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| import os | |
| import streamlit as st | |
| from dotenv import load_dotenv | |
| from PyPDF2 import PdfReader | |
| from langchain_openai import OpenAI, OpenAIEmbeddings | |
| from langchain.prompts import PromptTemplate | |
| from langchain.chains import LLMChain | |
| from langchain_community.vectorstores import FAISS | |
| # 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 process PDF and create vector database | |
| def processing(pdf): | |
| raw_text = get_pdf_text(pdf) | |
| text_chunks = get_text_chunks(raw_text) | |
| vectorDB = get_vectorstore(text_chunks) | |
| return vectorDB | |
| # 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('Process PDF'): | |
| st.session_state['vectorDB'] = processing(pdf_content) | |
| st.success('PDF Processed and Vector Database Created') | |
| # Generate Quiz Questions | |
| for i in range(1, num_questions + 1): | |
| st.subheader(f"Question {i}") | |
| question = st.text_input(f"Enter Question {i}:", key=f"question_{i}") | |
| options = [] | |
| for j in range(1, 5): | |
| option = st.text_input(f"Option {j}:", key=f"option_{i}_{j}") | |
| options.append(option) | |
| correct_answer = st.selectbox(f"Correct Answer for Question {i}:", options=options, key=f"correct_answer_{i}") | |
| # Save question, options, and correct answer in vector database | |
| if st.session_state.vectorDB: | |
| # Create a prompt template for question and options | |
| template = f"Quiz: {quiz_name}\nTopic: {quiz_topic}\nQuestion: {question}\nOptions: {', '.join(options)}\nCorrect Answer: {correct_answer}" | |
| prompt = PromptTemplate(template=template) | |
| # Store question data in vector database | |
| st.session_state.vectorDB.add(prompt.generate(), embedding=None) | |
| # Save button to store vector database | |
| if st.session_state.vectorDB: | |
| if st.button('Save Vector Database'): | |
| st.success('Vector Database Saved') | |
| if __name__ =='__main__': | |
| st.set_page_config(page_title="CB Quiz Generator", page_icon="📝") | |
| st.title('🤖CB Quiz Generator🧠') | |
| st.subheader('Powered By CoffeeBeans') | |
| # 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=5, 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) | |