AI Interview & Placement Preparation Platform

A comprehensive full-stack web application that simulates real interviews, analyzes user behavior, generates academic quizzes, and provides deep AI-powered analytics.

Features

Core Features

• Real Interview Simulator - Practice interviews with AI-generated questions and real-time evaluation
• Video Proctoring - Face detection, eye gaze tracking, phone detection using MediaPipe & YOLOv8
• Emotion & Voice Analysis - Stress detection, confidence scoring, tone analysis
• Flashcards & Quizzes - AI-generated study materials for OS, CN, DBMS, OOPS
• Group Discussion Simulation - Practice GD skills with AI participants
• Analytics Dashboard - Track progress, identify weak areas, get learning recommendations
• PDF Report Generation - Downloadable detailed performance reports

AI Integrations

• Groq API - LLaMA 3.1 70B for evaluation, Mixtral for fast generation, Whisper for STT
• Google Gemini - Flashcard and quiz generation
• LangChain - Multi-agent orchestration, interview chains
• MediaPipe - Face mesh, iris tracking
• YOLOv8 - Object detection for proctoring

Interview Modes

• HR Round
• Technical Round (DSA, CS fundamentals)
• Behavioral Round
• System Design (Simplified)

Company-Specific Practice

• Amazon
• Microsoft
• Infosys
• TCS
• CRED

Interviewer Personas

• Strict Senior Engineer
• Friendly HR
• Curious Fresher
• Logical Tech Lead

Tech Stack

Backend

• Flask
• MongoDB Atlas
• JWT Authentication
• LangChain
• Groq SDK
• Google GenerativeAI
• MediaPipe
• Ultralytics YOLOv8
• ReportLab (PDF generation)

Frontend

• React 18
• Vite
• Tailwind CSS
• Chart.js
• React Router DOM
• React Webcam
• RecordRTC

Project Structure

ai-interview-platform/
├── backend/
│   ├── app.py                 # Main Flask application
│   ├── requirements.txt       # Python dependencies
│   ├── .env.example          # Environment variables template
│   ├── routes/               # API endpoints
│   │   ├── auth.py
│   │   ├── interview.py
│   │   ├── flashcards.py
│   │   ├── quiz.py
│   │   ├── proctoring.py
│   │   ├── analytics.py
│   │   ├── reports.py
│   │   └── gd.py
│   ├── models/               # MongoDB schemas
│   │   └── schemas.py
│   ├── services/             # AI service integrations
│   │   ├── groq_service.py
│   │   ├── gemini_service.py
│   │   ├── langchain_service.py
│   │   └── proctoring_service.py
│   └── ai_pipelines/         # AI processing pipelines
│       └── interview_pipeline.py
├── frontend/
│   ├── package.json
│   ├── vite.config.js
│   ├── tailwind.config.js
│   ├── index.html
│   └── src/
│       ├── main.jsx
│       ├── App.jsx
│       ├── index.css
│       ├── context/          # React context providers
│       ├── components/       # Reusable components
│       ├── pages/            # Page components
│       └── services/         # API service
└── README.md

Setup Instructions

Prerequisites

• Python 3.9+
• Node.js 18+
• MongoDB Atlas account (free tier)
• API keys for:
  • Groq API
  • Google Gemini API
  • HuggingFace (optional)

1. Clone the Repository

cd ai-interview-platform

2. Backend Setup

# Navigate to backend
cd backend

# Create virtual environment
python -m venv venv

# Activate virtual environment
# Windows:
venv\Scripts\activate
# Mac/Linux:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Download YOLOv8 model (first run will auto-download)
# OR manually download yolov8n.pt from Ultralytics

3. Configure Environment Variables

Create .env file in backend folder:

# MongoDB
MONGODB_URI=mongodb+srv://username:password@cluster.mongodb.net/ai_interview_platform

# JWT
SECRET_KEY=your-super-secret-key-here
JWT_SECRET_KEY=your-jwt-secret-key-here

# Groq API (Get from https://console.groq.com)
GROQ_API_KEY=your-groq-api-key

# Google Gemini API (Get from https://makersuite.google.com/app/apikey)
GEMINI_API_KEY=your-gemini-api-key

# HuggingFace (optional)
HUGGINGFACE_API_KEY=your-huggingface-api-key

# App Config
FLASK_ENV=development
FLASK_DEBUG=1

4. Frontend Setup

# Navigate to frontend
cd ../frontend

# Install dependencies
npm install

5. Run the Application

Terminal 1 - Backend:

cd backend
python app.py

Backend will run on http://localhost:5000

Terminal 2 - Frontend:

cd frontend
npm run dev

Frontend will run on http://localhost:3000

6. Access the Application

Open http://localhost:3000 in your browser

API Endpoints

Authentication

• POST /api/auth/register - Register new user
• POST /api/auth/login - Login user
• GET /api/auth/profile - Get user profile
• PUT /api/auth/profile - Update profile

