off_load.py

import streamlit as st
import logging
from streamlit_chat import message
from langchain.chains import ConversationalRetrievalChain
from langchain.embeddings import HuggingFaceEmbeddings, CacheBackedEmbeddings, HuggingFaceInstructEmbeddings
from langchain.llms import LlamaCpp
from langchain.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import PyPDFLoader
from langchain.storage import LocalFileStore
from langchain.llms import HuggingFaceHub
from langchain.embeddings import HuggingFaceInstructEmbeddings

from datetime import datetime
import os
import tempfile
import requests  # Import requests here

now = datetime.now()
underlying_embeddings = HuggingFaceEmbeddings()



def initialize_session_state():
    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 🤗"]

    if 'past' not in st.session_state:
        st.session_state['past'] = ["Hey! 👋"]

def conversation_chat(query, chain, history):
    result = chain({"question": query, "chat_history": history})
    history.append((query, result["answer"]))
    return result["answer"]

def cache_checker(question, question_cache, chain):
    # Check if the response is already cached
    logging.info("I'm here")
    if question in question_cache:
        response = question_cache[question]
        logging.info("Response retrieved from cache.")
    else:
        # Perform the Q&A operation
        response = chain({"question": question})
        question_cache[question] = response["answer"]
        logging.info("Response computed and cached.")
    
    return response["answer"]

def display_chat_history(chain):
    reply_container = st.container()
    container = st.container()
    question_cache = {}
    with container:
        with st.form(key='my_form', clear_on_submit=True):
            user_input = st.text_input("Question:", placeholder="Ask about your PDF", key='input')
            submit_button = st.form_submit_button(label='Send')

        if submit_button and user_input:
            with st.spinner('Generating response...'):
                output = conversation_chat(user_input, chain, st.session_state['history'])
                # Check if the question is being cached
                if user_input:
                    if user_input in question_cache:
                        st.info("Response retrieved from cache.")
                        response = question_cache[user_input]
                    else:
                        st.info("Response computed.")
                        response = cache_checker(user_input, question_cache, chain)
                        question_cache[user_input] = response

                    # Display the response
                    st.write("Response:", response)

            st.session_state['past'].append(user_input)
            st.session_state['generated'].append(output)

    if st.session_state['generated']:
        with reply_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="thumbs")
                message(st.session_state["generated"][i], key=str(i), avatar_style="fun-emoji")

def create_conversational_chain(vector_store):
    # Create llm
    llm = LlamaCpp(
        streaming=True,
        model_path="mistral-7b-instruct-v0.1.Q2_K.gguf",
        temperature=0.75,
        top_p=1, 
        verbose=True,
        n_ctx=4096
    )
    # llm = HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta",  model_kwargs={
    #     "temperature": 0.75,
    #     "n_ctx": 4096,
    #     "streaming":True,
    #     "top_p": 0.99,
    #     "verbose": True,
    #     "max_length": 4096
    # })

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

    chain = ConversationalRetrievalChain.from_llm(llm=llm, chain_type='stuff',
                                                retriever=vector_store.as_retriever(search_kwargs={"k": 2}),
                                                memory=memory)
    return chain

def main():
    # Initialize session state
    initialize_session_state()
    st.title("Medbot :books:")
    # Initialize Streamlit
    st.sidebar.title("Document Processing")
    uploaded_files = st.sidebar.file_uploader("Upload files", accept_multiple_files=True)

    if uploaded_files:
        text = []
        for file in uploaded_files:
            file_extension = os.path.splitext(file.name)[1]
            with tempfile.NamedTemporaryFile(delete=False) as temp_file:
                temp_file.write(file.read())
                temp_file_path = temp_file.name
       #  Initialize cache store
            cache_store = LocalFileStore("./cache/")
            loader = None
            if file_extension == ".pdf":
                loader = PyPDFLoader(temp_file_path)

            if loader:
                text.extend(loader.load())
                os.remove(temp_file_path)

        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
        text_chunks = text_splitter.split_documents(text)

       # Create embeddings
        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", 
                                           model_kwargs={'device': 'cpu'})

        # Create cache-backed embeddings
        cached_embeddings = CacheBackedEmbeddings.from_bytes_store(embeddings, cache_store, namespace="embeddings")

         # Cache the embeddings
        #cache_store.save("embeddings", cached_embeddings)

        # Create vector store
        vector_store = FAISS.from_documents(text_chunks, embedding=cached_embeddings)


        # Create the chain object
        chain = create_conversational_chain(vector_store)

        display_chat_history(chain)

if __name__ == "__main__":
    main()