EduExtract - AI-Powered Quiz Generation Platform

A Flask-based web application that automatically generates quizzes from PDF documents using Natural Language Processing and Machine Learning techniques.

🚀 Features

Core Functionality

PDF Text Extraction: Automatically extracts text content from uploaded PDF documents
AI-Powered Quiz Generation: Creates three types of quizzes:
- Multiple Choice Questions (MCQ)
- Fill-in-the-blank questions
- Subjective questions
Smart Answer Evaluation: Uses semantic similarity for answer checking
Performance Analytics: Visual charts showing quiz performance over time

User Management

User registration and authentication
Password reset with OTP verification via email
Session management
Quiz history tracking

Additional Features

PDF Result Export: Download quiz results as formatted PDF
Email Notifications: OTP-based password recovery
Performance Dashboard: Graphical representation of quiz scores
Responsive Web Interface: Modern HTML/CSS templates

🛠️ Technology Stack

Backend

Flask: Web framework
SQLite: Database for user data and quiz results
PyMuPDF (fitz): PDF text extraction
NLTK: Natural language processing
Transformers: Hugging Face models for question generation
Sentence Transformers: Semantic similarity checking
Flask-Mail: Email functionality

Frontend

HTML5/CSS3
JavaScript
Responsive design

AI/ML Components

Question Generation: Valhalla T5-small model
Semantic Similarity: SentenceTransformer all-MiniLM-L6-v2
NLP Processing: NLTK for tokenization and POS tagging

📋 Prerequisites

Python 3.8 or higher
pip (Python package installer)
Gmail account for email functionality (or modify email settings)

🔧 Installation

Clone the repository

git clone https://github.com/Mru-preeti/python_project_sem4.git
cd python_project_sem4-1

Create a virtual environment

python -m venv venv

# On Windows
venv\Scripts\activate

# On macOS/Linux
source venv/bin/activate

Install dependencies
```
pip install -r requirements.txt
```

Download NLTK data

python -c "import nltk; nltk.download('averaged_perceptron_tagger'); nltk.download('punkt'); nltk.download('wordnet'); nltk.download('stopwords')"

Configure email settings

Update the email configuration in app.py:

app.config['MAIL_USERNAME'] = 'your-email@gmail.com'
app.config['MAIL_PASSWORD'] = 'your-app-password'

Initialize the database
```
python database.py
```

🚀 Running the Application

Start the Flask application
```
python app.py
```
Access the application
- Open your web browser and go to http://localhost:5000

🐳 Docker Deployment

Quick Start with Docker

Build the Docker image
```
docker build -t eduextract .
```
Run the container
```
docker run -p 5000:5000 eduextract
```

Advanced Docker Configuration

With environment variables:

docker run -p 5000:5000 \
  -e FLASK_ENV=production \
  -e MAIL_USERNAME=your-email@gmail.com \
  -e MAIL_PASSWORD=your-app-password \
  eduextract

With volume mounting for persistent data:

docker run -p 5000:5000 \
  -v $(pwd)/uploads:/app/uploads \
  -v $(pwd)/*.db:/app/ \
  eduextract

Docker Compose (create docker-compose.yml):

version: "3.8"
services:
  eduextract:
    build: .
    ports:
      - "5000:5000"
    volumes:
      - ./uploads:/app/uploads
      - ./Login_data.db:/app/Login_data.db
    environment:
      - FLASK_ENV=production
      - MAIL_USERNAME=your-email@gmail.com
      - MAIL_PASSWORD=your-app-password

Docker Features

Pre-built NLTK data: NLTK datasets are downloaded during image build
System dependencies: Includes necessary system packages for ML libraries
Optimized caching: Dockerfile layers optimized for faster rebuilds
Production ready: Configured for production deployment

📁 Project Structure

python_project_sem4-1/
├── app.py                 # Main Flask application
├── database.py            # Database initialization
├── requirements.txt       # Python dependencies
├── dockerfile            # Docker configuration
├── README.md             # Project documentation
├── static/               # CSS, JS, and image files
│   ├── styles.css
│   ├── quiz.css
│   ├── script.js
│   └── ...
├── templates/            # HTML templates
│   ├── login.html
│   ├── home.html
│   ├── quiz.html
│   ├── result.html
│   └── ...
├── uploads/              # PDF file storage
└── *.db                 # SQLite databases

🎯 Usage Guide

1. User Registration/Login

Register with username, email, and password
Login with your credentials
Use forgot password feature if needed

2. Creating a Quiz

Navigate to the quiz creation page
Upload a PDF document
Select quiz type (MCQ, Fill-in-blank, or Subjective)
The system will automatically generate 5 questions

3. Taking a Quiz

Answer the generated questions
Submit your responses
View immediate results with detailed feedback

4. Performance Tracking

View quiz history
Check performance analytics
Download results as PDF

🔧 Configuration

Email Settings

Configure SMTP settings in app.py:

app.config['MAIL_SERVER'] = 'smtp.gmail.com'
app.config['MAIL_PORT'] = 587
app.config['MAIL_USE_TLS'] = True
app.config['MAIL_USERNAME'] = 'your-email@gmail.com'
app.config['MAIL_PASSWORD'] = 'your-app-password'

Database Configuration

The application uses SQLite by default. Database files:

Login_data.db: User authentication and quiz results
quiz.db: Quiz-specific data
your_database.db: Additional data storage

🤖 AI Models Used

Question Generation: valhalla/t5-small-qg-hl
- Generates questions from text passages
- Optimized for educational content
Semantic Similarity: all-MiniLM-L6-v2
- Evaluates answer correctness
- Provides similarity scores for subjective answers

🛡️ Security Features

Password validation with complexity requirements
Session management
OTP-based password recovery
SQL injection prevention with parameterized queries

🐛 Troubleshooting

Common Issues

Model Download Errors
- Ensure stable internet connection
- Models are downloaded automatically on first use
Email Not Sending
- Check Gmail app password configuration
- Verify SMTP settings
PDF Processing Errors
- Ensure PDF is text-based (not scanned images)
- Check file size limitations

Error Logs

Check console output for detailed error messages and debugging information.

🤝 Contributing

Fork the repository
Create a feature branch (git checkout -b feature/AmazingFeature)
Commit your changes (git commit -m 'Add some AmazingFeature')
Push to the branch (git push origin feature/AmazingFeature)
Open a Pull Request

📄 License

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

👥 Authors

Team Members: Mru-preeti and contributors
Project: Semester 4 Python Project

🙏 Acknowledgments

Hugging Face for pre-trained models
NLTK for natural language processing tools
Flask community for excellent documentation
PyMuPDF for PDF processing capabilities

📞 Support

For support and questions:

Create an issue on GitHub
Contact: srushtiprajakt.t@gmail.com

Note: Make sure to update email credentials and other sensitive configuration before deploying to production.

Name		Name	Last commit message	Last commit date
Latest commit History 26 Commits
static		static
templates		templates
.gitignore		.gitignore
README.md		README.md
app.py		app.py
database.py		database.py
dockerfile		dockerfile
quiz.db		quiz.db
requirements.txt		requirements.txt
your_database.db		your_database.db

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