app.py

from PyPDF2 import PdfReader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
import streamlit as st
from dotenv import load_dotenv,find_dotenv
from streamlit_extras.add_vertical_space import add_vertical_space
import pickle 
import os

from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI

## Slide-bar
with st.sidebar:
 st.title('PDF Q&A')
 st.markdown('''
 ## About
 This app is an LLM-powered chatbot built using:
 - [Streamlit](https://streamlit.io/)
 - [LangChain](https://python.langchain.com/)
 - [OpenAI](https://platform.openai.com/docs/models) LLM model
 
 ''')
 add_vertical_space(5)
 st.write('Made by Harshit')
 
def main():
 st.header("Q&A from Pdfs: ")
 
 
 load_dotenv(find_dotenv())
 
 pdf_reader = PdfReader('48lawsofpower.pdf')
 # st.write(pdf_reader)
 
 text = ""
 for page in pdf_reader.pages:
 text += page.extract_text()
 
 text_splitter = CharacterTextSplitter(
 separator = "\n",
 chunk_size = 1000,
 chunk_overlap = 200,
 length_function = len,
 )
 ## Chunk Formation
 chunks = text_splitter.split_text(text= text)
 
 ## Embedding 
 embeddings = OpenAIEmbeddings()
 document_search = FAISS.from_texts(chunks, embeddings)
 
 
 query = st.text_input("Ask your questions: ")
 
 docs = document_search.similarity_search(query=query)
 
 llm = OpenAI()
 chain = load_qa_chain(llm=llm, chain_type="stuff")
 response = chain.run(input_documents=docs, question=query)
 st.write(response)
 
 
if __name__ == '__main__':
 main()