app.py

##########################################################################
#   app.py   -  Pennwick Honeybee Robot
#
#   HuggingFace Spaces application to provide honeybee expertise
#           with open-source models
#
#   Mike Pastor  February 23, 2024


import streamlit as st
from streamlit.components.v1 import html
# from dotenv import load_dotenv
from PyPDF2 import PdfReader
from PIL import Image

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Local file
from htmlTemplates import css, bot_template, user_template


##################################################################################
#  Admin flags
DISPLAY_DIALOG_LINES = 6

SESSION_STARTED = False

# MODEL_NAME="deepset/roberta-base-squad2"
# MODEL_NAME="BEE-spoke-data/TinyLlama-3T-1.1bee"

# MODEL_NAME='HuggingFaceH4/zephyr-7b-beta'

##############################################################
#   Our model and tokenizer
#
MODEL_NAME = "facebook/blenderbot-400M-distill"

# MODEL_NAME = "facebook/blenderbot-3B"

model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)




##################################################################################
def process_user_question(user_question):


    # if not SESSION_STARTED:
    #     print('No Session')
    #     st.write( 'Please upload and analyze your PDF files first!')
    #     return

    if user_question == None:
        print('question is null')
        return
    if user_question == '':
        print('question is blank')
        return
    if st == None:
        print('session is null')
        return
    if st.session_state == None:
        print('session STATE is null')
        return

    print('question is: ', user_question)
    print('\nsession is: ', st)



    #################################################################
    #  Track the overall time for training & submission preparation
    # #
    from datetime import datetime
    global_now = datetime.now()
    global_current_time = global_now.strftime("%H:%M:%S")
    print("# app.py Starting up...  - Current Time =", global_current_time)

    st.write(('Question:    ' + user_question ), unsafe_allow_html=True)


    #  input_text = input('Say something-->  ')

    print( 'history--> ', st.session_state.history_string)
    ################################################################
    #  Tokenize the user prompt and conversation history
    inputs = tokenizer.encode_plus( st.session_state.history_string, user_question, return_tensors="pt" )

    # st.write('Len of inputs= ', len( inputs))
    #  Generate a response
    outputs = model.generate( **inputs )

    # decode the response
    response = tokenizer.decode( outputs[0], skip_special_tokens=True).strip()

    #  append history
    st.session_state.conversation_history.append(user_question)
    st.session_state.conversation_history.append(response)

    # st.session_state.history_string = "/n".join(st.session_state.conversation_history)
    st.session_state.history_string = "<br>".join( st.session_state.conversation_history )

    st.write( 'Response:   ', response)


    # Mission Complete!
    ##################################################################################
    global_later = datetime.now()
    st.write("Total query execute Time =", (global_later - global_now), global_later)



#################################################################################
def main():
    print('Pennwick Starting up...\n')

    ##################################################################
    #  Initial conversation tracking
    if not hasattr(st.session_state, "conversation_history"):
        st.session_state.conversation_history  = []
    if not hasattr(st.session_state, "history_string"):
        st.session_state.history_string  = "\n".join(st.session_state.conversation_history)


    # Load the environment variables - if any
    # load_dotenv()

    st.set_page_config(page_title="Pennwick Honeybee Robot",
                       page_icon="./HoneybeeLogo.ico")

    st.write(css, unsafe_allow_html=True)

    st.image("./HoneybeeLogo.png", width=96)
    st.header(f"Pennwick Honeybee Robot")
    st.write( "BETA TEST VERSION only!", unsafe_allow_html=True)

    print('Prepared page...\n')


    user_question = None
    user_question = st.text_input("Ask the  Open Source - "+MODEL_NAME+" - Model  any question about Honeybees...")
    if user_question != None:
        print('calling process question', user_question)
        process_user_question(user_question)

    html_history_string = ""
    if len( st.session_state.history_string ) > 100:
        html_history_string = st.session_state.history_string[-100:]
    else:
        html_history_string = st.session_state.history_string

    html(html_history_string , height=150, scrolling=True)
    # 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("Which documents would you like to analyze?")
    #     st.subheader("(no data is saved beyond the session)")
    #
    #     pdf_docs = st.file_uploader(
    #         "Upload your PDF documents here and click on 'Analyze'", accept_multiple_files=True)
    #
    #     # Upon button press
    #     if st.button("Analyze 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 = extract_pdf_text(pdf_docs)
    #             #  st.write(raw_text)
    #
    #             # # get the text chunks
    #             text_chunks = extract_bitesize_pieces(raw_text)
    #             # st.write(text_chunks)
    #
    #             # # create vector store
    #             vectorstore = prepare_embedding_vectors(text_chunks)
    #
    #             # # create conversation chain
    #             st.session_state.conversation = prepare_conversation(vectorstore)
    #
    #             SESSION_STARTED = True
    #
    #             # Mission Complete!
    #             global_later = datetime.now()
    #             st.write("Files Vectorized - Total EXECUTION Time =",
    #                      (global_later - global_now), global_later)
    #

if __name__ == '__main__':
    main()