Interview

• POST /api/interview/start - Start new interview
• POST /api/interview/transcribe - Transcribe audio
• POST /api/interview/evaluate - Evaluate answer
• POST /api/interview/complete - Complete interview
• GET /api/interview/history - Get interview history
• GET /api/interview/:id - Get interview details

Flashcards

• POST /api/flashcards/generate - Generate flashcards
• GET /api/flashcards/list - List flashcard sets
• GET /api/flashcards/:id - Get flashcard set
• POST /api/flashcards/:id/review - Review card

Quiz

• POST /api/quiz/generate - Generate quiz
• POST /api/quiz/submit - Submit quiz answers
• GET /api/quiz/history - Get quiz history
• GET /api/quiz/:id - Get quiz details
• GET /api/quiz/analytics - Get quiz analytics

Proctoring

• POST /api/proctoring/analyze-frame - Analyze video frame
• POST /api/proctoring/emotion - Analyze emotion
• POST /api/proctoring/voice-analysis - Analyze voice
• GET /api/proctoring/log/:id - Get proctoring log

Analytics

• GET /api/analytics/dashboard - Get dashboard data
• GET /api/analytics/knowledge-graph - Get knowledge graph
• GET /api/analytics/meta-analysis - Get meta analysis

Reports

• POST /api/reports/generate/:id - Generate interview report
• POST /api/reports/overall - Generate overall report
• GET /api/reports/list - List reports

Group Discussion

• POST /api/gd/start - Start GD session
• POST /api/gd/contribute - Add contribution
• POST /api/gd/complete - Complete GD
• GET /api/gd/topics - Get GD topics
• GET /api/gd/history - Get GD history

Deployment

Backend Deployment (Render)

1. Create a new Web Service on Render
2. Connect your GitHub repository
3. Configure:
   • Build Command: pip install -r requirements.txt
   • Start Command: gunicorn app:app
   • Environment Variables: Add all from .env
4. Deploy

Frontend Deployment (Vercel)

1. Import project to Vercel
2. Configure:
   • Framework Preset: Vite
   • Build Command: npm run build
   • Output Directory: dist
   • Environment Variables:
     • VITE_API_URL: Your Render backend URL
3. Deploy

Frontend Deployment (Netlify)

1. Import from Git
2. Configure:
   • Build Command: npm run build
   • Publish Directory: dist
3. Add _redirects file in public/:

   /api/* https://your-backend-url.onrender.com/api/:splat 200
   /* /index.html 200
   

4. Deploy

Environment Variables

Variable	Description	Required
MONGODB_URI	MongoDB connection string	Yes
SECRET_KEY	Flask secret key	Yes
JWT_SECRET_KEY	JWT secret key	Yes
GROQ_API_KEY	Groq API key	Yes
GEMINI_API_KEY	Google Gemini API key	Yes
HUGGINGFACE_API_KEY	HuggingFace API key	No

Getting API Keys

Groq API

1. Visit https://console.groq.com
2. Sign up/Login
3. Navigate to API Keys
4. Create new API key

Google Gemini API

1. Visit https://makersuite.google.com/app/apikey
2. Sign in with Google account
3. Create API key

MongoDB Atlas

1. Visit https://www.mongodb.com/atlas
2. Create free cluster
3. Get connection string from Connect > Drivers

Usage Guide

Starting an Interview

1. Navigate to Interview page
2. Select company, round type, and interviewer persona
3. Click "Start Interview"
4. Answer questions using the microphone
5. Receive real-time evaluation and follow-up questions
6. Complete interview to see overall scores

Studying with Flashcards

1. Go to Flashcards page
2. Select subject and topic
3. Generate flashcards
4. Study by flipping cards
5. Mark cards as mastered or need practice

Taking Quizzes

1. Navigate to Quiz page
2. Select subject, topic, and difficulty
3. Answer MCQ questions
4. Submit to see results and explanations

Group Discussion

1. Go to Group Discussion page
2. Select a topic
3. Participate with AI participants
4. Get evaluated on participation, relevance, and politeness

Viewing Analytics

1. Visit Analytics page
2. See Placement Readiness Score
3. View skill radar chart
4. Check weak areas and learning path
5. Track improvement over time

Generating Reports

1. Go to Reports page
2. Generate interview-specific or overall reports
3. Download PDF for sharing

Troubleshooting

Common Issues

Camera/Microphone not working:

• Ensure browser has permissions
• Use HTTPS in production
• Check device availability

API Errors:

• Verify API keys are correct
• Check rate limits
• Ensure MongoDB is accessible

Proctoring not detecting:

• Ensure good lighting
• Face should be clearly visible
• YOLO model needs to be downloaded

Contributing

1. Fork the repository
2. Create feature branch
3. Commit changes
4. Push to branch
5. Create Pull Request

License

MIT License

Support

For issues and questions, please create an issue in the repository.

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Built with AI-powered technologies for the next generation of interview preparation.