Overview

This Flask application provides a simple web interface for processing text input and files. Users can upload text files or input text directly to generate multiple-choice questions. The application also supports downloading results in PDF and Word formats.

Technology and Methodology

Technologies Used

• spaCy: A powerful NLP library for processing and analyzing text. It is used for tokenization, lemmatization, and sentence segmentation.
• scikit-learn: A machine learning library for Python. It provides tools for vectorization and feature extraction, specifically using the TfidfVectorizer for transforming text data into numerical features.
• NumPy: A library for numerical computations in Python. It is used for handling and manipulating arrays, such as sorting and selecting feature scores.

Methodology

1. Text Preprocessing:

   • Convert the input text to lowercase and remove newline characters.
   • Use spaCy to split the text into sentences and tokens.
   • Clean the tokens by removing stop words, punctuation, and digits, and perform lemmatization.

2. Feature Extraction:

   • Apply TfidfVectorizer to convert the cleaned sentences into TF-IDF features.
   • Calculate the TF-IDF scores for each feature and sort them to identify the most relevant terms.

3. Question Generation:

   • Select a fixed number of high-scoring features (terms) to create questions.
   • For each selected term, find its occurrence in the original sentences and replace it with a blank ("____").
   • Generate multiple-choice options by selecting random terms from the sorted features list, ensuring the correct answer is included among the choices.
   • Shuffle the choices and format them into a dictionary with letter options.

4. Question Formatting:

   • Create MCQ objects with the formatted question, choices, and the correct answer.
   • Ensure unique questions by checking for duplicates before adding them to the final list.

The process results in a set of multiple-choice questions that are designed to test comprehension of the key terms and concepts extracted from the input text.

Features

• Upload a .txt file or enter text directly.
• Generate multiple-choice questions from the text.
• Download the generated questions as a PDF or Word document.
• Contact Us page for sending inquiries.
• Feedback page for submitting feedback.
• Flash messages for user feedback on form submissions.

Project Structure

• app.py - The main Flask application script.
• templates/ - Directory containing HTML templates:
  • base.html - Base template with common layout and footer.
  • index.html - Home page for text input or file upload.
  • results.html - Results page displaying generated questions.
  • contact.html - Contact Us page.
  • feedback.html - Feedback page.
• static/ - Directory for static files:
  • style.css - CSS file for styling the application.
• core/ - Directory for core functionality:
  • text_processing.py - Contains functions for processing text.
  • pdf_operations.py - Contains functions for creating PDFs.
  • word_operations.py - Contains functions for creating Word documents.
• uploads/ - Directory for storing uploaded files.

Configuration

• UPLOAD_FOLDER: Directory for storing uploaded files.
• ALLOWED_EXTENSIONS: Allowed file extensions for uploads (currently .txt).

Usage

1. Navigate to the Home Page

   You can either enter text directly or upload a .txt file.

2. Submit Text or File

   • Enter text in the provided textarea or upload a .txt file.
   • Click "Submit" to process the input.

3. View Results

   • Generated questions will be displayed on the results page.
   • You can download the questions in PDF or Word format using the respective buttons.

4. Contact Us / Provide Feedback

   • Use the "Contact Us" and "Feedback" pages to send inquiries or provide feedback.

Installation

Prerequisites

• Python 3.x
• Flask
• Required Python libraries

Clone the Repository

git clone https://github.com/codeasarjun/mcq_genrator.git
cd <your folder>

Output