Adaptive Learning Platform

A modern, AI-powered learning management system built with FastAPI and React, designed to provide personalized educational experiences through intelligent content delivery and assessment.

🚀 Features

• AI-Powered Learning: Intelligent content recommendations and personalized learning paths
• User Authentication: Secure authentication system
• Interactive Content: Rich multimedia learning materials and assessments
• Responsive Design: Mobile-first design with Tailwind CSS
• RESTful API: Comprehensive REST API built with FastAPI
• Database Integration: Efficient data management with SQLAlchemy

🛠️ Tech Stack

Backend

• FastAPI - Modern, fast web framework for building APIs
• Python 3.8+ - Core programming language
• SQLAlchemy - SQL toolkit and ORM
• Pydantic - Data validation using Python type annotations
• Authentication - JWT-based authentication system

Frontend

• React - Frontend JavaScript library
• Tailwind CSS - Utility-first CSS framework
• Vite - Fast build tool and development server
• Modern JavaScript - ES6+ features

Development Tools

• Vite - Development server and build tool
• PostCSS - CSS processing
• ESLint/Prettier - Code formatting and linting

📋 Prerequisites

Before running this application, make sure you have:

• Python 3.8+ installed
• Node.js 16+ and npm/yarn installed
• Git for version control

⚡ Quick Start

1. Clone the Repository

git clone https://github.com/CodeMosaic7/learning-potato
cd learning-potato

2. Backend Setup

# Navigate to backend directory
cd ai-learning-backend

# Create virtual environment
python -m venv venv

# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Run database migrations (if applicable)
python -m alembic upgrade head

# Start the FastAPI server
uvicorn app.main:app --reload

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

• API Documentation: http://localhost:8000/docs
• Alternative Docs: http://localhost:8000/redoc

3. Frontend Setup

# Navigate to frontend directory (from root)
cd ai-learning-frontend

# Install dependencies
npm install

# Start development server
npm run dev

The frontend will be available at http://localhost:5173

📁 Project Structure

learning-potato/
├── ai-learning-backend/
│   ├── app/
│   │   ├── __pycache__/
│   │   ├── authentication/
│   │   ├── router/
│   │   └── services/
│   ├── crud.py
│   ├── db.py
│   ├── dependencies.py
│   ├── main.py
│   ├── models.py
│   ├── schemas.py
│   └── test.py
├── ai-learning-frontend/
│   ├── node_modules/
│   ├── public/
│   ├── src/
│   ├── eslint.config.js
│   ├── index.html
│   ├── package.json
│   ├── postcss.config.js
│   ├── tailwind.config.js
│   └── vite.config.js
├── .gitignore
├── auth_app.db
├── notes.txt
└── requirements.txt

🔧 Configuration

Environment Variables

Create a .env file in the backend directory:

# Database
DATABASE_URL=sqlite:///./auth_app.db

# JWT Settings
SECRET_KEY=your-secret-key-here
ALGORITHM=HS256
ACCESS_TOKEN_EXPIRE_MINUTES=30

# AI/ML Settings
AI_MODEL_ENDPOINT=your-ai-endpoint
AI_API_KEY=your-ai-api-key

# CORS Settings
ALLOWED_ORIGINS=http://localhost:5173

🚀 API Endpoints

Authentication

• POST /auth/register - User registration
• POST /auth/login - User login
• POST /auth/refresh - Refresh access token

Learning Content

• GET /courses - Get all courses
• GET /courses/{id} - Get specific course
• POST /courses - Create new course
• PUT /courses/{id} - Update course
• DELETE /courses/{id} - Delete course

AI Features

• POST /ai/recommendations - Get personalized recommendations
• POST /ai/assessment - AI-powered assessment
• GET /ai/progress - Get learning analytics

🧪 Testing

Backend Tests

cd ai-learning-backend
python -m pytest test.py -v

Frontend Tests

cd ai-learning-frontend
npm run test

🚢 Deployment

Backend Deployment

1. Install production dependencies
2. Set environment variables
3. Run database migrations
4. Start with a production ASGI server:

pip install uvicorn[standard]
uvicorn app.main:app --host 0.0.0.0 --port 8000 --workers 4

Frontend Deployment

npm run build
# Deploy the dist/ folder to your hosting service

📊 Database Schema

The platform uses SQLAlchemy models for:

• Users: User accounts and profiles
• Assessments: Quizzes and evaluations

🤖 AI Integration

The platform includes AI-powered features:

• Personalized Recommendations: Content suggestions based on learning patterns
• Adaptive Assessment: Dynamic difficulty adjustment
• Learning Analytics: Progress insights and performance prediction
• Content Generation: AI-assisted content creation

🔒 Security Features

• JWT-based authentication
• Password hashing with bcrypt
• CORS protection
• Input validation with Pydantic
• SQL injection prevention
• Rate limiting (configurable)

Development Guidelines

• Follow PEP 8 for Python code
• Use ESLint configuration for JavaScript
• Write tests for new features
• Update documentation as needed

📝 License

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

🔄 Changelog

Version 1.0.0

• Initial release with core learning platform features
• AI-powered recommendations
• User authentication and authorization
• Course management system
• Progress tracking and analytics

🙏 Acknowledgments

• FastAPI community for excellent documentation
• React and Vite teams for modern development tools
• Open source contributors and maintainers

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Screenshots

Chatbot

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Built with ❤️ using FastAPI and React