Building production-ready AI web applications with modern technology stacks
A production-ready web application that predicts Titanic passenger survival using machine learning models.
graph TB
subgraph "Frontend"
A[React TypeScript SPA]
B[Nginx Reverse Proxy]
end
subgraph "Backend Services"
C[FastAPI Web Backend]
D[FastAPI ML Service]
end
subgraph "Data Layer"
E[PostgreSQL Database]
F[Model Artifacts]
end
A --> B
B --> C
C --> D
C --> E
D --> F
style A fill:#61dafb
style C fill:#009688
style D fill:#009688
style E fill:#336791
- 🤖 5 ML Algorithms: Random Forest, SVM, Decision Tree, KNN, Logistic Regression
- 🔐 JWT Authentication: Secure user registration and login
- 👨💼 Admin Console: Train, evaluate, and manage ML models
- 📱 Mobile-First Design: Responsive across all devices
- 🐳 Containerized: One-command deployment with Docker Compose
- ⚡ Real-time Predictions: Instant survival probability calculations
- 📊 Prediction History: Track and analyze user predictions
- 🎯 Role-Based Access: Anonymous, User, and Admin permission levels
| Category | Technologies |
|---|---|
| Frontend | React 19, TypeScript, Tailwind CSS, Vite |
| Backend | FastAPI, Python 3.13, SQLAlchemy, Alembic |
| ML/AI | scikit-learn, pandas, NumPy |
| Database | PostgreSQL, pgAdmin |
| DevOps | Docker, Docker Compose, GitHub Actions |
| Testing | Pytest, React Testing Library, Playwright |
| Repository | Description | Tech Stack |
|---|---|---|
| docker-compose | 🏠 Main orchestration repository | Docker Compose, Nginx |
| web-frontend | 🎨 React TypeScript frontend | React, TypeScript, Tailwind |
| web-backend | ⚙️ FastAPI web backend | FastAPI, PostgreSQL, JWT |
| model-backend | 🧠 ML inference service | FastAPI, scikit-learn |
Tip
Zero Configuration Deployment - Get the entire application running with a single command!
# Clone the main repository
git clone --recurse-submodules https://github.com/random-iceberg/docker-compose.git
cd docker-compose
# Start all services
docker compose up --build -d
# Access the application
open http://localhost:8080Note
This project was developed as part of Software Engineering coursework at Deggendorf Institute of Technology (DIT) and demonstrates enterprise-level development practices.
What makes this project special:
- ✅ Production-Ready: Full CI/CD pipeline, automated testing, health checks
- ✅ Scalable Architecture: Microservices design with clear separation of concerns
- ✅ Security First: JWT authentication, input validation, SQL injection prevention
- ✅ Best Practices: Clean code, comprehensive documentation, test coverage
- ✅ Modern Stack: Latest versions of React, FastAPI, Python, and PostgreSQL
Team Random Iceberg - Software Engineering students specializing in full-stack development and machine learning applications.
| Role | Responsibilities |
|---|---|
| Full-Stack Developers | Frontend React development, UI/UX design, integration |
| Backend Engineers | FastAPI development, database design, authentication |
| ML Engineers | Model training, inference optimization, data preprocessing |
| DevOps Engineers | Containerization, CI/CD, deployment automation |
- Lines of Code: 12,000+
- Test Coverage: 80%+
- Docker Services: 4 containerized microservices
- API Endpoints: 20+ RESTful endpoints
- Browser Support: Chrome 119+, Firefox 122+, Safari 16.1+
- Response Time: <150ms prediction latency
Important
Successfully completed as part of AIN-B Software Engineering coursework under Prof. Dr. Christoph Schober at Deggendorf Institute of Technology.
This project is licensed under the MIT License - see the LICENSE file for details.
Built with ❤️ by Team Random Iceberg
Showcasing modern web development and machine learning integration