AI Interview Assistant

An intelligent interview practice application that helps users prepare for technical, behavioral, and system design interviews with real-time AI feedback.

Watch video demo

Features

• Multi-format Interview Practice: Choose from Technical, Behavioral, System Design, Problem Solving, Leadership, HR, and Case Study interviews.
• AI-generated Questions: Dynamic generation of relevant interview questions based on the selected category.
• Real-time Feedback: Receive immediate analysis and feedback on your responses.
• Learning Opportunities: Get detailed explanations when you're unfamiliar with a topic.
• Progress Tracking: Track your progress with a question counter.
• Detailed PDF Reports: Download comprehensive feedback summaries in PDF format.

How It Works

The AI Interview Assistant uses a sophisticated AI-powered approach to simulate realistic interview experiences.

Key Components:

1. Question Generation: The system uses Google's Gemini AI to generate contextually relevant interview questions based on the selected category (Technical, Behavioral, etc.).

2. Response Analysis: When you answer a question, the AI evaluates your response based on several criteria:

   • Technical accuracy
   • Demonstrated real-world experience
   • Problem-solving approach
   • Communication clarity

3. Adaptive Follow-up: The system dynamically adjusts based on your answers:

   • For strong answers: Provides positive feedback and challenging follow-up questions
   • For vague or incomplete answers: Offers constructive criticism and suggestions
   • For "I don't know" responses: Provides learning resources and explanations

4. Comprehensive Feedback: Upon completion, the AI analyzes your entire interview performance and generates:

   • Overall assessment (Accepted/Rejected)
   • Strengths and weaknesses
   • Detailed feedback for each question
   • Specific improvement suggestions

5. PDF Report Generation: All feedback is compiled into a professional PDF report you can download for later review.

Tech Stack

Frontend

• React with TypeScript
• Tailwind CSS for styling
• React Context API for state management
• Axios for API requests

Backend

• Django REST Framework
• Gemini API for AI question generation and response analysis
• JWT Authentication
• ReportLab for PDF generation

Getting Started

Prerequisites

• Python 3.9+
• Google API key for Gemini AI

Installation

Backend Setup

1. Clone the repository:

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

2. Create and activate a virtual environment:

   cd backend
   python -m venv venv
   # Windows
   venv\Scripts\activate
   # Linux/Mac
   source venv/bin/activate
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Run migrations:

   python manage.py migrate
   

5. Create a .env file in the backend directory with your Gemini API key:

   GEMINI_API_KEY=your_gemini_api_key
   

6. Start the backend server:

   python manage.py runserver
   

Frontend Setup

1. Navigate to the frontend directory:

   cd frontend
   

2. Install dependencies:

   npm install
   

3. Start the development server:

   npm run dev
   

4. Open your browser and go to http://localhost:5173

Usage

1. Register/Login: Create an account or log in to get started.
2. Select Interview Type: Choose the type of interview you want to practice.
3. Answer Questions: Respond to the AI-generated questions.
4. Receive Feedback: Get real-time feedback on your responses.
5. Continue or Review: Proceed to the next question or review your performance.
6. Download Report: Once the interview is complete, download a detailed PDF report.

Project Structure

ai-interview/
├── backend/
│   ├── ai_agent/                # Django project settings
│   ├── apps/
│   │   ├── authentication/      # User authentication
│   │   ├── interviews/          # Interview logic and API
│   │       ├── services/        # AI services integration
│   │       ├── utils.py         # Utility functions (PDF generation)
│   │       ├── views.py         # API endpoints
│   │       └── models.py        # Database models
│   └── manage.py
├── frontend/
│   ├── src/
│   │   ├── components/          # React components
│   │   ├── context/             # React context providers
│   │   ├── services/            # API service functions
│   │   ├── types/               # TypeScript type definitions
│   │   └── App.tsx              # Main application component
│   ├── package.json
│   └── vite.config.ts
└── README.md

API Endpoints

• POST /api/auth/register/: User registration
• POST /api/auth/login/: User login
• POST /api/interviews/generate-questions/: Generate interview questions
• GET /api/interviews/sessions/{session_id}/next-question/{current_order}/: Get next question
• POST /api/interviews/submit-response/{session_id}/{question_id}/: Submit response to a question
• POST /api/interviews/sessions/{session_id}/feedback/: Get interview feedback
• GET /api/interviews/sessions/{session_id}/feedback/pdf/: Download feedback as PDF

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Acknowledgments

• Google Gemini API for powering the AI capabilities
• Django REST Framework for the backend API
• React and Tailwind CSS for the frontend