SETUP_CHECKLIST.md

🎉 Setup Complete - Next Steps

✅ What Was Created

1. Model Loading System

• scripts/model_loader.py - Downloads & caches models from HuggingFace Hub
• scripts/upload_models_to_hub.py - Upload script for your 3 models
• model_config.yaml - Model metadata configuration

2. Configuration

• .env.example - Template for environment variables
• .gitignore - Prevents committing large files and secrets

3. Documentation

• MODEL_SETUP.md - Complete setup guide
• README.md - Updated with new architecture

4. App Updates

• app.py - Now loads models from HuggingFace Hub automatically
• requirements.txt - Added huggingface_hub and pyyaml

---

🚀 Your Next Steps (In Order)

Step 1: Create .env File

cd d:\DamageLens
copy .env.example .env

Edit .env in VS Code:

HF_MODEL_REPO=junaid17/damagelens-models
HF_TOKEN=hf_xxxxxxxxxxxxxxxxxxxx    # ← Replace with your actual token
MODEL_CACHE_DIR=./model_cache

Get HF_TOKEN:

1. Go to https://huggingface.co/settings/tokens
2. Click "New token"
3. Choose "Write" permission
4. Copy and paste in .env

Step 2: Install New Dependencies

pip install -r requirements.txt

This adds:

• huggingface_hub - for model downloads
• pyyaml - for config reading

Step 3: Upload Models to HuggingFace

python scripts/upload_models_to_hub.py

This will:

• Create repo: https://huggingface.co/junaid17/damagelens-models
• Upload all 3 models from checkpoints/ folder
• Show progress for each upload

This is one-time setup! After this, you don't need the local checkpoints/ folder.

Step 4: Test the App

python app.py

On first run, it will:

• Download models from HuggingFace (~1.4 GB)
• Cache them locally in model_cache/
• Start the server

On subsequent runs, it will:

• Use cached models
• Start in ~5 seconds

---

📍 How It Works Now

Your Computer
    ↓
[app.py starts]
    ↓
[calls model_loader.py]
    ↓
┌─ Check if model cached locally?
│
├─ Yes → Use cached model ✅ (fast)
│
└─ No → Download from HuggingFace Hub → Cache it → Use it ✅

Benefits:

• ✅ No large files in git repo
• ✅ Easy deployment to Docker/HF Spaces
• ✅ Models shared across multiple servers
• ✅ Version control for models
• ✅ Offline usage after caching

---

📂 File Structure After Setup

DamageLens/
├── .env                           ← Your config (do NOT commit)
├── .env.example                   ← Template (commit this)
├── .gitignore                     ← Prevents committing large files
├── app.py                         ← Now uses model_loader
├── requirements.txt               ← Updated
├── MODEL_SETUP.md                 ← Detailed guide
├── README.md                      ← Updated
├── model_config.yaml              ← Model metadata
├── scripts/
│   ├── model_loader.py            ← NEW: Downloads models
│   ├── upload_models_to_hub.py    ← NEW: Upload script
│   ├── prediction_helper.py       ← Unchanged
│   ├── gradcam.py                 ← Unchanged
│   └── yolo.py                    ← Unchanged
├── checkpoints/                   ← (Optional after upload)
│   ├── best_resnet_model.pt       ← Will be on HuggingFace
│   ├── best_fusion_model.pt       ← Will be on HuggingFace
│   └── damage_detector.pt         ← Will be on HuggingFace
└── model_cache/                   ← AUTO CREATED: Downloaded models
    ├── best_resnet_model.pt       ← Cached after first download
    ├── best_fusion_model.pt       ← Cached after first download
    └── damage_detector.pt         ← Cached after first download

---

🔒 Security Notes

DO NOT commit .env file!

• .gitignore already prevents this
• Contains sensitive HuggingFace token

For Deployment:

• Add HF_TOKEN to platform secrets
• Example (HuggingFace Spaces):
  • Settings → Secrets → Add HF_TOKEN

---

✨ What Changed in App

Before:

resnet_checkpoint = "checkpoints/best_resnet_model.pt"
Resnet_Model = ResnetCarDamagePredictor(resnet_checkpoint, class_map)

After:

from scripts.model_loader import initialize_models
Resnet_Model, Fusion_Model, loader = initialize_models(class_map)

The app now:

• ✅ Automatically downloads models from HuggingFace
• ✅ Caches them locally
• ✅ Requires no changes to prediction endpoints
• ✅ Works offline after first run

---

🆘 Troubleshooting

Issue	Solution
ModuleNotFoundError: huggingface_hub	Run pip install -r requirements.txt
HF_TOKEN not found	Create .env file with token
Repository not found	Check token has write permission
Slow startup	Normal on first run (downloading ~1.4GB)
Out of disk space	Delete model_cache/ and re-download

---

📚 Further Reading

See MODEL_SETUP.md for:

• Detailed troubleshooting
• Docker deployment
• HuggingFace Spaces setup
• Model verification

---

✅ Checklist

• Create .env file with HF_TOKEN
• Run pip install -r requirements.txt
• Run python scripts/upload_models_to_hub.py
• Run python app.py and verify it downloads models
• Test the web UI at http://localhost:8000
• Commit code (without .env and checkpoints/)

---

You're all set! 🎉 The app is now ready to load models from HuggingFace Hub.