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InterviewMaster AI

🎯 InterviewMaster AI

AI-powered interview coaching platform with real-time feedback, adaptive study plans, and voice analysis

Python FastAPI React TypeScript PostgreSQL Redis License

Features • Architecture • Quick Start • API • Contributing

📸 Overview

InterviewMaster AI is a full-stack interview preparation platform that uses AI agents to deliver personalized coaching. Upload your resume, practice with adaptive mock interviews, get real-time AI feedback, and track your progress — all from a premium, modern interface.

✨ Features

🤖 AI-Powered Interview Engine

  • Adaptive Question Generation — AI generates role-specific questions based on your resume, target role, and difficulty level
  • Real-Time Answer Evaluation — Get scored on relevance, depth, clarity, and structure with actionable suggestions
  • Multi-Category Support — Technical, behavioral, system design, and situational questions

📄 Resume Intelligence

  • Resume Upload & Parsing — Supports PDF, DOCX formats with AI-powered text extraction
  • Skill Extraction — Automatically identifies technical skills, experience level, and career trajectory
  • Resume-Based Interviews — AI generates interview questions specifically from your resume content

📋 Adaptive Study Plans

  • 30-Day Personalized Plans — AI creates day-by-day study schedules based on your skill gaps
  • Gap Analysis — Identifies weak areas and prioritizes improvement areas
  • Progress Tracking — Track daily completion and adjust plans dynamically

🏢 Company-Specific Coaching

  • FAANG & Top Company Prep — Tailored coaching for specific companies (Google, Amazon, Meta, etc.)
  • Company Culture Insights — Learn interview patterns and expectations per company
  • Role-Specific Strategies — Different coaching paths for SDE, PM, Data Science, and more

🎤 Voice & Media Recording

  • Interview Modes — Choose flexible Practice sessions or strict Mock Interviews (requires video/audio)
  • Dual-Mode Inputs — Answer via text or record voice/video responses
  • Speech-to-Text — Whisper-powered transcription of voice recordings (CPU/GPU supported)
  • Voice Analysis — Speaking pace, pause detection, filler word tracking, and confidence scoring
  • Audio/Video Playback — Review your recordings with AI-annotated feedback

📊 Analytics & Progress Dashboard

  • Performance Analytics — Track scores across sessions with interactive charts
  • Skill Heatmap — Visual breakdown of strengths and weaknesses by category
  • Session History — Complete history of all practice sessions with detailed reviews
  • Improvement Trends — See how your scores evolve over time

🏆 Gamification System

  • Achievements & Badges — Unlock achievements for milestones (first interview, streak goals, high scores)
  • Practice Streaks — Daily streak tracking with streak protection
  • Leaderboard — Compare your progress with other users
  • XP & Level System — Earn experience points and level up

⚙️ Additional Features

  • JWT Authentication — Secure login/register with access & refresh tokens
  • Email Service — Password reset, welcome emails (SMTP/Gmail/SendGrid/SES)
  • Data Export — Export session data and analytics as PDF reports
  • Admin Dashboard — User management and system monitoring
  • Redis Caching — High-performance caching for analytics and session data
  • Dark Mode UI — Premium glassmorphism design with smooth animations

🏗 Architecture

┌─────────────────────────────────────────────────────────┐
│                    Frontend (React 18)                   │
│         TypeScript • Material-UI • Framer Motion        │
│              Vite Dev Server (port 5173)                 │
└───────────────────────┬─────────────────────────────────┘
                        │ REST API
┌───────────────────────▼─────────────────────────────────┐
│                  Backend (FastAPI)                       │
│       SQLAlchemy • Alembic • Pydantic • Loguru          │
│            Uvicorn Server (port 8000)                    │
├─────────────┬───────────────────────┬───────────────────┤
│  AI Agents  │    Media Services     │   Auth & Core     │
│ ─────────── │ ───────────────────── │ ───────────────── │
│ Resume      │ Whisper STT           │ JWT Auth          │
│ Study Plan  │ Librosa Analysis      │ User Management   │
│ Company     │ FFmpeg Processing     │ Email Service     │
│ Coaching    │ Audio/Video Storage   │ Data Export       │
└──────┬──────┴───────────┬───────────┴────────┬──────────┘
       │                  │                    │
┌──────▼──────┐   ┌───────▼───────┐   ┌───────▼───────┐
│  AI / LLM   │   │  PostgreSQL   │   │    Redis      │
│  Providers   │   │   Database    │   │    Cache      │
│(Gemini, HF) │   │  (port 5432)  │   │  (port 6379)  │
└─────────────┘   └───────────────┘   └───────────────┘

Tech Stack

Layer Technologies
Frontend React 18, TypeScript, Material-UI (MUI), Framer Motion, Redux Toolkit, Vite
Backend Python 3.10+, FastAPI, SQLAlchemy 2.0, Alembic, Pydantic v2
Database PostgreSQL 16, Redis 7
AI/ML Google Gemini API, HuggingFace Inference API (Llama-3), LangChain, Whisper (Speech-to-Text), Librosa
Media FFmpeg, PyAV, SoundFile
Auth JWT (Access + Refresh Tokens), Passlib/Bcrypt
DevOps Docker, Docker Compose, GitHub Actions CI/CD

🚀 Quick Start

Prerequisites

  • Python 3.10+
  • Node.js 18+
  • PostgreSQL 16+
  • Redis 7+
  • FFmpeg (for audio/video processing)

Option 1: Docker (Recommended)

# Clone the repository
git clone https://github.com/Logesh-Murugan/AI-Powered-Interview-Coach.git
cd AI-Powered-Interview-Coach

