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, GooglePalmEmbeddings
from langchain.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.chat_models import ChatGooglePalm
from langchain.llms import HuggingFaceHub
from htmlTemplates import bot_template, user_template, css

from transformers import pipeline

def get_pdf_text(pdf_files):
    
    text = ""
    for pdf_file in pdf_files:
        reader = PdfReader(pdf_file)
        for page in 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_vector_store(text_chunks):
    
    # For OpenAI Embeddings
    
    # embeddings = OpenAIEmbeddings()
    
    # For Huggingface Embeddings

    # embeddings = HuggingFaceInstructEmbeddings(model_name = "hkunlp/instructor-xl")

    # For GooglePalm Embeddings

    embeddings = GooglePalmEmbeddings(model_name = "models/embedding-gecko-001")

    vectorstore = FAISS.from_texts(texts = text_chunks, embedding = embeddings)
    
    return vectorstore

def get_conversation_chain(vector_store):
    
    # OpenAI Model

    # llm = ChatOpenAI()

    # HuggingFace Model

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

    # GooglePalm Model

    llm = ChatGooglePalm(model_name="models/chat-bison-001", model_kwargs={"temperature":0.7, "max_length":800})

    memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)

    conversation_chain = ConversationalRetrievalChain.from_llm(
        llm = llm,
        retriever = vector_store.as_retriever(),
        memory = memory
    )

    return conversation_chain

def handle_user_input(question):

    response = st.session_state.conversation({'question':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()
    st.set_page_config(page_title='Chat with Your own PDFs', page_icon=':books:')

    st.write(css, unsafe_allow_html=True)
    
    if "conversation" not in st.session_state:
        st.session_state.conversation = None

    if "chat_history" not in st.session_state:
        st.session_state.chat_history = None
    
    
    st.header('Chat with Your own PDFs :books:')
    question = st.text_input("Ask anything to your PDF: ")

    if question:
        handle_user_input(question)

    with st.sidebar:
        st.subheader("Upload your Documents Here: ")
        pdf_files = st.file_uploader("Choose your PDF Files and Press OK", type=['pdf'], accept_multiple_files=True)

        if st.button("OK"):
            with st.spinner("Processing your PDFs..."):

                # Get PDF Text
                raw_text = get_pdf_text(pdf_files)

                # Get Text Chunks
                text_chunks = get_chunk_text(raw_text)
                

                # Create Vector Store
                
                vector_store = get_vector_store(text_chunks)
                st.write("DONE")

                # Create conversation chain

                st.session_state.conversation =  get_conversation_chain(vector_store)


if __name__ == '__main__':
    main()