AI Interview

AI-powered interview tool that analyzes answers and provides instant, personalized feedback for effective preparation.

📸 Screenshots

🎯 Overview

AI Interview is a comprehensive interview preparation platform that leverages artificial intelligence to simulate real interview scenarios. The application provides instant feedback on your responses, helping you identify areas for improvement and build confidence for your actual interviews.

✨ Features

• 🤖 AI-Powered Questions - Dynamic question generation based on role and experience level
• 📊 Real-time Analysis - Instant feedback on communication skills and answer quality
• 🎙️ Voice Recognition - Speech-to-text for natural conversation flow
• 📈 Performance Tracking - Detailed analytics and progress monitoring
• 🔐 Secure Authentication - User management with Clerk integration
• 💬 Real-time Communication - WebSocket integration for seamless interactions
• 🎨 Modern UI/UX - Clean, responsive interface built with Tailwind CSS
• 📱 Mobile Responsive - Optimized for all devices

🛠️ Tech Stack

Category	Technology	Purpose
Frontend	React 18+	User interface framework
Language	TypeScript	Type-safe development
Build Tool	Vite	Fast development and bundling
Styling	Tailwind CSS	Utility-first styling
State Management	Redux Toolkit	Predictable state management
Authentication	Clerk	User authentication & management
Real-time	WebSocket	Live communication
Validation	Zod	Runtime type validation
UI Components	shadcn/ui	Reusable component library
Containerization	Docker	Application deployment

🚀 Quick Start

Prerequisites

• Node.js 18+ and npm (for local development)
• Git
• Docker and Docker Compose (for containerized deployment)

Option 1: Docker Installation (Recommended)

The fastest way to get started is using our pre-built Docker image:

Using Docker Run

1. Pull and run the Docker image

   docker run -d \
   --name ai-interview \
   -p 3000:80 \
   -e VITE_CLERK_PUBLISHABLE_KEY=your_clerk_key_here \
   -e VITE_API_BASE_URL=your_api_url_here \
   -e VITE_WEBSOCKET_URL=your_websocket_url_here \
   tanish2000/ai-interview:latest

2. Access the application Open your browser and navigate to http://localhost:3000

Using Docker Compose

1. Create a docker-compose.yml file

   version: '3.8'services:
   ai-interview:
   image: tanish2000/ai-interview:latestcontainer_name: ai-interviewports:
   - "3000:80"environment:
   - VITE_CLERK_PUBLISHABLE_KEY=your_clerk_key_here
   - VITE_API_BASE_URL=your_api_url_here
   - VITE_WEBSOCKET_URL=your_websocket_url_hererestart: unless-stopped

2. Start the application

   docker-compose up -d

3. View logs (optional)

   docker-compose logs -f ai-interview

Environment Variables for Docker

Create a .env file for Docker Compose:

VITE_CLERK_PUBLISHABLE_KEY=your_clerk_key_hereVITE_API_BASE_URL=your_api_url_hereVITE_WEBSOCKET_URL=your_websocket_url_here

Then update your docker-compose.yml:

version: '3.8'services:
ai-interview:
image: tanish2000/ai-interview:latestcontainer_name: ai-interviewports:
- "3000:80"env_file:
- .envrestart: unless-stopped

Docker Management Commands

# Pull the latest image
docker pull tanish2000/ai-interview:latest
# Stop the container
docker stop ai-interview
# Start the container
docker start ai-interview
# Remove the container
docker rm ai-interview
# View container logs
docker logs ai-interview
# Execute commands inside the container
docker exec -it ai-interview /bin/sh

Option 2: Local Development Installation

1. Clone the repository

   git clone https://github.com/devtanish/AI-Interview.git
   cd AI-Interview

2. Install dependencies

   npm install

3. Set up environment variables

   cp .env.example .env.local

   Update .env.local with your configuration:

   VITE_CLERK_PUBLISHABLE_KEY=your_clerk_key_hereVITE_API_BASE_URL=your_api_url_hereVITE_WEBSOCKET_URL=your_websocket_url_here

4. Start the development server ```bash npm run dev ```

5. Open your browser Navigate to http://localhost:5173

📁 Project Structure

AI-Interview/
├── public/ # Static assets
├── src/
│ ├── components/ # Reusable UI components
│ │ ├── ui/ # shadcn/ui components
│ │ ├── interview/ # Interview-specific components
│ │ └── common/ # Shared components
│ ├── pages/ # Application pages/routes
│ ├── hooks/ # Custom React hooks
│ ├── store/ # Redux store configuration
│ │ ├── slices/ # Redux slices
│ │ └── api/ # API slice definitions
│ ├── lib/ # Utility libraries
│ ├── types/ # TypeScript type definitions
│ ├── schemas/ # Zod validation schemas
│ └── styles/ # Global styles
├── package.json
├── tsconfig.json
├── tailwind.config.js
├── vite.config.ts
└── README.md

🔧 Available Scripts

# Development
npm run dev # Start development server
npm run build # Build for production 
npm run preview # Preview production build locally# Code Quality
npm run lint # Run ESLint
npm run lint:fix # Fix ESLint issues
npm run type-check # Run TypeScript compiler# Testing
npm run test# Run tests
npm run test:ui # Run tests with UI# Docker (if building locally)
docker build -t ai-interview .# Build Docker image
docker run -p 3000:80 ai-interview # Run Docker container

Custom Domain Setup

To use a custom domain with Lovable:

1. Go to Project → Settings → Domains
2. Click Connect Domain
3. Follow the DNS configuration steps

🎮 Usage

1. Sign Up/Login - Create an account or sign in with existing credentials
2. Choose Interview Type - Select from technical, behavioral, or custom interviews
3. Set Parameters - Configure difficulty level, duration, and focus areas
4. Start Interview - Begin your AI-powered interview session
5. Review Feedback - Analyze your performance and improvement suggestions
6. Track Progress - Monitor your improvement over time

🐳 Docker Hub

The application is available as a Docker image on Docker Hub:

• Repository: tanish2000/ai-interview
• Tags Available:
  • latest - Latest stable version
  • v1.0.0 - Specific version tag

Pull the image directly: ```bash docker pull tanish2000/ai-interview:latest ```

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Development Workflow

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Make your changes and add tests
4. Commit your changes: git commit -m 'Add amazing feature'
5. Push to the branch: git push origin feature/amazing-feature
6. Open a Pull Request

Code Style

• Use TypeScript for all new code
• Follow the existing ESLint configuration
• Write meaningful commit messages
• Add tests for new features

🐛 Issues & Support

• 🐛 Report a bug
• 🚀 Request a feature
• 💬 Join discussions

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👨‍💻 Author

Tanish

• GitHub: @devtanish
• LinkedIn: Connect with me

🙏 Acknowledgments

• React - The foundation of our UI
• Clerk - Authentication made simple
• shadcn/ui - Beautiful component library
• Tailwind CSS - Utility-first CSS framework
• Lovable - Rapid development platform
• Docker - Containerization platform

📈 Roadmap

• Multi-language support
• Video interview simulation
• Advanced analytics dashboard
• Integration with job platforms
• Mobile app development
• AI interviewer personality customization
• Kubernetes deployment support
• Multi-architecture Docker images

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⭐ Star this repo if you found it helpful!

Made with ❤️ by Tanish