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Create app.py
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app.py
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import io
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import os
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import streamlit as st
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from dotenv import load_dotenv
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from PyPDF2 import PdfReader
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from langchain_community.embeddings import OpenAIEmbeddings
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from langchain_community.llms import OpenAI
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from langchain.prompts import PromptTemplate
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from langchain_community.vectorstores import FAISS
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import openai
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# Load environment variables
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load_dotenv()
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openai_api_key = os.getenv('OPENAI_API_KEY')
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# Initialize Streamlit session states
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if 'vectorDB' not in st.session_state:
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st.session_state.vectorDB = None
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# Function to extract text from a PDF file
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def get_pdf_text(pdf):
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text = ""
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pdf_reader = PdfReader(pdf)
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for page in pdf_reader.pages:
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text += page.extract_text()
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return text
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# Function to create a vector database
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def get_vectorstore(text_chunks):
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embeddings = OpenAIEmbeddings(api_key=openai_api_key)
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vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
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return vectorstore
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# Function to split text into chunks
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def get_text_chunks(text):
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text_chunks = text.split('\n\n') # Modify this based on your text splitting requirements
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return text_chunks
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# Function to retrieve quiz data from the vector database
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def retrieve_quiz_data(vectorDB, num_questions):
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# Retrieve stored quiz data from the vector database
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# You need to implement the logic to query the vector database and get quiz data
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# For illustration purposes, assuming you have a function named 'query_vector_database'
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quiz_data = query_vector_database(vectorDB, num_questions)
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return quiz_data
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# Function to generate quiz questions
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def generate_quiz(quiz_name, quiz_topic, num_questions, pdf_content):
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st.header(f"Quiz Generator: {quiz_name}")
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st.subheader(f"Topic: {quiz_topic}")
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# Process PDF and create vector database
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if st.button('Generate Quiz'):
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st.session_state['vectorDB'] = processing(pdf_content)
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st.success('PDF Processed and Vector Database Created')
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# Generate Quiz Questions
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if st.session_state.vectorDB:
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# Retrieve quiz data from the vector database
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generated_quiz_data = retrieve_quiz_data(st.session_state.vectorDB, num_questions)
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# Display retrieved questions, options, and correct answers
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for i, (question, options, correct_answer) in enumerate(generated_quiz_data):
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st.subheader(f"Question {i + 1}")
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st.write(f"Retrieved Question: {question}")
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st.write(f"Options: {', '.join(options)}")
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st.write(f"Correct Answer: {correct_answer}")
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# Save button to store vector database
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if st.session_state.vectorDB:
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if st.button('Save Vector Database'):
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st.success('Vector Database Saved')
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# Replace this function with your logic to query the vector database and get quiz data
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def query_vector_database(vectorDB, num_questions):
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# Implement your logic to query the vector database and retrieve quiz data
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# This is a placeholder, replace it with your actual implementation
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return [(f"Question {i + 1}", [f"Option {j}" for j in range(1, 5)], f"Option {i % 4 + 1}") for i in range(num_questions)]
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if __name__ =='__main__':
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st.set_page_config(page_title="CB Quiz Generator", page_icon="📝")
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st.title('CB Quiz Generator')
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# User inputs
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quiz_name = st.text_input('Enter Quiz Name:')
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quiz_topic = st.text_input('Enter Quiz Topic:')
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num_questions = st.number_input('Enter Number of Questions:', min_value=1, value=1, step=1)
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pdf_content = st.file_uploader("Upload PDF Content for Questions:", type='pdf')
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# Generate quiz if all inputs are provided
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if quiz_name and quiz_topic and num_questions and pdf_content:
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generate_quiz(quiz_name, quiz_topic, num_questions, pdf_content)
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