InterviewQGen

InterviewQGen is a web-based tool designed to generate tailored interview questions based on job descriptions. Using advanced AI technologies, it transforms job descriptions into a list of insightful interview questions categorized into Easy, Medium, and Hard levels.

Features

• Job Description Input: Enter a detailed job description to get relevant questions.
• AI-Powered Question Generation: Utilizes state-of-the-art AI models to craft questions tailored to the job role.
• User-Friendly Interface: Professional and dynamic UI for a seamless user experience.
• Real-Time Processing: Instant question generation with a visually appealing loader.
• Caching: Efficient caching mechanism to speed up repeated requests.
• Concurrency: Optimized handling of multiple requests for better performance.

Technologies Used

• Frontend: HTML, CSS, JavaScript
• Backend: FastAPI
• AI Models: Hugging Face Transformers for summarization, LangChain OpenAI for question generation
• Environment: Python, Uvicorn for serving the FastAPI application

Installation

1. Clone the Repository

   git clone https://github.com/yourusername/InterviewQGen.git
   cd InterviewQGen

2. Set Up a Virtual Environment

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install Dependencies

   pip install -r requirements.txt

4. Create a .env File

   Add your OpenAI API key to a .env file in the root directory:

   OPENAI_API_KEY=your_openai_api_key
   

Usage

1. Run the Application

   uvicorn main:app --reload --workers 4

   This command starts the FastAPI server with 4 worker processes for handling requests.

2. Access the API

   The API will be available at http://127.0.0.1:8000.

3. Endpoints

   • POST /generate: Generates interview questions based on a job description.

     Request Body:

     {
       "description": "The job description text here."
     }

     Response:

     {
       "questions": "Generated interview questions here."
     }

Code Overview

• main.py: Contains the FastAPI application code including endpoints, model initialization, and logic for summarizing job descriptions and generating interview questions.
• requirements.txt: Lists the Python packages required for the application.

Contributing

Contributions are welcome! Please follow the standard GitHub fork-and-pull request workflow. Ensure that any changes are tested and adhere to the project's coding standards.

License

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

Acknowledgments

• Hugging Face Transformers: For the BART model used in summarization.
• OpenAI: For the API used in generating interview questions.
• FastAPI: For providing the web framework to build this application.

For any questions or feedback, please open an issue or reach out via email.