AI Learning Assistant

An intelligent learning assistant that helps students study by uploading PDF materials, asking questions, and tracking their learning progress using AI-powered analysis.

Features

• 📚 PDF Upload & Processing: Upload study materials and automatically extract text chunks
• 🤖 AI-Powered Q&A: Ask questions about your uploaded materials with difficulty-based responses
• 📊 Progress Tracking: Monitor your learning progress across different topics
• 🧠 Forgetting Curve: Get intelligent revision suggestions based on spaced repetition
• 📈 Mastery Assessment: Track your mastery levels for different subjects
• 🎯 Misconception Detection: Identify and correct misunderstandings in your explanations

Supported Topics

• Data Structures & Algorithms (DSA)
• Operating Systems (OS)
• Database Management Systems (DBMS)
• Computer Networks (CN)
• Object-Oriented Programming (OOP)

Tech Stack

Backend

• FastAPI: Modern, fast web framework for building APIs
• Google Gemini AI: Advanced language model for intelligent responses
• Sentence Transformers: Semantic similarity for relevant content retrieval
• PDFPlumber: PDF text extraction
• Scikit-learn: Machine learning for mastery prediction

Frontend

• HTML5/CSS3/JavaScript: Modern web technologies
• Tailwind CSS: Utility-first CSS framework
• Font Awesome: Beautiful icons

Installation

Prerequisites

• Python 3.8+
• Google Gemini API Key

Backend Setup

1. Clone the repository

   git clone <repository-url>
   cd SDP/Backend

2. Create virtual environment

   python -m venv venv
   # Windows
   venv\Scripts\activate
   # Unix/MacOS
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Set up environment variables

   # Create a .env file in the Backend directory
   echo "GEMINI_API_KEY=your_actual_api_key_here" > .env

   Or get a free API key from Google AI Studio

5. Create necessary directories

   mkdir -p data/uploads data/extracted_text

6. Run the backend server

   uvicorn main:app --reload --host 0.0.0.0 --port 8000

Frontend Setup

1. Navigate to frontend directory

   cd ../Frontend

2. Serve the frontend

   # Using Python's built-in server
   python -m http.server 3000
   
   # Or using Node.js if you have it installed
   npx serve . -p 3000

Usage

1. Start both servers:

   • Backend: http://localhost:8000
   • Frontend: http://localhost:3000

2. Upload Study Materials:

   • Navigate to the Upload section
   • Select a PDF file containing your study materials
   • Upload and wait for processing

3. Ask Questions:

   • Go to the Chat section
   • Select the subject and difficulty level
   • Type your question and get AI-powered answers

4. Track Progress:

   • Visit the Progress section to see your learning statistics
   • Get revision suggestions based on the forgetting curve

API Endpoints

Upload

• POST /upload - Upload and process PDF files

Q&A

• POST /ask - Ask questions about uploaded materials
• POST /explain - Get feedback on your explanations

Progress

• GET /progress - Get learning progress and forgetting curve status
• GET /session-report - Generate session summary

Project Structure

SDP/
├── Backend/
│   ├── api/
│   │   └── routes.py          # API endpoints
│   ├── services/
│   │   ├── llm_service.py     # AI interactions
│   │   ├── pdf_service.py     # PDF processing
│   │   ├── progress_service.py # Progress tracking
│   │   ├── memory_service.py  # Session history
│   │   ├── semantic_service.py # Text similarity
│   │   ├── web_service.py     # Web fallback
│   │   └── ml/
│   │       ├── mastery_model.py    # Mastery prediction
│   │       └── forgetting_model.py # Forgetting curve
│   ├── utils/
│   │   └── text_utils.py      # Text processing utilities
│   ├── data/                  # Storage directory
│   ├── config.py              # Configuration
│   ├── main.py                # FastAPI application
│   └── requirements.txt       # Dependencies
└── Frontend/
    ├── index.html             # Main web interface
    └── script.js              # Frontend logic

Configuration

The application can be configured through environment variables and the config.py file:

• GEMINI_API_KEY: Your Google Gemini API key (required)
• CHUNK_SIZE: Text chunk size for processing (default: 500)
• MAX_CHUNKS_PER_QUERY: Maximum chunks to consider per query (default: 3)

Troubleshooting

Common Issues

1. API Key Error: Make sure your Gemini API key is valid and set in the .env file
2. PDF Processing Error: Ensure the PDF is text-based (not scanned images)
3. CORS Issues: The backend is configured to allow all origins for development
4. Memory Issues: For large PDFs, consider reducing the chunk size

Getting Help

• Check the browser console for frontend errors
• Check the backend terminal for API errors
• Ensure all dependencies are properly installed
• Verify your API key has sufficient quota

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

License

This project is licensed under the MIT License.

Future Enhancements

• Multi-language support
• More subject areas
• Advanced analytics dashboard
• Collaborative study features
• Mobile app
• Integration with learning management systems