app.py

import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
from langchain.vectorstores.faiss import FAISS
from langchain.chat_models import ChatOpenAI 
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from htmlTemplates import css, bot_template, user_template 

def get_pdf_text(pdf_docs):
    text = ""
    for pdf in pdf_docs:
        pdf_reader = PdfReader(pdf)
        for pg in pdf_reader.pages:
            text += pg.extract_text()
    return text

def get_text_chunks(text):
    text_splitter = CharacterTextSplitter(separator='\n', chunk_size = 1000, chunk_overlap = 200, length_function = len)
    chunks = text_splitter.split_text(text)
    return chunks

def get_vector_embeddings(text):
    # embeddings = OpenAIEmbeddings()
    embeddings = HuggingFaceInstructEmbeddings(model_name = "hkunlp/instructor-xl")
    vectorstore = FAISS.from_texts(texts = text, embedding = embeddings)
    return vectorstore

def get_conversation_chain(vectorstore):
    llm = ChatOpenAI( )
    memory = ConversationBufferMemory(memory = 'chat_history', return_messages=True)
    conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=vectorstore.as_retriever(),memory = memory)
    return conversation_chain

def handle_userquestion(user_question): 
    # response = st.session_state.conversation({'question':user_question})
    response = st.session_state.conversation({'question': user_question})
    st.write(response )

def main():
    load_dotenv() 
    st.set_page_config(page_title='Chat with Multiple PDFs', page_icon=':books:')
    st.write(css, unsafe_allow_html=True )
    # if "conversation" not in st.session_state:
    #     st.session_state.conversation = None

    st.header("Chat with Multiple PDFs :books:")
    user_question = st.text_input("Ask Question about your documents:")
    if user_question:
        handle_userquestion(user_question)

    st.write(user_template.replace("{{MSG}}","Hello Robot"), unsafe_allow_html=True)
    st.write(bot_template.replace("{{MSG}}","Hello Human"), unsafe_allow_html=True )

    with st.sidebar:
        st.subheader('Your documents')
        pdf_docs = st.file_uploader("Upload your PDFs here and Click on Process", accept_multiple_files= True)
        if st.button('Process'):
            with st.spinner('Processing'):
                #get pdf text
                raw_text= get_pdf_text(pdf_docs)
                st.write(raw_text)

                #get text chunks
                text_chunks = get_text_chunks(raw_text)
                st.write(text_chunks)
                #create vector store
                vectorstore = get_vector_embeddings(text_chunks)

                #create conversation chain
                st.session_state.conversation = get_conversation_chain(vectorstore)
               
if __name__ == '__main__':
    main()