🧠 AI Interview Trainer (Mock Q&A Engine)

AI Interview Trainer is a full-stack web application designed to simulate real-time mock interviews using advanced AI models. It evaluates your answers, scores your performance, and even lets you download interview summaries as PDFs — making it an ideal tool for job preparation.

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🚀 Key Features

• 🎤 Mock Interviews Powered by AI – Real-time Q&A using OpenRouter’s AI models.
• 🧠 Answer Scoring & Feedback – Evaluates answers for clarity, relevance, and completeness.
• 📄 PDF Summary Export – Download detailed summaries of your interview sessions.
• 📜 Interview History – View all your past interviews and feedback.
• 🔐 Authentication – Secure login using JWT tokens.
• 🌐 Modern UI – Built with React, Tailwind CSS, and Vite for a fast and responsive experience.

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⚙️ Setup Instructions

📁 Folder Structure

ai-interview-trainer/ ├── frontend/ # React + Vite + Tailwind CSS └── backend/ # Node.js + Express + MongoDB + PDFKit

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🔧 Backend Setup

1. Navigate to the backend directory:

   cd backend

2. Install dependencies:

   npm install

3.Create a .env file in the backend folder and add the following:

PORT=5000
MONGODB_URI=your_mongodb_connection_string
OPENROUTER_API_KEY=your_openrouter_api_key
JWT_SECRET=your_jwt_secret

4.Start the backend server:

npm start

🔧 Frontend Setup

cd ../frontend
npm install

Update the backend URL inside all API requests (you can use a constant file for this if needed).

Start frontend:

npm run dev

📁 Project Structure

oxeir-ai-interview-trainer/
│
├── backend/
│   ├── routes/
│   ├── controllers/
│   ├── models/
│   ├── utils/
│   ├── index.js
│   └── .env
│
├── frontend/
│   ├── src/
│   │   ├── components/
│   │   ├── pages/
│   │   ├── services/
│   │   └── App.jsx
│   └── vite.config.js
│
└── README.md

##🧠 AI Logic

• The backend uses OpenRouter's deepseek-chat-v3-0324 model.
• On starting an interview, domain-specific questions are generated.
• User answers are stored, and follow-up questions + feedback are generated.
• A summary is auto-generated and stored.
• A PDF version can be exported using pdfkit.

##🧾 API Endpoints

• Auth POST /api/login Request:

{
  "userId": "1234",
  "name": "Aman"
}

Response:

{
  "token": "<JWT_TOKEN>"
}

Interview

• POST /api/interview/start – Start a new interview session
• POST /api/interview/answer – Submit answer and get follow-up
• POST /api/interview/summary – Generate summary of the session
• POST /api/interview/download-pdf – Export summary as PDF

🌍 Deployment

• Frontend: Vercel
• Backend: Render
• Database: MongoDB Atlas

Make sure to set environment variables correctly on both platforms.

🙌 Acknowledgments

• OpenRouter for model access
• DeepSeek for AI model
• Vercel & Render for deployment