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


import streamlit as st
from dotenv import load_dotenv




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()