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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)
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