DEPLOYMENT.md

Deployment Guide

Docker (Recommended)

Quick Start

1. Copy the environment template and configure:

   cp backend/.env.example backend/.env
   # Edit backend/.env and fill in your values

2. Build and run all services:

   docker compose up --build

3. Access the services:

   • Backend API: http://localhost:8000
   • Frontend: http://localhost:5173
   • API Documentation: http://localhost:8000/docs

The backend and frontend run on a shared internal network (ghostid-net) and expose the specified ports to your host machine.

---

Backend → Railway

1. Push this repo to GitHub (gostid)
2. Railway → New Project → Deploy from GitHub
3. Root directory: backend/
4. Start command: uvicorn main:app --host 0.0.0.0 --port $PORT
5. Environment variables from backend/.env.example
6. After Kaggle training, ensure backend/ml/ghostid_encoder.onnx is committed or uploaded

Frontend → Vercel

1. Import repo on Vercel
2. Root directory: frontend/
3. Build: npm run build · Output: dist/
4. Env: VITE_API_URL=https://your-backend.up.railway.app

Local Development

# Terminal 1 — API
cd backend && pip install -r requirements.txt && python main.py

# Terminal 2 — UI
cd frontend && npm install && npm run dev

Post-Training Checklist

• Run ml/notebooks/ghostid_v3_training.ipynb on Kaggle
• Copy ghostid_encoder.onnx + scaler_params.json → backend/ml/
• Copy analysis PNGs → ml/analysis/
• Re-run CI — ONNX validation job should pass