docs/README_DEPLOYMENT.md

🚀 Quick Deployment Guide

🎯 Choose Your Deployment Method

🟢 Option 1: Quick Demo (Recommended for Interviews)

Perfect for demonstrations and quick testing.

Windows:

# Double-click or run:
start_demo.bat

Linux/Mac:

./start_demo.sh

What it does:

• Starts backend on port 8001
• Starts frontend on port 8501
• Opens browser automatically
• Shows progress in separate windows

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🟡 Option 2: Docker Deployment (Recommended for Production)

Professional containerized deployment.

Prerequisites:

• Install Docker Desktop

Windows:

# Double-click or run:
deploy_docker.bat

Linux/Mac:

./deploy_docker.sh

What it does:

• Builds Docker containers
• Sets up networking
• Provides health checks
• Includes nginx reverse proxy (optional)

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📊 Check Deployment Status

Windows:

check_status.bat

Linux/Mac:

curl http://localhost:8001/    # Backend health
curl http://localhost:8501/    # Frontend health

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🔗 Access Your Application

Once deployed, access these URLs:

• 🎨 Frontend UI: http://localhost:8501
• ⚡ Backend API: http://localhost:8001
• 📚 API Documentation: http://localhost:8001/docs

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🛑 Stop Services

Quick Demo:

• Windows: Run stop_services.bat or close command windows
• Linux/Mac: Press Ctrl+C in terminal

Docker:

docker-compose down

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🆘 Troubleshooting

Common Issues:

1. Port already in use:

   # Kill existing processes
   taskkill /f /im python.exe     # Windows
   pkill -f python                # Linux/Mac
   

2. Models not loading:

   • Check if models/indictrans2/ directory exists
   • Ensure models were downloaded properly
   • Check backend logs for errors

3. Frontend can't connect to backend:

   • Verify backend is running on port 8001
   • Check frontend/app.py has correct API_BASE_URL

4. Docker issues:

   # Check Docker status
   docker ps
   docker-compose logs
   
   # Reset Docker
   docker-compose down
   docker system prune -f
   docker-compose up --build
   

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🔧 Configuration

Environment Variables:

Create .env file in root directory:

MODEL_TYPE=indictrans2
MODEL_PATH=models/indictrans2
DEVICE=cpu
DATABASE_PATH=data/translations.db

For Production:

• Copy .env.production to .env
• Update database settings
• Configure CORS origins
• Set up monitoring

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📈 Performance Tips

1. Use GPU if available:

   DEVICE=cuda  # in .env file
   

2. Increase memory for Docker:

   • Docker Desktop → Settings → Resources → Memory: 8GB+

3. Monitor resource usage:

   docker stats           # Docker containers
   htop                   # System resources
   

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🎉 Success Indicators

✅ Deployment Successful When:

• Backend responds at http://localhost:8001
• Frontend loads at http://localhost:8501
• Can translate "Hello" to Hindi
• API docs accessible at http://localhost:8001/docs
• No error messages in logs

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🆘 Need Help?

1. Check the logs:

   • Quick Demo: Look at command windows
   • Docker: docker-compose logs -f

2. Verify prerequisites:

   • Python 3.11+ installed
   • All dependencies in requirements.txt
   • Models downloaded in correct location

3. Test individual components:

   • Backend: curl http://localhost:8001/
   • Frontend: Open browser to http://localhost:8501

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🎯 For Interview Demos: Use Quick Demo option - it's fastest and shows everything working!