CourseWagon is an enterprise-grade educational platform that leverages generative AI to facilitate dynamic course content generation and comprehensive learning management. The platform integrates modern web technologies with advanced AI capabilities to deliver a scalable, secure, and user-centric educational experience.

• AI-Powered Content Generation: Automatically generate course subjects, chapters, topics, and detailed content using Google Gemini AI
• Comprehensive Course Management: Create and organize courses with hierarchical structure (Courses Subjects Chapters Topics Content)
• Real-time Authentication: Secure user authentication with Firebase Auth and JWT tokens
• Cloud Storage Integration: Store images and media using Azure Blob Storage and Firebase Storage
• Responsive UI: Modern, responsive interface built with Angular 19 and Tailwind CSS
• Admin Dashboard: Comprehensive admin panel for managing users, courses, and testimonials
• Mathematical Content Support: Render mathematical equations using KaTeX and MathJax
• Diagram Support: Create and display diagrams using Mermaid.js
• Email Notifications: Automated email system for password resets and user notifications

• Framework: Angular 19
• Styling: Tailwind CSS 4.1
• Authentication: Firebase Auth
• Markdown: ngx-markdown with Prism.js syntax highlighting
• Math Rendering: KaTeX, MathJax
• Diagrams: Mermaid.js
• Icons: Font Awesome

• Framework: FastAPI (Python)
• Database: MySQL with SQLAlchemy ORM
• AI Integration: Google Gemini AI
• Storage: Azure Blob Storage, Firebase Storage
• Authentication: JWT tokens with Firebase Admin SDK
• Email Service: Mailgun
• Deployment: Google Cloud Run / Microsoft Azure

coursewagon/
 angular-client/          # Frontend Angular application
    src/
       app/            # Angular components and services
       environments/   # Environment configurations
       styles.css      # Global styles
    package.json        # Frontend dependencies
    angular.json        # Angular configuration

 python-server/          # Backend FastAPI application
    app.py             # Main FastAPI application
    models/            # Database models
    routes/            # API endpoints
    services/          # Business logic
    repositories/      # Data access layer
    utils/             # Helper utilities
    migrations/        # Database migrations
    requirements.txt   # Backend dependencies

 CLAUDE.md              # AI assistant instructions
 .env.example           # Environment variables template

• Node.js (v18 or higher)
• Python 3.10+
• MySQL database
• Firebase project (for authentication)
• Azure Storage account (optional)
• Google Gemini API key

# Navigate to frontend directory
cd angular-client

# Install dependencies
npm install

# Configure environment variables
# Edit src/environments/environment.ts with your API endpoints

# Run development server
npm start

The application will be available at http://localhost:4200

# Navigate to backend directory
cd python-server

# Install uv package manager (recommended)
pip install uv

# Install dependencies
uv pip install -r requirements.txt

# Copy and configure environment variables
cp .env.example .env
# Edit .env with your configuration

# Run development server
uvicorn app:app --reload

The API will be available at http://localhost:8000

Copy .env.example to .env in the python-server directory and configure:

• Database: MySQL connection details
• API Keys: Gemini AI, Firebase, Azure Storage
• JWT: Secret keys and token expiration
• Email: Mailgun configuration
• Storage: Azure Blob Storage credentials

1. Create a Firebase project
2. Enable Authentication
3. Download service account credentials
4. Place in python-server/utils/
5. Update Firebase configuration in Angular app

Once the backend is running, access the interactive API documentation at:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

cd angular-client
npm test

cd python-server
python -m pytest tests/

cd python-server/scripts
./test-local.sh

cd angular-client
npm run build
firebase deploy

cd python-server
gcloud run deploy coursewagon-api \
  --source . \
  --region asia-southeast1 \
  --allow-unauthenticated

• npm start - Run development server
• npm run build - Build for production
• npm test - Run unit tests

• uvicorn app:app --reload - Run development server
• ./scripts/test-local.sh - Test with local Azure storage
• ./scripts/deploy-complete.sh - Deploy to production

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

This project is licensed under the MIT License - see the LICENSE file for details.

• Google Gemini AI for content generation
• Firebase for authentication and hosting
• Azure for cloud storage
• Angular and FastAPI communities

For support, email [email protected] or open an issue in the GitHub repository.

• Live Application: https://www.coursewagon.live