Video Quizify

An intelligent platform that generates interactive quizzes from video content using AI-powered transcription and question generation.

🚀 Features

• Video Processing: Upload and process video content
• AI-Powered Transcription: Convert speech to text with high accuracy
• Smart Quiz Generation: Automatically create multiple-choice questions from video content
• Interactive Quizzes: Engage users with automatically generated quizzes
• Multi-language Support: Support for multiple languages in transcription and question generation
• Scalable Architecture: Microservices-based architecture for reliability and scalability

🏗️ Project Structure

vidQuizify/
├── frontend/           # React-based web interface
├── server/             # Main backend service (NestJS)
└── services/
    └── transcription-service/  # Audio processing and question generation

Demo

🛠️ Prerequisites

• Node.js 16+
• Python 3.8+
• Docker (optional, for containerized deployment)
• FFmpeg (for audio processing)
• Ollama (for question generation)

🚀 Quick Start

1. Clone the Repository

git clone https://github.com/teja-dev-tech/vidQuizify.git
cd vidQuizify

2. Set Up Services

Frontend

cd frontend
npm install
npm run dev

Backend Server

cd server
npm install
npm run start:dev

Transcription Service

cd services/transcription-service
python -m venv venv
source venv/bin/activate  # On Windows: .\venv\Scripts\activate
pip install -r requirements.txt
uvicorn app.main:app --reload

3. Configure Environment

Create .env files in each service directory with appropriate configuration. See individual service READMEs for details.

🌐 API Documentation

Once services are running, access the API documentation at:

• Backend API: http://localhost:3000/api
• Transcription Service: http://localhost:8000/docs

🐳 Docker Deployment

# Build and start all services
docker-compose up --build

# Start in detached mode
docker-compose up -d

# View logs
docker-compose logs -f

🤝 Contributing

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

📄 License

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

🙏 Acknowledgments

• Whisper for speech recognition
• Ollama for language model capabilities
• All open-source libraries and frameworks used in this project