Gen AI Interview Preparation Web Application

A production-ready, full-stack web application for AI-powered job interview preparation. Users can upload resumes, analyze job descriptions, detect skill gaps, and generate AI-driven interview questions and ATS-optimized resumes. Built with React, Node.js, Express, MongoDB, JWT, Gemini AI, and Puppeteer.

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Table of Contents

• Project Overview
• Features
• Tech Stack
• Architecture
• Folder Structure
• Setup & Installation
• Environment Variables
• How It Works
• Contributing
• License

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Project Overview

This project simulates a real-world SaaS product for job seekers. It leverages Generative AI to:

• Parse and analyze resumes
• Compare with job descriptions
• Detect skill gaps
• Generate interview questions
• Create ATS-optimized resumes
• Export results as PDFs

Features

• Secure Authentication: JWT-based login, registration, and token blacklisting for logout.
• Resume Upload & Parsing: Users upload resumes (PDF/DOCX), which are parsed for skills and experience.
• Job Description Analysis: Paste or upload job descriptions to compare with your resume.
• Skill Gap Detection: AI highlights missing or weak skills.
• AI Interview Questions: Gemini AI generates tailored interview questions.
• ATS Resume Generation: Create resumes optimized for Applicant Tracking Systems.
• PDF Export: Download AI-generated resumes and reports as PDFs (via Puppeteer).
• Recent Reports: View and manage previous interview reports.

Tech Stack

• Frontend: React.js (Vite, React Router, Context API, SCSS)
• Backend: Node.js, Express.js
• Database: MongoDB Atlas
• Authentication: JWT, Token Blacklisting
• AI Integration: Gemini API (Google GenAI)
• PDF Generation: Puppeteer

Architecture

• Frontend: SPA with protected routes, context-based state, and modular features.
• Backend: RESTful API, layered structure (controllers, services, models, routes, middlewares).
• Security: Input validation, sanitization, and secure environment variable management.

Folder Structure

Backend/
  src/
    config/           # Database config
    controllers/      # API controllers
    middlewares/      # Auth, file, error handling
    models/           # Mongoose schemas
    routes/           # Express routes
    services/         # AI, business logic
  .env                # Backend environment variables
  package.json

Frontend/
  src/
    features/         # Auth, interview modules
    style/            # SCSS styles
    App.jsx           # Main app
    app.routes.jsx    # Routing
  .env                # Frontend environment variables
  package.json

Setup & Installation

Prerequisites

• Node.js (v18+ recommended)
• npm or yarn
• MongoDB Atlas account
• Gemini AI API key

1. Clone the Repository

git clone <your-repo-url>
cd GenAiInterviewPrep

2. Backend Setup

cd Backend
cp .env.example .env # Fill in your secrets
npm install
npm run dev

3. Frontend Setup

cd ../Frontend
cp .env.example .env # Set VITE_API_URL
npm install
npm run dev

4. Access the App

• Frontend: http://localhost:5173
• Backend API: http://localhost:3000

Environment Variables

• Backend: See Backend/.env.example for required variables (MongoDB URI, JWT secret, API keys).
• Frontend: See Frontend/.env.example (all variables must start with VITE_).

How It Works

1. Register/Login: Secure JWT authentication with token blacklisting for logout.
2. Upload Resume: Parse and extract skills/experience.
3. Paste Job Description: Analyze and compare with resume.
4. AI Skill Gap Detection: Gemini AI highlights gaps and generates interview questions.
5. Generate ATS Resume: Create and download optimized resumes as PDFs.
6. View Reports: Manage and review previous interview reports.

Contributing

1. Fork the repo
2. Create a feature branch
3. Commit your changes
4. Open a pull request

License

This project is for educational purposes.