MCQ Generator

MCQ Generator is a Flask-based web application that enables users to generate multiple-choice questions (MCQs) from text input. This tool is designed for educators, students, and content creators looking to automate quiz generation.

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✨ Features

• ✅ Accepts input via URL, direct text entry, or file upload (PDF/TXT)
• ✅ Dynamically generates MCQs using NLP and deep learning
• ✅ Users can choose the number of questions to generate
• ✅ Displays questions in interactive quiz or detailed list formats
• ✅ Allows users to download questions as a PDF
• ✅ Simple, user-friendly UI with light/dark mode
• ✅ Responsive design powered by Bootstrap

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🧰 Technologies Used

• Python
• Flask – Backend web framework
• spaCy – NLP processing and word embeddings
• TensorFlow/Keras – LSTM model for sentence structure learning
• BeautifulSoup + Requests – Web scraping for URL input
• PyPDF2 – PDF text extraction
• ReportLab – PDF generation
• Bootstrap – Frontend UI framework
• Gunicorn – WSGI HTTP server for deployment

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📁 Folder Structure

your_project_folder/
├── app.py              # Main Flask application logic
├── requirements.txt    # Project dependencies
├── README.md           # Project documentation
├── static/             # Static files (CSS, JS, images)
│   ├── style.css
│   └── main.js
└── templates/          # HTML templates
    ├── index.html
    ├── mcqs.html
    ├── result.html
    ├── 404.html
    └── 500.html

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🚀 Installation

1. Clone the Repository

git clone <repository-url>
cd <repository-folder-name>

If the files are already present locally, simply navigate to the project folder.

2. Create a Virtual Environment

On Windows:

python -m venv venv
.�env\Scripts�ctivate

On macOS/Linux:

python3 -m venv venv
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

Note: Installing TensorFlow and spaCy may take some time.

4. Download spaCy Model

python -m spacy download en_core_web_md

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🧪 Usage

Run the Application

python app.py

Then visit http://127.0.0.1:10000/ (or the port shown in your terminal).

Generate MCQs

1. Choose input type: URL, text entry, or file upload (PDF/TXT)
2. Select the number of questions
3. Click "Generate MCQs"

Interact with Questions

• Use the quiz view to test yourself
• Click "Show Answers" to reveal correct choices
• Switch to List View for a full breakdown
• Download the MCQs as a PDF
• Toggle between light/dark themes via the navbar

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🌐 Deployment

To deploy on platforms like Heroku, Render, or Railway:

Procfile

Create a Procfile (no extension) in the root directory:

web: gunicorn app:app

Platform Configuration

Build Command:

pip install -r requirements.txt && python -m spacy download en_core_web_md

Start Command:

gunicorn app:app

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⚠️ Notes

• Security: Replace the default app.secret_key with a secure, randomly generated one. Use environment variables for production.
• Model Performance: The LSTM model is lightly trained and may not generalize well. For production, consider training on a large, domain-specific dataset.
• Compatibility: Maintain versions in requirements.txt to avoid conflicts, especially with TensorFlow and NumPy.
• Error Handling: Custom 404 and 500 pages are included, along with basic file size limits.

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Happy Coding! 🚀