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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 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, options, and correct answers | |
def generate_quiz_content(text, num_questions): | |
messages = [{'role': 'system', 'content': 'You are a helpful assistant.'}] | |
for i in range(num_questions): | |
user_message = {'role': 'user', 'content': f'Generate question {i + 1} from the given text:\n{text}'} | |
messages.append(user_message) | |
response = openai.ChatCompletion.create( | |
model='gpt-3.5-turbo-16k', | |
messages=messages, | |
max_tokens=200, | |
temperature=0.7 | |
) | |
quiz_data = [] | |
for choice in response.get('choices', []): | |
content = choice.get('content', '').strip() | |
options_start = content.find("Options:") + len("Options:") | |
correct_answer_start = content.find("Correct Answer:") + len("Correct Answer:") | |
question = content[:options_start].strip() | |
options_str = content[options_start:correct_answer_start].strip() | |
correct_answer = content[correct_answer_start:].strip() | |
options = [opt.strip() for opt in options_str.split(',')] | |
quiz_data.append((question, options, correct_answer)) | |
return quiz_data | |
# Function to retrieve quiz data from the vector database | |
def retrieve_quiz_data(vectorDB, num_questions): | |
# Replace this with your actual logic to query the vector database and retrieve quiz data | |
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)] | |
# 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') | |
if __name__ =='__main__': | |
st.set_page_config(page_title="CB Quiz Generator", page_icon="π") | |
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) | |