DEPLOYMENT_GUIDE.md

# 🚀 Deployment Guide
## AI-Powered Shipment Route Optimization System
**Created by: Zayeem Khateeb**

## 📋 Deployment Options

### Option 1: Docker Deployment (Recommended)

#### Prerequisites
- Docker and Docker Compose installed
- 4GB RAM minimum
- 10GB disk space

#### Steps
```bash
# 1. Build and run with Docker Compose
docker-compose up -d

# 2. Access your application
http://localhost:8050

# 3. View logs
docker-compose logs -f dashboard

# 4. Stop deployment
docker-compose down
```

### Option 2: Cloud Deployment (Heroku)

#### Prerequisites
- Heroku CLI installed
- Git repository

#### Steps
```bash
# 1. Login to Heroku
heroku login

# 2. Create Heroku app
heroku create your-shipment-optimizer

# 3. Set environment variables
heroku config:set FLASK_ENV=production
heroku config:set SECRET_KEY=your-secret-key

# 4. Deploy
git add .
git commit -m "Deploy AI Shipment Optimizer"
git push heroku main

# 5. Open your app
heroku open
```

### Option 3: Vercel Deployment

#### Prerequisites
- Vercel CLI or GitHub integration

#### Steps
```bash
# 1. Install Vercel CLI
npm i -g vercel

# 2. Deploy
vercel

# 3. Follow prompts and get your URL
```

### Option 4: Railway Deployment

#### Steps
```bash
# 1. Install Railway CLI
npm install -g @railway/cli

# 2. Login
railway login

# 3. Deploy
railway up

# 4. Get your URL
railway domain
```

### Option 5: Local Production Server

#### Steps
```bash
# 1. Install production server
pip install gunicorn

# 2. Run production server
gunicorn -w 4 -b 0.0.0.0:8050 deploy:app

# 3. Access at http://localhost:8050
```

## 🔧 Environment Variables

Create `.env` file with:
```
# Database
DATABASE_URL=your_database_url

# API Keys
OPENWEATHER_API_KEY=your_weather_api_key
GOOGLE_MAPS_API_KEY=your_maps_api_key

# Security
SECRET_KEY=your-secret-key-here

# App Config
FLASK_ENV=production
PORT=8050
```

## 📊 Performance Optimization

### For Production:
1. **Enable caching**: Redis configured in docker-compose
2. **Database optimization**: PostgreSQL with proper indexing
3. **Load balancing**: Multiple worker processes
4. **Monitoring**: Built-in health checks

### Scaling:
- **Horizontal**: Multiple container instances
- **Vertical**: Increase CPU/RAM allocation
- **Database**: Separate database server

## 🔒 Security Checklist

- ✅ Environment variables for secrets
- ✅ Non-root Docker user
- ✅ HTTPS in production (handled by platform)
- ✅ Input validation and sanitization
- ✅ Database connection security

## 📈 Monitoring

### Health Check Endpoint:
```
GET /health
```

### Metrics Available:
- Total shipments processed
- ML model accuracy
- Response times
- Error rates

## 🆘 Troubleshooting

### Common Issues:

1. **Port already in use**
   ```bash
   # Change port in docker-compose.yml or .env
   PORT=8051
   ```

2. **Database connection failed**
   ```bash
   # Check DATABASE_URL in .env
   # Ensure database is running
   ```

3. **ML model training fails**
   ```bash
   # Check data format
   # Increase memory allocation
   ```

## 📞 Support

For deployment issues:
1. Check logs: `docker-compose logs dashboard`
2. Verify environment variables
3. Ensure all dependencies are installed
4. Check system requirements

Your AI-Powered Shipment Route Optimization System is ready for production! 🎉