START_HERE.md

🎉 DEPLOYMENT READY!

✅ Success! Your HuggingFace Spaces deployment package is ready.

📦 What You Have

The huggingface_deployment folder contains everything needed to deploy your Image Selector Backend to Hugging Face Spaces. Just upload this entire folder!

huggingface_deployment/
│
├── 🐳 Docker Configuration
│   ├── Dockerfile              # Container setup optimized for HF Spaces
│   ├── .dockerignore          # Exclude unnecessary files from build
│   └── requirements.txt       # All Python dependencies
│
├── 📝 Documentation
│   ├── README.md              # Space description (with HF YAML frontmatter!)
│   ├── DEPLOYMENT_GUIDE.md    # Detailed step-by-step deployment guide
│   ├── QUICK_START.md         # Quick overview and tips
│   └── CHECKLIST.md           # Deployment checklist
│
├── ⚙️ Application Code
│   ├── main.py                # FastAPI entry point
│   └── app/                   # Your application
│       ├── api/routes.py      # REST API endpoints
│       ├── core/config.py     # Settings (HF-optimized paths)
│       ├── repositories/      # Database operations
│       └── services/          # ML processing logic
│
└── 📄 Legal
    ├── LICENSE                # MIT License
    └── .gitignore            # Git ignore rules

---

🚀 Next Steps (2 Minutes!)

1️⃣ Create Your Space

Go to: https://huggingface.co/new-space

• Name: image-selector-backend (or your choice)
• SDK: Docker ← Important!
• License: MIT
• Click "Create Space"

2️⃣ Upload Files

Either:

• Drag & Drop: Open your Space → Files tab → Drag the entire huggingface_deployment folder contents

Or:

git clone https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME
cd YOUR_SPACE_NAME
# Copy all files from huggingface_deployment folder here
git add .
git commit -m "Deploy Image Selector Backend"
git push

3️⃣ Wait for Build

• HuggingFace automatically builds your container (~5-10 minutes)
• Watch the "Logs" tab for progress
• Status changes to "Running" when ready

4️⃣ You're Live! 🎊

Your API will be at:

https://YOUR_USERNAME-YOUR_SPACE_NAME.hf.space

Test it:

curl https://YOUR_USERNAME-YOUR_SPACE_NAME.hf.space/
# Returns: {"status":"ok"}

---

📚 Read These First

1. QUICK_START.md - Overview of what's included
2. DEPLOYMENT_GUIDE.md - Detailed deployment instructions
3. CHECKLIST.md - Step-by-step checklist

---

🔑 Key Features Configured

✅ Docker SDK - Full container control
✅ Port 7860 - HuggingFace Spaces default
✅ Non-root user - Security best practices
✅ Smart storage - Auto-detects /data or /tmp
✅ CORS enabled - Works with any frontend
✅ Auto cleanup - Deletes files after download
✅ Per-user isolation - Multiple users supported
✅ Progress tracking - Real-time processing updates

---

💰 Cost Estimates

Free Tier (CPU Basic)

• Cost: $0
• Good for: Testing, demos, light usage
• Limitations: Slow processing, sleeps when idle, no GPU

Production Tier (T4 Small GPU)

• Cost: ~$0.60/hour (only when running)
• Good for: Real users, fast processing
• Benefits: GPU acceleration, always-on, faster processing

With Persistent Storage

• Add: $5/month for 20GB
• Benefit: Data persists across restarts

---

🎨 Connect Your Frontend

Update your frontend code to use your new API:

const API_URL = "https://YOUR_USERNAME-YOUR_SPACE_NAME.hf.space";

Frontend repo: https://github.com/basilbenny1002/image-selector-front-end

---

⚡ Quick Tips

• First run is slow: Downloads ~500MB of ML models
• Subsequent runs are fast: Models are cached
• GPU recommended: 10-20x faster than CPU
• Free tier sleeps: Upgrade to paid for always-on
• Logs are your friend: Check them if issues occur

---

🆘 Troubleshooting

Problem	Solution
Build fails	Check Logs tab for errors
API not responding	Verify Space is "Running" not "Sleeping"
Slow processing	Upgrade to GPU hardware
Out of memory	Upgrade to larger CPU/GPU tier
Models not loading	Wait for first download (~5 mins)

---

📞 Support Resources

• HF Spaces Docs: https://huggingface.co/docs/hub/spaces
• Docker Spaces: https://huggingface.co/docs/hub/spaces-sdks-docker
• HF Discord: https://discord.gg/hugging-face
• FastAPI Docs: https://fastapi.tiangolo.com/

---

🎯 Success Criteria

Your deployment is successful when:

✅ Space status shows "Running"
✅ Health endpoint returns {"status":"ok"}
✅ You can upload an image via API
✅ Processing completes without errors
✅ Download returns a ZIP file
✅ Files are cleaned up after download

---

🌟 What's Next?

After successful deployment:

1. Share your Space with the world
2. Connect your frontend to the new API
3. Monitor usage in Space analytics
4. Upgrade hardware if needed for production
5. Add to your portfolio - you deployed ML to production!

---

🎊 You're Ready to Deploy!

Everything in the huggingface_deployment folder is configured and ready to go.

Just upload to HuggingFace Spaces and you're live!

Good luck! 🚀

---

Created: November 2025
Based on: Image-Selecter
License: MIT