MockMate

MockMate is an AI-powered interview platform with separate backend and frontend services, designed to help candidates practice interviews and/or help recruiters run AI-assisted interview sessions.

Features

• AI-driven interview question generation powered by Gemini
• Candidate response evaluation
• REST API for interview session management
• Responsive frontend for conducting/practicing interviews

Project Structure

MockMate/
├── backend/    # FastAPI application (API, business logic, AI integration)
├── frontend/   # React application (user interface)
└── .gitignore

Tech Stack

Frontend	Backend	AI & Storage
React.js	FastAPI	Google Vertex AI
Ant Design (UI library)	Uvicorn (ASGI server)	Gemini-2.0-flash
Axios (API calls)	Python 3.12+	MongoDB Atlas
React Toastify	Pydantic

Getting Started

Prerequisites

• Git
• Python 3.12+
• Node.js and npm
• A MongoDB Atlas cluster
• A Google Cloud project with Vertex AI enabled (for Gemini-2.0-flash access)

Installation

1. Clone the repository

   git clone https://github.com/tiyaagarwal/MockMate.git
   cd MockMate

2. Set up the backend

   cd backend
   python -m venv venv
   source venv/bin/activate  # Windows: venv\Scripts\activate
   pip install -r requirements.txt

3. Set up the frontend

   cd ../frontend
   npm install

Environment Variables

Create a .env file in the backend directory with the required keys, for example:

MONGODB_URI=your_mongodb_atlas_connection_string
GOOGLE_APPLICATION_CREDENTIALS=path_to_your_service_account_json
GCP_PROJECT_ID=your_gcp_project_id
GEMINI_MODEL=gemini-2.0-flash
PORT=8000

Running the App

1. Start the backend

   cd backend
   uvicorn app.main:app --reload --port 8000

2. Start the frontend

   cd frontend
   npm start

3. Open your browser at http://localhost:3000 (frontend) — the API will be available at http://localhost:8000

Usage

Once both servers are running, open the frontend in your browser, start an interview session, and interact with the AI interviewer in real time. Responses are evaluated and stored via the backend, with session data persisted in MongoDB Atlas.

Contributing

Contributions are welcome. Please open an issue to discuss what you'd like to change, then submit a pull request.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/your-feature)
3. Commit your changes
4. Push to the branch and open a pull request

License

This project is licensed under the MIT License.