app.py

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


import streamlit as st
from dotenv import load_dotenv

from PyPDF2 import PdfReader

from htmlTemplates import css, bot_template, user_template


#  from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain_community.embeddings import HuggingFaceInstructEmbeddings

# from langchain.vectorstores import FAISS
from langchain_community.vectorstores import FAISS

from langchain.text_splitter import CharacterTextSplitter

# 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):

    st.write('Here in vector store....', unsafe_allow_html=True)
    # embeddings = OpenAIEmbeddings()

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

    st.write('Here in vector store - got embeddings ', unsafe_allow_html=True)
    #  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()