app.py

import PyPDF2
import nltk
import random
import streamlit as st
from openai import OpenAI
from dotenv import load_dotenv
import os

# Download NLTK data (if not already downloaded)
nltk.download('punkt')

# load the environment variables into the python script
load_dotenv() 
# fetching the openai_api_key environment variable
openai_api_key = os.getenv('OPENAI_API_KEY')

def extract_text_from_pdf(pdf_file):
 pdf_reader = PyPDF2.PdfReader(pdf_file)
 text = ""
 for page_num in range(len(pdf_reader.pages)):
 text += pdf_reader.pages[page_num].extract_text()
 return text

def generate_mcqs_on_topic(text, topic, num_mcqs=5):
 # Tokenize the text into sentences
 sentences = nltk.sent_tokenize(text)

 # Randomly select sentences to create Questions
 selected_sentences = random.sample(sentences, min(num_mcqs, len(sentences)))

 mcqs = []
 for sentence in selected_sentences:
 # Use ChatGPT for interactive question generation
 chatgpt_question = generate_question_with_chatgpt(sentence, topic)
 mcqs.append(chatgpt_question)
 print(mcqs)
 return mcqs

def extract_options_and_correct_answer(api_response):
 if 'choices' in api_response:
 choices = api_response['choices']
 if isinstance(choices, list) and choices: # Check if 'choices' is a non-empty list
 message = choices[0].get('message', {})
 content = message.get('content', "Unable to generate a question..")
 options = message.get('options', [])
 correct_answer = message.get('correct_answer', "Unknown")

 return content, options, correct_answer

 return "Unexpected API response format.", [], "Unknown"


def generate_question_with_chatgpt(context, topic):
 client = OpenAI()
 # Initializing the default value
 generated_question = {
 'content': "Unable to generate a question..",
 'options': [], # assuming options is a list
 'correct_answer': "Unknown"
 }

 result = client.chat.completions.create(
 model="gpt-3.5-turbo",
 max_tokens=1024,
 temperature=0.7,
 messages=[
 {"role": "system", "content": "You are a helpful assistant."},
 {"role": "user", "content": f"What is the question on {topic} for the following? {context}"},
 ]
 )

 print("API Response:", result) # Print the API response for debugging

 # Modify the logic based on the actual structure of the 'result'
 if 'choices' in result:
 choices = result['choices']
 if isinstance(choices, list) and choices:
 choice = choices[0]
 if 'message' in choice and isinstance(choice['message'], dict):
 message = choice['message']
 content = message.get('content')

 if content:
 options = message.get('options', [])
 correct_answer = message.get('correct_answer', "Unknown")

 generated_question['content'] = content
 generated_question['options'] = options if isinstance(options, list) else []
 generated_question['correct_answer'] = correct_answer

 return generated_question



def main(): 
 # Title of the Application
 st.header("🤖CB Quiz Generator🧠", divider='rainbow')
 st.subheader("☕CoffeeBeans☕")

 # User input 
 pdf_file = st.file_uploader("Upload PDF Document:", type=["pdf"])
 num_mcqs = st.number_input("Enter Number of MCQs to Generate:", min_value=1, step=1, value=5)
 topic = st.text_input("Enter the Topic in which the quiz has to be generated")

 # Button to trigger QUIZ generation
 if st.button("Generate Quiz"):
 if pdf_file:
 text = extract_text_from_pdf(pdf_file)
 mcqs = generate_mcqs_on_topic(text, topic, num_mcqs)

 # Display the generated Questions
 st.success(f"Generated {num_mcqs} Questions:")
 for i, generated_question in enumerate(mcqs, start=1):
 st.write(f"\nQuestion {i}: {generated_question['content']}")
 st.write(f"Options: {', '.join(generated_question['options'])}")
 st.write(f"Correct Answer: {generated_question['correct_answer']}")
 else:
 st.error("Please upload a PDF document.")

if __name__ == "__main__":
 main()