shreekeshavan/optimalquest_objective-questions-with-multiple-correct-answers

📂OptimalQuest, an innovative project designed to convert text content from PDF files into engaging multiple-choice questions with multiple correct answers.🤔💭

OptimalQuest_Objective-Questions-with-Multiple-Correct-Answers (Creation of Objective Questions with Multiple Correct Answers)

Welcome to OptimalQuest, an innovative project designed to convert text content from PDF files into engaging multiple-choice questions with multiple correct answers.

Features

• Extract text from PDFs.
• Generates multiple-choice questions.
• Each question can have multiple correct answers.
• Utilizes machine learning and natural language processing for question generation.

Installation

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

1. Clone the repository: git clone https://github.com/ShreeKeshavan/OptimalQuest_Objective-Questions-with-Multiple-Correct-Answers.git
2. Install required libraries: pip install spacy PyPDF2

Libraries Used

• Spacy: For natural language processing tasks.
• Random: To generate randomness for question generation.
• PyPDF2: To read PDF files.
• Warnings: To suppress warnings.

How to Use

1. Load the English language model using Spacy.

   import spacy
   nlp_model = spacy.load("en_core_web_sm")

2. Extracting Text from PDFs: The extract_text_from_pdfs function is used to extract text from one or more PDF documents and concatenate it into a single text string. This is done using the PdfReader object from the PyPDF2 library. Each page from each PDF is read and the text is extracted and appended to the output string.

   Here is an example of how to use this function:

   # Define the PDF documents to extract text from
   pdf_files = ['chapter-2.pdf', 'chapter-3.pdf', 'chapter-4.pdf']
   
   # Extract text
   extracted_text = extract_text_from_pdfs(pdf_files)

3. Call the get_mca_questions function with the extracted text from the PDF and the number of questions as arguments. This will generate a list of multiple-choice questions.

   get_mca_questions(text_context, num_of_questions)