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