# Set up environment variables
cp backend/.env.example backend/.env
# Edit backend/.env with your API keys

# Start all services
docker-compose up -d

# Access the application
# Frontend: http://localhost:5173
# Backend:  http://localhost:8000
# API Docs: http://localhost:8000/docs

Option 2: Manual Setup

1. Database Setup

# Create PostgreSQL database
createdb interviewmaster

# Start Redis server
redis-server

2. Backend

cd backend

# Create virtual environment
python -m venv venv
venv\Scripts\activate        # Windows
# source venv/bin/activate   # macOS/Linux

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Edit .env with your database URL and API keys

# Run database migrations
alembic upgrade head

# Start the server
python -m uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload

3. Frontend

cd frontend

# Install dependencies
npm install

# Start development server
npm run dev

4. Access

Service URL
Frontend http://localhost:5173
Backend API http://localhost:8000
API Documentation http://localhost:8000/docs
Health Check http://localhost:8000/health

🔑 Environment Variables

Create a backend/.env file based on backend/.env.example:

# Required
DATABASE_URL=postgresql://user:password@localhost:5432/interviewmaster
SECRET_KEY=your-secure-secret-key-minimum-32-characters

# AI Providers (At least one is recommended)
GEMINI_API_KEY=your-gemini-api-key
HUGGINGFACE_API_KEY=your-huggingface-api-key

# Optional (for additional features)
REDIS_HOST=localhost
EMAIL_ENABLED=False
SMTP_HOST=smtp.gmail.com
SMTP_USERNAME=your-email@gmail.com
SMTP_PASSWORD=your-app-password

Note: Get your Gemini API key from Google AI Studio

📡 API Reference

Core Endpoints

Method Endpoint Description
POST /api/v1/auth/register Register a new user
POST /api/v1/auth/login Login and get tokens
POST /api/v1/auth/refresh Refresh access token
GET /api/v1/users/me Get current user profile

Interview System

Method Endpoint Description
POST /api/v1/interviews/ Create new interview session
GET /api/v1/interviews/ List all sessions
GET /api/v1/interviews/{id} Get session details
POST /api/v1/interviews/{id}/submit Submit answer
GET /api/v1/interviews/{id}/summary Get session summary

AI Features

Method Endpoint Description
POST /api/v1/resumes/upload Upload resume for analysis
POST /api/v1/resume-analysis/analyze AI resume analysis
POST /api/v1/study-plans/ Generate study plan
POST /api/v1/company-coaching/ Get company coaching

Analytics & Gamification

Method Endpoint Description
GET /api/v1/analytics/summary Performance analytics
GET /api/v1/achievements/ User achievements
GET /api/v1/streaks/ Streak data
GET /api/v1/leaderboard/ Global leaderboard

📖 Full interactive API documentation available at /docs when running the backend.

📁 Project Structure

Ai_powered_interview_coach/
├── backend/
│   ├── app/
│   │   ├── config.py              # Application configuration
│   │   ├── database.py            # Database connection
│   │   ├── main.py                # FastAPI app entry point
│   │   ├── models/                # SQLAlchemy models
│   │   ├── routes/                # API route handlers
│   │   ├── schemas/               # Pydantic schemas
│   │   ├── services/              # Business logic
│   │   │   ├── agents/            # AI agent system
│   │   │   │   ├── resume_agent_service.py
│   │   │   │   ├── study_plan_agent_service.py
│   │   │   │   └── company_coaching_agent_service.py
│   │   │   ├── ai/               # AI provider orchestration
│   │   │   │   ├── orchestrator.py
│   │   │   │   ├── circuit_breaker.py
│   │   │   │   └── quota_tracker.py
│   │   │   ├── analytics_service.py
│   │   │   ├── evaluation_service.py
│   │   │   └── media_service.py
│   │   ├── middleware/            # Request validation
│   │   └── utils/                 # Utilities
│   ├── alembic/                   # Database migrations
│   ├── requirements.txt
│   ├── Dockerfile
│   └── .env.example
├── frontend/
│   ├── src/
│   │   ├── components/            # Reusable UI components
│   │   ├── pages/                 # Application pages
│   │   │   ├── LandingPage.tsx
│   │   │   ├── dashboard/
│   │   │   ├── interview/
│   │   │   ├── ai/               # Resume, Study Plans, Coaching
│   │   │   ├── analytics/
│   │   │   ├── achievements/
│   │   │   └── leaderboard/
│   │   ├── services/              # API service layer
│   │   ├── store/                 # Redux state management
│   │   └── theme/                 # MUI theme configuration
│   ├── package.json
│   └── vite.config.ts
├── docker-compose.yml
├── LICENSE
└── README.md

🧪 Running Tests

# Backend tests
cd backend
pytest --cov=app tests/

# Frontend tests
cd frontend
npm test

🤝 Contributing

Contributions are welcome! Here's how to get started:

  1. Fork the repository
  2. Create a feature branch 
    git checkout -b feature/your-feature-name
  3. Commit your changes 
    git commit -m "feat: add your feature description"
  4. Push to your branch 
    git push origin feature/your-feature-name
  5. Open a Pull Request

Commit Convention

Prefix Usage
feat: New feature
fix: Bug fix
docs: Documentation
style: Formatting
refactor: Code refactoring
test: Adding tests
chore: Maintenance

👨‍💻 Author

Logesh M
📧 logeshmuruganofficial@gmail.com
🔗 LinkedIn — linkedin.com/in/logesh01

📄 License

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

Built with ❤️ by Logesh M
Empowering candidates with AI-driven coaching to land their dream jobs

About

🤖 AI-powered interview preparation platform with multi-provider AI, real-time feedback, analytics, gamification, and personalized coaching. Built with FastAPI, React, PostgreSQL, and Redis.

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aifastapiinterview-preparationpythonreact

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