EdAgent - AI-Powered Career Coaching Assistant

EdAgent is a conversational AI system designed to help beginners learn new skills and advance their careers through personalized guidance, skill assessments, and curated educational content recommendations.

Project Structure

edagent/
├── __init__.py                 # Main package initialization
├── models/                     # Data models and schemas
│   └── __init__.py
├── services/                   # Business logic services
│   └── __init__.py
├── interfaces/                 # Abstract base classes and interfaces
│   ├── __init__.py
│   ├── ai_interface.py
│   ├── user_context_interface.py
│   ├── content_interface.py
│   └── conversation_interface.py
├── api/                        # REST API and WebSocket endpoints
│   └── __init__.py
├── database/                   # Database models and connection management
│   └── __init__.py
└── config/                     # Configuration and environment management
    ├── __init__.py
    ├── settings.py
    └── environment.py

Setup

1. Copy .env.example to .env and configure your API keys:

   cp .env.example .env

2. Install dependencies:

   pip install -r requirements.txt

3. Set up your environment variables in .env:

   • GEMINI_API_KEY: Your Google Gemini API key (Get it here)
   • YOUTUBE_API_KEY: Your YouTube Data API key (Get it here)
   • SECRET_KEY: A secure secret key for session management (generate with openssl rand -hex 32)

Getting API Keys

Google Gemini API Key

1. Visit Google AI Studio
2. Sign in with your Google account
3. Click "Create API Key"
4. Copy the generated key to your .env file

YouTube Data API Key

1. Go to the Google Cloud Console
2. Create a new project or select an existing one
3. Enable the YouTube Data API v3
4. Go to "Credentials" and create an API key
5. Copy the key to your .env file

Features

Core AI Capabilities

• Conversational AI: Natural language interactions powered by Google Gemini
• Skill Assessment: Comprehensive evaluation of technical and soft skills
• Learning Path Generation: Personalized roadmaps based on goals and current skills
• Content Recommendation: Curated resources from YouTube and educational platforms
• Career Coaching: Resume analysis, interview preparation, and career guidance

User Interface

• Streamlit Web App: Comprehensive web interface with interactive components
• Real-time Chat: WebSocket-powered live conversations with EdAgent
• Interactive Dashboards: Progress tracking, analytics, and learning insights
• Mobile-Friendly: Responsive design that works on all devices

Technical Features

• REST API: Complete FastAPI backend with comprehensive endpoints
• WebSocket Support: Real-time bidirectional communication
• Privacy Controls: Comprehensive data management and privacy features
• Docker Support: Containerized deployment with Docker Compose

Quick Start

Option 1: Run Both Backend and Frontend

# Start both FastAPI backend and Streamlit frontend
python run_app.py

Option 2: Run Components Separately

# Terminal 1: Start FastAPI backend
uvicorn main:app --reload

# Terminal 2: Start Streamlit frontend
./scripts/start_frontend.sh

Option 3: Demo Mode (Frontend Only)

# Run Streamlit with mock data (no backend required)
./scripts/start_demo.sh

Access Points

• Streamlit Frontend: http://localhost:8501
• FastAPI Backend: http://localhost:8000
• API Documentation: http://localhost:8000/docs

Configuration

The application uses environment-based configuration with support for:

• Development, testing, staging, and production environments
• Streamlit frontend configuration
• WebSocket and real-time features
• Configurable AI model parameters
• Database connection settings
• Rate limiting and security options
• Logging configuration

Architecture

EdAgent follows a modular architecture with clear separation of concerns:

• Models: Data structures and validation
• Services: Business logic and external integrations
• Interfaces: Abstract contracts for service implementations
• API: HTTP endpoints and WebSocket connections
• Database: Data persistence and migrations
• Config: Environment and application settings

Development

This project follows Python best practices:

• Type hints for all functions and methods
• Async/await for I/O operations
• Comprehensive error handling
• Unit testing with pytest
• Code formatting with black and isort
• Linting with flake8 and mypy

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines on how to contribute to this project.

License

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

Support

If you encounter any issues or have questions, please:

• Check the documentation
• Search existing issues
• Create a new issue if needed

Acknowledgments

• Google Gemini API for conversational AI capabilities
• YouTube Data API for educational content discovery
• The open source community for the amazing tools and libraries that make this project possible