🎯 Intelliview - AI-Powered Interview Preparation Platform

An intelligent platform that helps you ace your job interviews with AI-powered mock interviews and personalized feedback

🚀 Features

📋 Interview Preparation

• Smart Workflow Management: Create and manage interview workflows for different positions
• Resume Analysis: Upload your PDF resume for AI-powered analysis and optimization suggestions
• Personalized Q&A: Get tailored interview questions based on your background and target role
• Resource Recommendations: Access curated learning materials and preparation resources

🤖 AI-Powered Mock Interviews

• Real-time Conversations: Engage in natural conversations with AI interviewer via text or voice
• Adaptive Questioning: Dynamic follow-up questions based on your responses
• Session Management: Timed interview sessions with automatic transcript generation
• Multi-modal Support: Text and audio communication options

📊 Intelligent Feedback System

• Comprehensive Analysis: Detailed performance evaluation across multiple dimensions
• Strength Recognition: Highlight what you did well during the interview
• Improvement Areas: Specific suggestions with examples and actionable advice
• Progress Tracking: Monitor your improvement over multiple interview sessions
• Resource Library: Curated links to help you improve identified weak areas

📈 Interview History & Analytics

• Session History: Access all your past interview transcripts and feedback
• Position-based Filtering: Filter interviews by specific job positions
• Performance Trends: Track your progress across different interview sessions
• Exportable Reports: Download interview transcripts and feedback for review

🛠️ Tech Stack

Frontend

• Flutter Web - Cross-platform UI framework for responsive web applications
• Dart - Programming language optimized for building user interfaces
• Material Design - Google's design system for consistent UI/UX

Backend

• FastAPI - Modern, fast Python web framework for building APIs
• Python 3.9+ - Core backend programming language
• Google ADK - Agent Development Kit for AI agent orchestration
• WebSocket - Real-time bidirectional communication

AI & ML

• Google Gemini 2.0 - Advanced language model for interview conversations
• Google Cloud AI - Cloud-based AI services and infrastructure

Database & Storage

• Firestore - NoSQL document database for scalable data storage
• Firebase Auth - Authentication and user management

DevOps & Deployment

• Google Cloud Platform - Cloud infrastructure and hosting
• GitHub Actions - CI/CD pipeline automation

📦 Installation & Setup

Prerequisites

• Flutter SDK (3.0+)
• Python (3.9+)
• Node.js (16+)
• Firebase Project with Firestore and Authentication enabled
• Google Cloud Project with required APIs enabled

🔧 Backend Setup

1. Clone the repository

   git clone https://github.com/lydia-yan/Intelliview
   cd Intelliview

2. Set up Python environment

   cd backend
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install -r requirements.txt

3. Configure environment variables Create a .env file in the backend/ directory:

   # backend/.env
   GOOGLE_CLOUD_PROJECT=your-actual-project-id
   GOOGLE_CLOUD_LOCATION=us-central1
   GOOGLE_GENAI_USE_VERTEXAI=True

4. Set up Firebase credentials Create a credentials folder and put your firebase_key.json inside it

   FIREBASE_KEY_PATH=credentials/firebase_key.json #Or change it to your actual path of firebase_key.json

5. Run the backend server From the project root path

   uvicorn backend.app:app --reload --port 8000

🎨 Frontend Setup

1. Navigate to frontend directory

   cd frontend/mocker_web

2. Install Flutter dependencies

   flutter pub get

3. Configure Firebase and Google Sign-In Client ID

   • Add your firebase_options.dart file or change the ID and key to your own version in the existing firebase_options.dart file
   • Add your own Google Sign-In Client ID in lib/services/auth_service.dart and web/index.html file

4. Run the web application

   flutter run -d chrome --web-port 3000

📁 Project Structure

mocker/
├── backend/                    # Python FastAPI backend
│   ├── agents/                # AI agents (interviewer, judge)
│   ├── api/                   # REST API endpoints
│   ├── data/                  # Database models and schemas
│   ├── coordinator/           # Session management
│   └── service/              
├── frontend/                  # Flutter web frontend
│   └── mocker_web/
│       ├── lib/
│       │   ├── pages/         # UI pages/screens
│       │   ├── services/      # API service layer
│       │   ├── models/        # Data models
│       │   ├── widgets/       # Reusable UI components
│       │   └── config/        # App configuration
│       └── web/               # Web-specific assets
└── README.md                  # Project documentation

🎯 Usage

1. Create Account: Sign up using your email or social login
2. Prepare Workflow: Upload your resume and create a workflow for your target position
3. Get Recommendation Q&A: Review AI-generated interview questions tailored to your profile
4. Practice Interview: Start a mock interview session with our AI interviewer
5. Receive Feedback: Get detailed analysis and suggestions for improvement
6. Track Progress: Monitor your performance across multiple sessions

🔗 API Documentation

Once the backend is running, visit http://localhost:8000/docs for interactive API documentation powered by Swagger UI.

🤝 Contributing

We welcome contributions!

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

📝 License

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

📞 Support

If you have any questions or need help, please:

• 📧 Open an issue on GitHub
• 💬 Join our community discussions
• 📖 Check the documentation

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Built with ❤️ using Flutter, Python, and Google AI

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