🧠 AI-Powered MCQ and Q&A Generator

Welcome to the AI-Powered MCQ and Q&A Generator, a cutting-edge tool designed to transform your documents into interactive learning resources! This project leverages advanced natural language processing (NLP) models to generate multiple-choice questions (MCQs) and provide answers to custom questions based on uploaded documents. Whether you're a student, educator, or professional, this tool simplifies studying and content exploration with an intuitive interface and robust functionality.

🌟 Features

• MCQ Generation: Automatically create multiple-choice questions from PDF documents with one correct answer and three distractors.
• Question Answering: Ask any question about the content of your document and receive accurate, context-based answers.
• Multi-Format Support: Process PDFs, DOCX, and TXT files (with primary focus on PDFs).
• Streamlit Web Interface: A user-friendly UI powered by Streamlit for seamless interaction.
• Chunked Text Processing: Efficiently handle large documents by splitting them into manageable chunks.
• Hugging Face Integration: Utilize powerful NLP models like EleutherAI/gpt-neo-1.3B and mistralai/Mistral-7B-Instruct-v0.1 for text generation.
• GPU Acceleration: Optimized for GPU usage to speed up processing (configurable for lighter models).
• Vector Store (Chroma): Experimental support for document embeddings to enhance question answering (placeholder implementation).

🚀 Getting Started

Prerequisites

• Python 3.8+
• A Hugging Face API key (set up in a .env file)
• GPU (optional, for faster processing)
• Required libraries (listed in requirements.txt)

Installation

1. Clone the Repository:

   git clone https://github.com/rohanmistry231/AI-Powered-MCQ-QA-Generator.git
   cd AI-Powered-MCQ-QA-Generator

2. Set Up 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. Configure Environment Variables: Create a .env file in the project root and add your Hugging Face API key:

   HUGGINGFACE_API_KEY=your_huggingface_api_key
   

Running the Application

• Streamlit App (app.py): Launch the web interface:

  streamlit run app.py

  Open your browser to http://localhost:8501 and upload a PDF to start generating MCQs or asking questions.

• Command-Line MCQ Generator (mcq_app.py or model.py): Run the script to generate MCQs from a PDF:

  python mcq_app.py

  Enter the path to your PDF and the number of questions when prompted.

• Command-Line Q&A Tool (qa_app.py): Run the script to ask questions about a document:

  python qa_app.py

  Provide the file path and ask questions interactively.

📂 Project Structure

ai-mcq-qa-generator/
├── app.py               # Streamlit web app for MCQ and Q&A
├── mcq_app.py           # Command-line MCQ generator
├── qa_app.py            # Command-line Q&A tool
├── model.py             # Core logic for MCQ generation (shared with mcq_app.py)
├── .env                 # Environment variables (API keys)
├── .gitignore           # Git ignore file
├── requirements.txt     # Python dependencies
└── README.md            # Project documentation

🛠️ How It Works

1. Document Loading: Upload a PDF (or other supported formats) via the Streamlit UI or command-line scripts.
2. Text Extraction: Extract text using PyPDF2 or fitz (PyMuPDF) for PDFs, with support for chunking large documents.
3. MCQ Generation:
   • Text is processed in chunks to handle large documents.
   • Hugging Face models generate MCQs with one correct answer and three incorrect options.
4. Question Answering:
   • Text is chunked and optionally stored in a Chroma vector store (placeholder embedding).
   • Custom questions are answered using context-aware prompts sent to Hugging Face models.
5. Output: Results are displayed in the Streamlit UI or printed to the console.

💡 Use Cases

• Education: Generate practice quizzes for students from textbooks or lecture notes.
• Training: Create assessments for corporate training materials.
• Self-Study: Explore and understand complex documents by asking targeted questions.
• Content Review: Quickly generate questions to test comprehension of reports or articles.

🧪 Future Improvements

• Add support for more document formats (e.g., Markdown, HTML).
• Implement real embeddings for Chroma vector store to improve Q&A accuracy.
• Enhance MCQ formatting for export to formats like JSON or CSV.
• Optimize chunking and model parameters for faster processing.
• Add user authentication and session management for the Streamlit app.

🤝 Contributing

We welcome contributions! To get started:

1. Fork the repository.
2. Create a feature branch (git checkout -b feature/YourFeature).
3. Commit your changes (git commit -m "Add YourFeature").
4. Push to the branch (git push origin feature/YourFeature).
5. Open a pull request.

🙏 Acknowledgments

• Hugging Face for providing powerful NLP models.
• Streamlit for the intuitive web interface framework.
• PyMuPDF and PyPDF2 for PDF processing.
• LangChain for document chunking and vector store utilities.

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Ready to transform your documents into interactive learning tools? Dive in and start generating MCQs and answers today! 🚀