app.py

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


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_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_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):
        # Display user message
        if i % 2 == 0:
            st.write(user_template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
        else:
            print(message)
            # Display AI response
            st.write(bot_template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
            # Display source document information if available in the message
            if hasattr(message, 'source') and message.source:
                st.write(f"Source Document: {message.source}", unsafe_allow_html=True)


def safe_vec_store():
    os.makedirs('vectorstore', exist_ok=True)
    filename = 'vectores' + datetime.now().strftime('%Y%m%d%H%M') + '.pkl'
    file_path = os.path.join('vectorstore', filename)
    vector_store = st.session_state.vectorstore

    # Serialize and save the entire FAISS object using pickle
    with open(file_path, 'wb') as f:
        pickle.dump(vector_store, f)



def main():
    load_dotenv()
    st.set_page_config(page_title="Doc Verify RAG", page_icon=":hospital:")
    st.write(css, unsafe_allow_html=True)

    st.subheader("Your documents")
    pdf_docs = st.file_uploader("Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
    filenames = [file.name for file in pdf_docs if file is not None]

    if st.button("Process"):
        with st.spinner("Processing"):
            loaded_vec_store = None
            for filename in filenames:
                if ".pkl" in filename:
                    file_path = os.path.join('vectorstore', filename)
                    with open(file_path, 'rb') as f:
                        loaded_vec_store = pickle.load(f)
            raw_text = get_pdf_text(pdf_docs)
            text_chunks = get_text_chunks(raw_text)
            vec = get_vectorstore(text_chunks)
            if loaded_vec_store:
                vec.merge_from(loaded_vec_store)
                st.warning("loaded vectorstore")
            if "vectorstore" in st.session_state:
                vec.merge_from(st.session_state.vectorstore)
                st.warning("merged to existing")
            st.session_state.vectorstore = vec
            st.session_state.conversation = get_conversation_chain(vec)
            st.success("data loaded")

    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("Doc Verify RAG :hospital:")
    user_question = st.text_input("Ask a question about your documents:")
    if user_question:
        handle_userinput(user_question)

    with st.sidebar:

        st.subheader("Classification Instrucitons")
        classifier_docs = st.file_uploader("Upload your instructions here and click on 'Process'", accept_multiple_files=True)
        filenames = [file.name for file in classifier_docs if file is not None]

        if st.button("Process Classification"):
            with st.spinner("Processing"):
                loaded_vec_store = None
                for filename in filenames:
                    if ".pkl" in filename:
                        file_path = os.path.join('vectorstore', filename)
                        with open(file_path, 'rb') as f:
                            loaded_vec_store = pickle.load(f)
                raw_text = get_pdf_text(pdf_docs)
                text_chunks = get_text_chunks(raw_text)
                vec = get_vectorstore(text_chunks)
                if loaded_vec_store:
                    vec.merge_from(loaded_vec_store)
                    st.warning("loaded vectorstore")
                if "vectorstore" in st.session_state:
                    vec.merge_from(st.session_state.vectorstore)
                    st.warning("merged to existing")
                st.session_state.vectorstore = vec
                st.session_state.conversation = get_conversation_chain(vec)
                st.success("data loaded")

        # Save and Load Embeddings
        if st.button("Save Embeddings"):
            if "vectorstore" in st.session_state:
                safe_vec_store()
                # st.session_state.vectorstore.save_local("faiss_index")
                st.sidebar.success("safed")
            else:
                st.sidebar.warning("No embeddings to save. Please process documents first.")

        if st.button("Load Embeddings"):
            st.warning("this function is not in use, just upload the vectorstore")


if __name__ == '__main__':
    main()