AI-Powered Interview Application

An intelligent interview platform that automates the technical interview process using AI-powered question generation and answer evaluation. The application provides two distinct interfaces: one for candidates to take interviews and another for interviewers to review candidate performance.

🌟 Features

For Candidates (Interviewee Interface)

• Resume Upload & Parsing: Automatic extraction of contact information from PDF/DOCX resumes
• AI-Generated Questions: Role-specific interview questions with varying difficulty levels
• Timed Responses: Each question has appropriate time limits based on difficulty
• Real-time Feedback: Instant evaluation and scoring for each answer
• Progress Tracking: Interview state persists across browser refreshes
• Comprehensive Results: Final score with detailed performance summary

For Interviewers (Interviewer Interface)

• Candidate Dashboard: View all interviewed candidates in one place
• Detailed Analytics: Review individual answers with scores and AI feedback
• Performance Metrics: Weighted scoring system based on question difficulty
• Interview Summaries: AI-generated professional summaries for each candidate
• Export Capabilities: Download candidate data and interview results

AI-Powered Intelligence

• Smart Question Generation: Creates role-specific questions using LLM
• Answer Evaluation: AI-powered scoring with constructive feedback
• Professional Summaries: Automated comprehensive interview assessments
• Difficulty Balancing: 2 easy, 2 medium, 2 hard questions per interview

🏗️ Tech Stack

Frontend

• Framework: React 18 with TypeScript
• Build Tool: Vite with SWC for fast compilation
• UI Components: Shadcn/UI (built on Radix UI primitives)
• Styling: Tailwind CSS with custom design system
• State Management: Redux Toolkit with Redux Persist
• Routing: React Router DOM v6
• HTTP Client: Axios with singleton API service
• Data Fetching: TanStack Query (React Query)
• Form Handling: React Hook Form with Zod validation
• Animations: Framer Motion

Backend

• Framework: FastAPI (Python 3.13)
• Document Processing:
  • pdfplumber for PDF parsing
  • python-docx for DOCX files
• AI Integration: OpenRouter API for LLM capabilities
• Model: OpenAI GPT-OSS-20B (free tier)
• CORS: Configured for local development

📋 Prerequisites

• Node.js: v18 or higher
• Python: 3.11 or higher
• npm or bun: Package manager
• OpenRouter API Key: For AI features (free tier available)

🚀 Installation

1. Clone the Repository

git clone https://github.com/AnsariUsaid/ai-interview-application.git
cd ai-interview-application

2. Backend Setup

# Navigate to backend directory
cd backend

# Create virtual environment
python -m venv myvenv

# Activate virtual environment
# On macOS/Linux:
source myvenv/bin/activate
# On Windows:
myvenv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Create .env file
echo "API_KEY=your_openrouter_api_key_here" > .env

Get your API Key:

1. Visit OpenRouter.ai
2. Sign up for a free account
3. Generate an API key
4. Add it to your .env file

3. Frontend Setup

# Navigate to frontend directory
cd ../frontend

# Install dependencies
npm install
# or
bun install

# Start development server
npm run dev
# or
bun dev

🎯 Running the Application

Start Backend Server

cd backend
source myvenv/bin/activate  # On macOS/Linux
python main.py

Backend runs on: http://localhost:8000

Start Frontend Server

cd frontend
npm run dev
# or
bun dev

Frontend runs on: http://localhost:8080

Access the Application

Open your browser and navigate to: http://localhost:8080

📱 Usage

Taking an Interview (Candidate Flow)

1. Upload Resume

   • Click "Start Interview" on the home page
   • Upload your resume (PDF or DOCX format)
   • System automatically extracts your contact information

2. Begin Interview

   • Review extracted information
   • Click "Start Interview" to begin
   • Questions are presented one at a time

3. Answer Questions

   • Each question has a specific time limit
   • Type your answer in the provided text area
   • Click "Submit Answer" to proceed

4. View Results

   • Receive immediate feedback on each answer
   • See your score and AI-generated feedback
   • Review final summary at the end

Reviewing Candidates (Interviewer Flow)

1. Access Interviewer Dashboard

   • Click "Interviewer" from the main menu
   • View list of all candidates who completed interviews

2. Review Individual Candidates

   • Click on any candidate card
   • View detailed answers with scores
   • Read AI-generated feedback for each response
   • Check overall performance metrics

3. Export Data (if needed)

   • Use browser's print function for reports
   • Copy candidate information for records

🛠️ Development

Project Structure

swipe-project/
├── backend/
│   ├── main.py              # FastAPI application with all endpoints
│   ├── requirements.txt     # Python dependencies
│   ├── myvenv/             # Virtual environment (not in git)
│   └── .env                # Environment variables (not in git)
│
├── frontend/
│   ├── src/
│   │   ├── components/      # React components
│   │   │   ├── pages/      # Main page components
│   │   │   ├── interview/  # Interview-specific components
│   │   │   ├── modals/     # Modal dialogs
│   │   │   └── ui/         # Shadcn UI components
│   │   ├── store/          # Redux store and slices
│   │   ├── services/       # API service layer
│   │   ├── hooks/          # Custom React hooks
│   │   └── lib/            # Utility functions
│   ├── public/             # Static assets
│   ├── package.json        # Node dependencies
│   └── vite.config.ts      # Vite configuration
│
├── WARP.md                 # Development guidelines
└── README.md               # This file

