eduMe_AI - AI-Powered Learning Platform

🎓 Overview

eduMe_AI is a comprehensive AI-powered learning platform that combines cutting-edge AI technology with educational tools. The platform features a SadTalker API for creating talking avatars from images and audio, along with a modern web interface for various learning activities.

🌟 Key Features

• AI Avatar Generation: Create talking avatars using SadTalker technology
• Career Quiz System: Interactive career assessment and guidance
• AI Tutoring: Personalized learning assistance
• Exam Preparation: Comprehensive study tools and resources
• Skill Hub: Learning path recommendations and skill development
• Rewards System: Gamified learning experience
• Leaderboard: Competitive learning environment

🏗️ Architecture

The project consists of two main components:

Backend (SadTalker API)

• FastAPI server with SadTalker integration
• AWS S3 integration for file storage
• Docker support for easy deployment
• CUDA support for GPU acceleration

Frontend (Learning Platform)

• Next.js 15 with TypeScript
• Tailwind CSS for modern styling
• Firebase for authentication and data storage
• Framer Motion for smooth animations
• Radix UI components for accessibility

🚀 Quick Start

Prerequisites

• Python 3.8+
• Node.js 18+
• CUDA-compatible GPU (optional but recommended)
• Docker (for containerized deployment)

Backend Setup

Manual Installation

1. Clone the repository

git clone <your-repo-url>
cd eduMe_AI/backend

2. Create and activate conda environment

conda create -n sadtalker python=3.8
conda activate sadtalker

3. Install PyTorch with CUDA support

pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113

4. Install other dependencies

conda install ffmpeg
pip install -r requirements.txt

5. Set up environment variables

cp .env.example .env
# Edit .env with your AWS credentials and other settings

6. Download SadTalker models

bash scripts/download_models.sh

7. Start the API server

uvicorn --host "0.0.0.0" --port "8000" api:app

Docker Installation

1. Build the Docker image

cd backend
docker build -t sadtalker .

2. Run with GPU support

docker run --gpus=all --rm -p 8000:8000 -v ./.env:/sadtalker/.env -d --name sadtalker sadtalker

Frontend Setup

1. Navigate to frontend directory

cd ../frontend

2. Install dependencies

npm install

3. Set up environment variables

# Create .env.local with your Firebase configuration

4. Start development server

npm run dev

📚 API Documentation

SadTalker API Endpoints

Generate Talking Avatar

POST /generate/

Request Body:

{
  "image_link": "https://example.com/image.jpg",
  "audio_link": "https://example.com/audio.wav",
  "s3_object_path": "uploads/avatar/"
}

Response:

{
  "status": "success",
  "video_url": "https://s3.amazonaws.com/bucket/video.mp4"
}

Test the API

curl -X POST "http://localhost:8000/generate/" \
  -H "Content-Type: application/json" \
  -d '{
    "image_link": "https://raw.githubusercontent.com/OpenTalker/SadTalker/main/examples/source_image/happy.png",
    "audio_link": "https://github.com/OpenTalker/SadTalker/raw/main/examples/driven_audio/chinese_poem2.wav"
  }'

API Documentation UI

Visit http://localhost:8000/docs for interactive API documentation.

🎯 Learning Platform Features

Career Quiz System

• Interactive career assessment questions
• Personalized career recommendations
• Progress tracking and analytics

AI Tutoring

• Personalized learning paths
• Real-time assistance
• Adaptive content delivery

Exam Preparation

• Comprehensive study materials
• Practice tests and quizzes
• Performance analytics

Skill Hub

• Skill assessment tools
• Learning path recommendations
• Progress tracking

Rewards & Gamification

• Point-based reward system
• Achievement badges
• Leaderboard competition

🛠️ Development

Project Structure

eduMe_AI/
├── backend/                 # SadTalker API server
│   ├── api.py              # FastAPI application
│   ├── src/                # SadTalker source code
│   ├── scripts/            # Utility scripts
│   └── requirements.txt    # Python dependencies
├── frontend/               # Next.js learning platform
│   ├── app/                # Next.js app directory
│   ├── components/         # React components
│   ├── contexts/           # React contexts
│   └── package.json        # Node.js dependencies
└── README.md              # This file

Environment Variables

Backend (.env)

AWS_ACCESS_KEY=your_aws_access_key
AWS_SECRET_KEY=your_aws_secret_key
AWS_S3_REGION=us-west-1
AWS_S3_BUCKET_NAME=your_bucket_name

Frontend (.env.local)

NEXT_PUBLIC_FIREBASE_API_KEY=your_firebase_api_key
NEXT_PUBLIC_FIREBASE_AUTH_DOMAIN=your_firebase_auth_domain
NEXT_PUBLIC_FIREBASE_PROJECT_ID=your_firebase_project_id

🧪 Testing

Backend Tests

cd backend
python -m pytest tests/

Frontend Tests

cd frontend
npm run test

🚀 Deployment

Production Build

Backend

cd backend
docker build -t eduMe_AI_backend .
docker run -p 8000:8000 eduMe_AI_backend

Frontend

cd frontend
npm run build
npm start

Environment Setup

1. Set up AWS S3 bucket for file storage
2. Configure Firebase project for authentication
3. Set up environment variables in production
4. Configure domain and SSL certificates

🤝 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

• SadTalker: Original SadTalker implementation by OpenTalker
• Phạm Gia Linh: Original API implementation from sad-talker-api
• FastAPI: Modern web framework for building APIs
• Next.js: React framework for production
• Tailwind CSS: Utility-first CSS framework

📞 Support

For support and questions:

• Create an issue in the repository
• Contact the development team
• Check the FAQ for common questions

🔄 Updates

See CHANGELOG for detailed version history and updates.

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