ProfAI MIT - AI-Powered Learning Platform

An intelligent, AI-driven learning platform that creates personalized educational experiences with integrated YouTube video content and ChromaDB knowledge retrieval.

🚀 Features

Core Learning Features

• AI-Generated Course Structures: Automatically creates 7x10x10 course structures (7 modules, 10 submodules each, 10 lessons each)
• Intelligent Lesson Expansion: AI-powered detailed lesson plans with comprehensive learning objectives
• Interactive Code Editor: Built-in code editor with syntax highlighting for Python, C, and C++
• Real-time Code Evaluation: AI-powered code analysis and guidance without revealing solutions
• Voice-Based Learning Assessment: Record verbal summaries for AI evaluation of understanding

YouTube Integration (NEW!)

• Smart Video Discovery: AI generates 3 search prompts based on lesson content
• YouTube Video Search: Finds relevant educational videos using YouTube Data API
• Automatic Transcription: Downloads and transcribes video content
• AI-Generated Summaries: Creates concise summaries of video content
• Relevance Explanations: AI explains why each video is relevant to the lesson
• Seamless UI Integration: Videos displayed alongside lesson content

Knowledge Management

• ChromaDB Integration: Intelligent knowledge retrieval for personalized learning
• Progress Tracking: Comprehensive progress monitoring and analytics
• Adaptive Learning: Course adaptation based on student performance

🛠️ Technology Stack

• Frontend: Streamlit (Python web framework)
• AI Models: Ollama (via ngrok endpoint)
• Knowledge Base: ChromaDB
• Video Processing: YouTube Data API + youtube-transcript-api
• Code Editor: Streamlit-Ace
• Storage: Local file system with JSON metadata

📦 Installation

1. Clone the repository:

   git clone <your-repo-url>
   cd primary_code_hackathon

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables:

   # Copy the example environment file
   cp env.example .env
   
   # Edit .env file with your actual API keys
   # You'll need:
   # - ElevenLabs API key for voice features
   # - YouTube Data API key for video integration
   # - ngrok endpoint for Ollama server

4. Run the setup script (optional):

   python setup.py

5. Run the application:

   streamlit run app.py

🎯 Usage

Creating a New Course

1. Navigate to "Create Course" in the main menu
2. Enter your learning topic (e.g., "learn C++", "learn web development")
3. The AI will generate a comprehensive course structure
4. Review and customize the generated content

Taking a Course

1. Select a course from the course map
2. Click "Expand" on any lesson to generate detailed content
3. NEW: YouTube videos will automatically be processed and displayed
4. Use the interactive code editor to practice
5. Record voice summaries for AI evaluation

YouTube Video Features

• Videos are automatically discovered based on lesson content
• Each video includes:
  • AI-generated summary
  • Relevance explanation
  • Direct YouTube link
  • Channel information
• Videos are displayed in both course map and lesson workspace views

🔧 Configuration

API Keys Required

• YouTube Data API: For video search functionality
• Ollama Endpoint: For AI model interactions (via ngrok)

ChromaDB Setup

The system uses ChromaDB for intelligent knowledge retrieval. Ensure ChromaDB is properly configured for optimal performance.

📁 Project Structure

primary_code_hackathon/
├── app.py                          # Main Streamlit application
├── requirements.txt                # Python dependencies
├── README.md                       # This file
├── .gitignore                      # Git ignore rules
├── src/
│   ├── services/
│   │   ├── course_service.py       # Course generation and management
│   │   ├── course_storage.py       # File system storage operations
│   │   ├── lesson_expander.py      # AI lesson plan expansion
│   │   ├── youtube_service.py      # YouTube video processing (NEW!)
│   │   ├── ollama_client.py        # AI model communication
│   │   ├── chroma_store.py         # ChromaDB knowledge retrieval
│   │   └── language_service.py     # Language-specific utilities
│   └── utils/                      # Utility functions
└── courses/                        # Generated course content (gitignored)

🎬 YouTube Integration Details

How It Works

1. Lesson Expansion: When a lesson is expanded, the AI generates 3 search prompts
2. Video Discovery: YouTube Data API searches for videos matching each prompt
3. Content Processing: Videos are transcribed and summarized by AI
4. Storage: All data is saved locally with the lesson content
5. Display: Videos appear in the UI with summaries and explanations

Video Processing Pipeline

Lesson Plan → AI Search Prompts → YouTube Search → Transcription → AI Summary → UI Display

🤝 Contributing

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

📝 License

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

🙏 Acknowledgments

• Streamlit for the amazing web framework
• Ollama for local AI model hosting
• ChromaDB for vector database capabilities
• YouTube Data API for video discovery
• youtube-transcript-api for video transcription

🆕 Recent Updates

YouTube Video Integration (Latest)

• ✅ AI-powered video discovery based on lesson content
• ✅ Automatic video transcription and summarization
• ✅ Seamless UI integration with lesson content
• ✅ Relevance explanations for each video
• ✅ Local storage of video metadata and transcripts

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