth")
sam_predictor = SamPredictor(sam)
AFTER (in your app.py):
from medsam_space_client import MedSAMSpacePredictor
sam_predictor = MedSAMSpacePredictor(os.getenv('MEDSAM_SPACE_URL'))
Everything else stays EXACTLY the same! ✨
BENEFITS:
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βœ“ No more 2.5GB model in memory
βœ“ Can deploy backend to Vercel/serverless
βœ“ Model hosted on HuggingFace (free!)
βœ“ Same API as SAM (drop-in replacement)
COSTS:
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HuggingFace Space:
- Free tier (CPU): FREE, but slower (5-10s per image)
- Paid tier (T4 GPU): $0.60/hour (~$432/month if always on)
Backend Deployment:
- Vercel: Free tier or $20/month (Pro)
- Railway: $7-10/month
- Render: Free tier or $7/month
NEXT STEPS:
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1. Read QUICKSTART.md
2. Deploy your Space (5 minutes)
3. Read README_INTEGRATION.md
4. Update your app.py (5 minutes)
5. Deploy your backend to Vercel/Railway
6. Deploy your frontend to Vercel
7. Celebrate! πŸŽ‰
SUPPORT:
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- Questions about Space deployment? β†’ DEPLOYMENT_GUIDE.md
- Questions about integration? β†’ README_INTEGRATION.md
- Want to test? β†’ test_space.py
- Want examples? β†’ integration_example.py
IMPORTANT LINKS:
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- Create Space: https://huggingface.co/new-space
- Your Model: https://huggingface.co/Aniketg6/Fine-Tuned-MedSAM
- HF Spaces Docs: https://huggingface.co/docs/hub/spaces
- Vercel Docs: https://vercel.com/docs
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Questions? Start with QUICKSTART.md - it has everything you need!
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