Week 3: AI-Powered ETA Prediction

• Machine Learning Model: Random Forest model for delivery time prediction
• Real-time Tracking: GPS-based position tracking and distance calculation
• Proximity Notifications: Smart notifications based on delivery proximity
• Historical Data Analysis: Learn from past delivery patterns

Analytics & Reporting

• Metabase Integration: Professional dashboards and reporting
• Performance Metrics: Driver performance scoring and analytics
• Trend Analysis: Historical data visualization and insights

🏗️ Architecture

fullstack-ai-dashboard/
├── ai-service/                  # Flask microservice with ML model
│   ├── app.py                  # Main Flask application
│   ├── model.pkl               # Trained ML model
│   ├── requirements.txt        # Python dependencies
│   └── Dockerfile
│
├── backend/                    # Laravel API backend
│   ├── app/Models/             # Driver, Violation, Feedback models
│   ├── app/Http/Controllers/   # API controllers
│   ├── database/migrations/    # Database schema
│   ├── routes/api.php          # API routes
│   └── Dockerfile
│
├── frontend/                   # Next.js + TypeScript frontend
│   ├── app/                    # App router pages
│   ├── components/             # Reusable components
│   └── Dockerfile
│
├── metabase/                   # Analytics platform
│   └── docker-compose.yml
│
└── docker-compose.yml          # Main orchestrator

🛠️ Technology Stack

Backend

• Laravel 11: PHP framework for API development
• PostgreSQL: Primary database for driver and logistics data
• Redis: Caching and background job processing

AI Service

• Flask: Python web framework
• Scikit-learn: Machine learning library
• Pandas/NumPy: Data processing
• Geopy: Geographic calculations

Frontend

• Next.js 14: React framework with App Router
• TypeScript: Type-safe JavaScript
• Tailwind CSS: Utility-first CSS framework
• Radix UI: Accessible component library

Analytics

• Metabase: Business intelligence and dashboards
• PostgreSQL: Data warehouse for analytics

DevOps

• Docker: Containerization
• Docker Compose: Multi-container orchestration

📦 Installation & Setup

Prerequisites

• Docker and Docker Compose installed
• Git for version control

Quick Start

1. Clone the repository

   git clone <repository-url>
   cd fullstack-ai-dashboard

2. Environment Setup

   # Copy environment file for Laravel backend
   cp backend/.env.example backend/.env
   
   # Update database credentials in backend/.env
   DB_CONNECTION=pgsql
   DB_HOST=postgres
   DB_PORT=5432
   DB_DATABASE=logistics_db
   DB_USERNAME=postgres
   DB_PASSWORD="password"

3. Start all services

   docker-compose up -d

4. Initialize the database

   # Run migrations
   docker-compose exec backend php artisan migrate
   
   # Seed sample data
   docker-compose exec backend php artisan db:seed

5. Train the AI model

   # Train the ETA prediction model
   curl -X POST http://localhost:5000/train_model

Service Access

Service	URL	Description
Frontend	http://localhost:3000	Main dashboard
Backend API	http://localhost:8000	Laravel API
AI Service	http://localhost:5000	ML prediction service
Metabase	http://localhost:3001	Analytics dashboard
PostgreSQL	localhost:5432	Database
Redis	localhost:6379	Cache/Queue

🎯 Usage Guide

Driver Management

1. View All Drivers: Navigate to /drivers to see the driver list
2. Driver Profile: Click on any driver to view detailed profile
3. Performance Tracking: Monitor drug tests, violations, and feedback
4. Credential Management: Track document validity and expiry dates

ETA Prediction

1. Get ETA: Send POST request to /ai/predict_eta with coordinates

   curl -X POST http://localhost:5000/predict_eta \
     -H "Content-Type: application/json" \
     -d '{
       "current_lat": 14.5995,
       "current_lng": 120.9842,
       "dropoff_lat": 14.6091,
       "dropoff_lng": 121.0223
     }'

2. Response: Get ETA in minutes and proximity-based message

Analytics Dashboard

1. Setup Metabase: First-time setup at http://localhost:3001
2. Connect Database: Use PostgreSQL connection details
3. Create Dashboards: Build custom analytics dashboards

🔧 API Documentation

Driver Endpoints

Method	Endpoint	Description
GET	/api/drivers	List all drivers
POST	/api/drivers	Create new driver
GET	/api/drivers/{id}	Get driver details
GET	/api/drivers/{id}/profile	Get driver profile with analytics
PUT	/api/drivers/{id}	Update driver
DELETE	/api/drivers/{id}	Delete driver

AI Service Endpoints

Method	Endpoint	Description
GET	/health	Health check
POST	/train_model	Train ETA prediction model
POST	/predict_eta	Predict delivery ETA
POST	/driver_analytics	Get driver performance analytics

📊 Database Schema

Core Tables

• drivers: Driver personal information
• drug_test_results: Drug testing history
• violations: Traffic violations and fines
• feedback: Customer feedback and ratings
• credentials: Driver licenses and certifications
• infractions: Work-related incidents

Relationships

• One driver has many: drug tests, violations, feedback, credentials, infractions
• Comprehensive foreign key constraints for data integrity

🚀 Development

Local Development Setup

1. Backend Development

   cd backend
   composer install
   php artisan serve --host=0.0.0.0 --port=8000

2. Frontend Development

   cd frontend
   npm install
   npm run dev

3. AI Service Development

   cd ai-service
   pip install -r requirements.txt
   python app.py

Adding New Features

1. Database Changes: Create migrations in backend/database/migrations/
2. API Endpoints: Add controllers in backend/app/Http/Controllers/
3. Frontend Pages: Create components in frontend/app/
4. AI Features: Extend ai-service/app.py

🤝 Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Make your changes and commit: git commit -m 'Add feature'
4. Push to the branch: git push origin feature-name
5. Submit a pull request

📄 License

This project is open-source and available under the MIT License.

🆘 Troubleshooting

Common Issues

1. Database Connection Error

   • Ensure PostgreSQL container is running
   • Check database credentials in .env

2. Frontend Build Errors

   • Run npm install to ensure dependencies are installed
   • Check Node.js version compatibility

3. AI Model Training Fails

   • Verify Python dependencies are installed
   • Check Redis connection for caching

4. Docker Issues

   • Run docker-compose down && docker-compose up -d to restart
   • Check Docker logs: docker-compose logs [service-name]

Support

For technical support, please create an issue in the repository with:

• Detailed error description
• Steps to reproduce
• System information
• Docker logs if applicable

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