AI Interview Prep is a job interview preparation web application powered by AI. The application uses LangChain agents with GPT-4 to automatically analyze job postings and company information, generating personalized preparation plans. The app leverages OpenAI's Realtime voice technology to allow users to practice voice-based mock interviews with an AI coach that provides feedback and guidance.
- Job Analysis: Automatically extracts and analyzes job requirements, company information, and role responsibilities from job postings
- Personalized Preparation Plans: Creates custom interview preparation plans based on the job requirements
- Virtual Interview Coach: Utilizing OpenAI's Realtime voice technology, users can practice with a responsive AI interviewer that adapts to their responses, provides immediate feedback, and helps improve their interview performance.
- Structured Practice Sessions: Guides users through focused practice sessions targeting specific skills and competencies
- Real-time Interview Practice: Integrates with OpenAI's real-time API for interactive interview practice sessions
- Skills Assessment: Provides detailed breakdown of required skills and competencies for the target role
- Progress Tracking: Tracks completion of practice sessions and preparation milestones
- Frontend: Next.js 15.1, React 19, TypeScript
- Backend: Python with FastAPI
- AI/ML: LangChain with GPT-4
- Styling: Tailwind CSS with DaisyUI
- State Management: TanStack Query
- Testing: Jest, React Testing Library
- API Mocking: MSW (Mock Service Worker)
- Node.js 18+
- Python 3.8+
- OpenAI API key
- Tavily API key (for web search capabilities)
- Lakera Guard API key (for checking prompt injection)
- Clone the repository:
git clone https://github.com/energydrink9/ai-interview-prep.git
cd ai-interview-prep- Install frontend dependencies:
npm install
# or
yarn install- Install backend dependencies:
cd api
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
pip install -r requirements.txt- Set up environment variables:
Create a .env file in the root directory:
OPENAI_API_KEY=your_openai_api_key
TAVILY_API_KEY=your_tavily_api_key- Start the backend server:
cd api
source .venv/bin/activate # On Windows: .venv\Scripts\activate
python index.py- In a new terminal, start the frontend development server:
# Regular development mode
npm run dev
# or
yarn dev
# Development mode with mocked APIs
npm run dev-mock
# or
yarn dev-mock- Open http://localhost:3000 in your browser
# Run tests in watch mode with coverage
npm test
# Run tests once for CI
npm run test:ci
# Run a single test file
npm run test:single├── api/ # Python backend
│ ├── index.py # FastAPI application
│ └── planner_agent.py # LangChain agent implementation
├── app/
│ ├── api/ # Frontend API clients
│ ├── components/ # Shared React components
│ ├── gather/ # Job URL input page
│ ├── interview-session/ # Interview practice session
│ ├── plan/ # Preparation plan display
│ └── providers/ # React context providers
├── public/ # Static assets
└── package.json
The application includes a mock API implementation using MSW (Mock Service Worker). To run the application with mocked APIs:
npm run dev-mock
# or
yarn dev-mockThis is useful for development and testing when you don't want to make actual API calls.
- Create a new feature branch:
git checkout -b feature/your-feature-name- Make your changes and commit them:
git add .
git commit -m "feat: add your feature"- Push to your branch and create a pull request:
git push origin feature/your-feature-nameThe application is configured for deployment on Vercel. The deployment process is automated through GitHub Actions (see .github/workflows/deploy.yml).
To deploy manually:
- Install Vercel CLI:
npm i -g vercel- Deploy:
vercelContributions are welcome! Please read our contributing guidelines and code of conduct before submitting pull requests.
This project is licensed under the MIT License - see the LICENSE file for details.