MCQ Generator: AI-Powered Multiple-Choice Question Creator

Overview

The MCQ Generator is an AI-driven project that automates the generation of multiple-choice questions (MCQs) from a given text, evaluates their difficulty, and presents them through an interactive web interface. This tool combines OpenAI's language models with LangChain and Streamlit to deliver a seamless experience for creating and interacting with educational content.

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📝 Table of Contents

• MCQ Generator: AI-Powered Multiple-Choice Question Creator
  • Overview
  • 📝 Table of Contents
  • 📁 Project Structure
  • ✨ Features
  • 💻 Installation
  • 🚀 Usage
  • ⚙️ Configuration
  • 📊 Data
  • 🔬 Experiments
  • 🙏 Acknowledgements

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📁 Project Structure

MCQ-Generator/
├── .gitignore
├── README.md
├── mcq_training_data.txt
├── requirements.txt
├── response.json
├── setup.py
├── streamlit.py
├── experiments/
│   ├── data/Machine_Learning_Quiz.csv
│   └── mcq.ipynb
└── src/
    ├── __init__.py
    └── mcqgenerator/
        ├── MCQgenerator.py
        ├── logger.py
        └── utils.py

✨ Features

• MCQ Generation: Generate MCQs from provided text using advanced natural language processing techniques.
• Complexity Evaluation: Assess the complexity of the generated MCQs.
• Web Interface: User-friendly web interface to interact with the MCQ generator.

💻 Installation

To install the necessary dependencies, run the following command:

pip install -r requirements.txt

🚀 Usage

To use the MCQ generator, run the streamlit.py script:

streamlit run streamlit.py

⚙️ Configuration

• Adjust project settings in setup.py.
• Manage logging configurations in src/mcqgenerator/logger.py.

You can configure various aspects of the project in the setup.py file and adjust logging settings in src/mcqgenerater/logger.py.

📊 Data

• Sample training data is available in mcq_training_data.txt.
• Example responses are provided in response.json.

🔬 Experiments

The experiments directory contains a Jupyter notebook (mcq.ipynb) and a CSV file with machine learning quiz data (machine_learning_quiz.csv).

🙏 Acknowledgements

A huge thanks to the teams behind LangChain and Streamlit for providing robust tools. Special appreciation to ineuron for the project opportunity.