Available Scripts

Frontend

npm run dev          # Start development server (port 8080)
npm run build        # Build for production
npm run build:dev    # Build with development mode
npm run lint         # Run ESLint
npm run preview      # Preview production build

Backend

python main.py       # Start FastAPI server (port 8000)

API Endpoints

Endpoint	Method	Description
/health/	GET	Health check
/parse-resume/	POST	Upload and parse resume
/generate-questions	POST	Generate interview questions
/evaluate-answer	POST	Evaluate candidate answer
/final-summary	POST	Generate interview summary

Environment Variables

Backend (.env)

API_KEY=your_openrouter_api_key

Frontend

No environment variables required for basic setup. Uses http://localhost:8000 as default backend URL.

🎨 Features in Detail

Resume Parsing

• Supports PDF and DOCX formats
• Extracts name, email, and phone number
• Uses regex patterns for contact information
• Provides raw text snippet for verification

Question Generation

• Role-specific question creation
• Difficulty levels: Easy (20s), Medium (60s), Hard (120s)
• Fallback to static question bank if AI unavailable
• Automatic retry mechanism for reliability

Answer Evaluation

• 0-10 scoring scale
• Difficulty-weighted scoring system
• Constructive feedback for each answer
• Heuristic fallback when AI is unavailable

State Persistence

• Interview progress saved in browser
• Resume if browser is closed accidentally
• Welcome back modal for incomplete interviews
• Complete candidate data retention

🔧 Configuration

CORS Settings

Backend CORS is configured for localhost development:

allow_origins=["http://localhost:8080"]

For production, update this in backend/main.py to include your production domain.

Port Configuration

• Frontend: Port 8080 (configured in vite.config.ts)
• Backend: Port 8000 (default FastAPI port)

To change ports, update:

• Frontend: vite.config.ts → server.port
• Backend: main.py → uvicorn.run() parameters

AI Model Selection

Current model: openai/gpt-oss-20b:free

To use a different model, update MODEL variable in backend/main.py:

MODEL = "your-preferred-model"

See OpenRouter Models for available options.

🐛 Troubleshooting

Backend Issues

Problem: ModuleNotFoundError when starting backend

# Solution: Ensure virtual environment is activated and dependencies installed
source myvenv/bin/activate
pip install -r requirements.txt

Problem: AI features not working

# Solution: Check your API key in .env file
# Ensure API_KEY is set correctly
# Verify you have credits on OpenRouter

Problem: CORS errors

# Solution: Ensure frontend is running on port 8080
# Or update CORS settings in backend/main.py

Frontend Issues

Problem: Cannot find module errors

# Solution: Reinstall dependencies
rm -rf node_modules package-lock.json
npm install

Problem: Port already in use

# Solution: Kill process on port 8080 or change port
# macOS/Linux:
lsof -ti:8080 | xargs kill -9
# Or change port in vite.config.ts

Problem: Build fails

# Solution: Clear cache and rebuild
npm run build -- --force

🚀 Deployment

Backend Deployment

1. Prepare for production:

   • Update CORS origins in main.py
   • Set production environment variables
   • Use production-grade WSGI server (Gunicorn/Uvicorn)

2. Deploy options:

   • Render.com
   • Railway.app
   • AWS Lambda
   • Google Cloud Run

Frontend Deployment

1. Build production bundle:

npm run build

2. Deploy options:

   • Vercel (recommended for Vite projects)
   • Netlify
   • GitHub Pages
   • AWS S3 + CloudFront

3. Update API URL:

   • Create environment variable for production backend URL
   • Update API service configuration

📊 Performance

• Question Generation: ~2-3 seconds (with AI)
• Answer Evaluation: ~1-2 seconds per answer
• Resume Parsing: <1 second
• Frontend Build: ~5-10 seconds
• Bundle Size: ~500KB (gzipped)

🔒 Security Considerations

• API keys stored in environment variables (not committed to git)
• CORS configured for specific origins
• File upload limited to PDF/DOCX only
• Input validation using Pydantic models
• No persistent data storage (stateless application)

🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a 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 open source and available for educational purposes.

👨‍💻 Author

Ansari Usaid Anzer

• GitHub: @AnsariUsaid
• Repository: ai-interview-application

🙏 Acknowledgments

• Shadcn/UI for beautiful React components
• OpenRouter for AI model access
• FastAPI for excellent Python backend framework
• Vite for blazing fast development experience
• Radix UI for accessible component primitives

Support

For issues, questions, or suggestions:

• Open an issue on GitHub
• Check existing issues for solutions

🗺️ Roadmap

• Add authentication system
• Implement database for persistent storage
• Add video interview capability
• Support multiple languages
• Add interview scheduling
• Implement email notifications
• Add analytics dashboard
• Support custom question banks

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Made with ❤️ for better technical interviews