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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="Quiz Generator", page_icon="📝")
st.title('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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