QueryBox AI

QueryBox 🤖 is an AI-powered interview preparation bot that simulates technical 💻 and behavioral 💬 interviews. Users pick roles & domains, practice Q&A, and get instant feedback on clarity, correctness, and accuracy. A final summary highlights strengths, weaknesses, and resources. 🚀

🚀 Features

• AI-powered query processing
• Modern React frontend with responsive design
• Python Flask backend with robust API
• Real-time query processing
• Clean and intuitive user interface

🛠️ Tech Stack

Frontend

• React - Modern JavaScript library for building user interfaces
• Vite - Fast build tool and development server
• CSS3 - Modern styling with responsive design

Backend

• Python - Core programming language
• Flask - Lightweight web framework
• Gemini AI - AI model integration for query processing

📁 Project Structure

QueryBox/
├── frontend/                 # React application
│   ├── src/
│   │   ├── App.jsx          # Main React component
│   │   ├── api.js           # API communication
│   │   ├── assets/          # Static assets
│   │   └── ...
│   ├── package.json         # Frontend dependencies
│   └── vite.config.js       # Vite configuration
├── backend/                 # Python Flask server
│   ├── app.py              # Main Flask application
│   ├── requirements.txt    # Python dependencies
│   └── .env.example        # Environment variables template
└── README.md              # Project documentation

🚀 Getting Started

Prerequisites

• Node.js (v14 or higher)
• Python (v3.7 or higher)
• pip (Python package manager)

Installation

1. Clone the repository

   git clone https://github.com/karansingh012/QueryBox.git
   cd QueryBox

2. Setup Backend

   cd backend
   pip install -r requirements.txt
   cp .env.example .env
   # Edit .env file with your API keys
   python app.py

3. Setup Frontend

   cd frontend
   npm install
   npm run dev

4. Access the application

   • Frontend: http://localhost:5173
   • Backend: http://localhost:5000

🔧 Configuration

1. Copy backend/.env.example to backend/.env
2. Add your API keys and configuration:

   GEMINI_API_KEY=your_gemini_api_key_here
   FLASK_ENV=development
   

🤝 Contributing

1. Fork the project
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.

👨‍💻 Author

Karan Singh

• GitHub: @karansingh012

🙏 Acknowledgments

• Thanks to Google Gemini AI for powering the intelligent query processing
• Built with modern web technologies for optimal performance