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 import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from htmltemplate import css,user_template,bot_template






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

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


def get_conversation_chain(vectorstore):
    llm = ChatOpenAI()
    # llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512})

    memory = ConversationBufferMemory(
        memory_key='chat_history', return_messages=True)
    conversation_chain = ConversationalRetrievalChain.from_llm(
        llm=llm,
        retriever=vectorstore.as_retriever(),
        memory=memory
    )
    return conversation_chain
def handle_userinput(user_question):
    response = st.session_state.conversation({'question': user_question})
    st.session_state.chat_history = response['chat_history']

    for i, message in enumerate(st.session_state.chat_history):
        if i % 2 == 0:
            st.write(user_template.replace(
                "{{MSG}}", message.content), unsafe_allow_html=True)
        else:
            st.write(bot_template.replace(
                "{{MSG}}", message.content), unsafe_allow_html=True)




def main():
    load_dotenv()
    open_api_key=os.getenv('OPEN_API_KEY')
    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 a question about docs:")
    
    if user_question:
        handle_userinput(user_question)
        
    
    st.write(user_template.replace("{{MSG}}","Hello Chitti"),unsafe_allow_html=True)
    
    st.write(bot_template.replace("{{MSG}}","Hello vassu"),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("Proccessing"):
                raw_text = get_pdf_text(pdf_docs)
                
                
                #get text chunks
                text_chunks=get_chunk_text(raw_text)
                
                
                vectorstore = get_vectorstore(text_chunks)
                
                
                st.session_state.conversation = get_conversation_chain(
                    vectorstore)
                
             
                
                
            
                
                
                 
if __name__=='__main__':
    main()