app.py

import tempfile 
import streamlit as st 
from streamlit_chat import message

import torch
import torch.nn

import transformers
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    HfArgumentParser,
    TrainingArguments,
    pipeline,
    logging,
)


import pandas as pd
import numpy as np
import os
import io

from langchain.document_loaders import TextLoader
from langchain import PromptTemplate
from langchain.text_splitter import CharacterTextSplitter
from langchain.document_loaders import PyPDFLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain.chains import RetrievalQA
from langchain import HuggingFacePipeline
from langchain.chains import ConversationalRetrievalChain

from helper import pdf_loader,splitDoc,makeEmbeddings,create_flan_t5_base

    
def conversational_chat(chain,query):    
    result = chain({"question": query, 
    "chat_history": st.session_state['history']})
    st.session_state['history'].append((query, result["answer"]))

    return result["answer"]
    

def ui(): 
    st.title('PDF Question Answer Bot')
    # hugging_face_key = os.environ["HUGGINGFACE_HUB_TOKEN"]
    llm = create_flan_t5_base(load_in_8bit=False)
    hf_llm = HuggingFacePipeline(pipeline=llm)

    uploaded_file = st.file_uploader("Choose a PDF file", type=["pdf"]) 
    #saving the uploaded pdf file 
    if uploaded_file is not None:
        save_path = "./uploaded_file.pdf"
        with open(save_path, "wb") as f:
            f.write(uploaded_file.read())
    
    #loading the pdf file 
        pdf_doc=pdf_loader('./uploaded_file.pdf')
        pdf_doc=splitDoc(pdf_doc)
        vector_database = makeEmbeddings(pdf_doc)
        #making the retriever of the vector database 
        retriever = vector_database.as_retriever(search_kwargs={"k":10})
        qa_chain  = ConversationalRetrievalChain.from_llm(llm = hf_llm,
            retriever=vector_database.as_retriever())

        # Create an empty container to hold the PDF loader section
        pdf_loader_container = st.empty()
        
        # Check if the PDF file is uploaded or not
        if uploaded_file is not None:
            st.text("The file has been uploaded successfully")
            # Hide the PDF loader interface when the file is uploaded
            pdf_loader_container.empty()
            # Show the chat interface
            show_chat_interface(qa_chain)

def show_chat_interface(qa_chain):
    if 'history' not in st.session_state:
        st.session_state['history'] = []
    
    if 'generated' not in st.session_state:
        st.session_state['generated'] = ["Hello ! Ask me anything about the Uploaded PDF " + " 🤗"]
    
    if 'past' not in st.session_state:
        st.session_state['past'] = ["Hey ! 👋"]

    response_container = st.container()
    #container for the user's text input
    container = st.container()
    
    with container:
        with st.form(key='my_form', clear_on_submit=True):
            
            user_input = st.text_input("Query:", placeholder="Talk about your PDF data here (:", key='input')
            submit_button = st.form_submit_button(label='Send')
            
        if submit_button and user_input:
            output = conversational_chat(qa_chain,user_input)
            
            st.session_state['past'].append(user_input)
            st.session_state['generated'].append(output)
    
    if st.session_state['generated']:
            with response_container:
                for i in range(len(st.session_state['generated'])):
                    message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="big-smile")
                    message(st.session_state["generated"][i], key=str(i), avatar_style="thumbs")


if __name__=='__main__': 
    ui()