PennwickFileAnalyzer.py

##############################################################
#   PDF Chat
#
#   Mike Pastor  February 2024


import streamlit as st
from dotenv import load_dotenv

from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter

from InstructorEmbedding import INSTRUCTOR
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 htmlTemplates import css, bot_template, user_template
from langchain.llms import HuggingFaceHub

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

#  Chunk size and overlap must not exceed the models capacity!
#
def get_text_chunks(text):
    text_splitter = CharacterTextSplitter(
        separator="\n",
        chunk_size=800,    #  1000
        chunk_overlap=200,
        length_function=len
    )
    chunks = text_splitter.split_text(text)
    return chunks


def get_vectorstore(text_chunks):
    # embeddings = OpenAIEmbeddings()

    #  pip install InstructorEmbedding
    #  pip install sentence-transformers==2.2.2
    embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")

    #  from InstructorEmbedding import INSTRUCTOR
    # model = INSTRUCTOR('hkunlp/instructor-xl')
    # sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
    # instruction = "Represent the Science title:"
    # embeddings = model.encode([[instruction, sentence]])

    # embeddings = model.encode(text_chunks)
    print('have Embeddings:   ')

    # text_chunks="this is a test"
    #   FAISS,  Chroma and other vector databases
    #
    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
    print('FAISS succeeds:   ')

    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})
    #  google/bigbird-roberta-base     facebook/bart-large
    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})
    # response = st.session_state.conversation({'summarization': user_question})
    st.session_state.chat_history = response['chat_history']


    # st.empty()

    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="MLP 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
    if "chat_history" not in st.session_state:
        st.session_state.chat_history = None

    st.header("Mike's PDF Chat :books:")

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

    # st.write( user_template, unsafe_allow_html=True)
    # st.write(user_template.replace( "{{MSG}}", "Hello robot!"), unsafe_allow_html=True)
    # st.write(bot_template.replace( "{{MSG}}", "Hello human!"), 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)

        # Upon button press
        if st.button("Process these files"):
            with st.spinner("Processing..."):

                #################################################################
                #  Track the overall time for file processing into Vectors
                # #
                from datetime import datetime
                global_now = datetime.now()
                global_current_time = global_now.strftime("%H:%M:%S")
                st.write("Vectorizing Files - Current Time =", global_current_time)

                # get pdf text
                raw_text = get_pdf_text(pdf_docs)
                #  st.write(raw_text)

                # # get the text chunks
                text_chunks = get_text_chunks(raw_text)
                # st.write(text_chunks)

                # # create vector store
                vectorstore = get_vectorstore(text_chunks)

                # # create conversation chain
                st.session_state.conversation = get_conversation_chain(vectorstore)

                # Mission Complete!
                global_later = datetime.now()
                st.write("Files Vectorized - Total EXECUTION Time =",
                         (global_later - global_now), global_later)


if __name__ == '__main__':
    main()