An AI-powered interview practice application that helps users prepare for technical interviews with real-time feedback and analysis.
- Interactive chat interface with AI interviewer
- Real-time timer for interview sessions
- Dashboard for tracking interview history
- Modern, responsive UI built with React and TailwindCSS
- React
- TailwindCSS
- Vite
- Python
- Flask/FastAPI (check requirements.txt for specifics)
- Navigate to the backend directory:
cd backend- Create a virtual environment:
python -m venv venv- Activate the virtual environment:
- Windows:
venv\Scripts\activate - Mac/Linux:
source venv/bin/activate
- Install dependencies:
pip install -r requirements.txt- Run the backend server:
python app.py- Navigate to the frontend directory:
cd frontend- Install dependencies:
npm install- Run the development server:
npm run dev- Start both backend and frontend servers
- Open your browser and navigate to the frontend URL (typically http://localhost:5173)
- Begin your AI interview practice session
- Push your code to GitHub
- Go to Vercel and sign in
- Click "New Project" and import your GitHub repository
- Vercel will auto-detect the configuration from
vercel.json - Add environment variable:
VITE_API_BASE: Your backend API URL (e.g.,https://your-backend.railway.app/api)
- Deploy!
Option 1: Railway
- Go to Railway
- Create new project from GitHub repo
- Select the
backenddirectory - Add environment variables:
GROQ_API_KEY: Your Groq API keySECRET_KEY: Django secret keyDEBUG: Set toFalsefor production
- Railway will auto-deploy
Option 2: Render
- Go to Render
- Create new Web Service
- Connect your GitHub repository
- Set root directory to
backend - Add environment variables
- Deploy
Important: After deploying backend, update the VITE_API_BASE environment variable in Vercel with your backend URL.
MIT