AI Interview Question Generator

A full-stack application that extracts text from resumes and job descriptions, then uses Google's Gemini AI to generate personalized interview questions.

Features

• File Upload Support: Upload resume and job description files in multiple formats (.txt, .pdf, .doc, .docx)
• Text Extraction: Extract raw text from various document formats
• AI Question Generation: Generate personalized interview questions using Google Gemini AI
• Categorized Questions: Questions are categorized by type (technical, behavioral, experience) and difficulty
• Clean UI: Modern, responsive React frontend

Setup Instructions

Prerequisites

• Python 3.8+
• React 19.1+
• Google Gemini API Key

Backend Setup

1. Navigate to the server directory:

   cd server

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Set up environment variables:

   cp .env.example .env

   Then edit .env and add your Gemini API key:

   GEMINI_API_KEY=your_actual_api_key_here
   

5. Start the FastAPI server:

   python main.py

   The server will run on http://localhost:8000

Frontend Setup

1. Navigate to the client directory:

   cd client

2. Install dependencies:

   npm install

3. Start the development server:

   npm run dev

   The React app will run on http://localhost:5173

Getting a Gemini API Key

1. Go to Google AI Studio
2. Sign in with your Google account
3. Create a new API key
4. Copy the API key to your .env file

API Endpoints

• GET /health - Health check
• POST /extract-text - Upload files and generate questions (main endpoint)

Usage

1. Open the React app in your browser
2. Upload a resume file and a job description file
3. Click "Generate Questions"
4. The app will:
   • Extract text from both files
   • Send the extracted text to Gemini AI
   • Generate personalized interview questions
   • Display the questions with categories and difficulty levels

File Structure

questions-generation/
├── server/
│   ├── services/
│   │   ├── text_extraction_service.py
│   │   └── gemini_ai_service.py
│   ├── main.py
│   ├── requirements.txt
│   ├── .env.example
│   └── .env
├── client/
│   ├── src/
│   │   ├── App.jsx
│   │   ├── App.css
│   │   └── main.jsx
│   ├── package.json
│   └── vite.config.js
└── README.md

Response Format

The main endpoint returns:

{
  "resume": "extracted resume text...",
  "job_description": "extracted job description text...",
  "generated_questions": {
    "questions": [
      {
        "id": 1,
        "question": "Question text",
        "category": "technical",
        "difficulty": "medium",
        "focus_area": "Python programming"
      }
    ],
    "summary": {
      "total_questions": 10,
      "technical_questions": 4,
      "behavioral_questions": 3,
      "experience_questions": 3
    }
  },
  "success": true
}

Error Handling

• If the Gemini API key is missing or invalid, the app will still extract text but won't generate questions
• Unsupported file formats will show an error message
• Network errors and parsing errors are handled gracefully

Technologies Used

• Backend: FastAPI, Python, Google Generative AI
• Frontend: React, Vite
• File Processing: PyPDF2, python-docx
• AI: Google Gemini 1.5 Flash

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request