app.py

# This version is the same model with only different UI, to be a chat-like experience

import streamlit as st
from streamlit_chat import message as st_message
import pandas as pd
import numpy as np
import datetime
import gspread
import pickle
import os
import csv
import json
import torch
from tqdm.auto import tqdm
from langchain.text_splitter import RecursiveCharacterTextSplitter


# from langchain.vectorstores import Chroma
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceInstructEmbeddings


from langchain import HuggingFacePipeline
from langchain.chains import RetrievalQA




st.set_page_config(
    page_title = 'aitGPT',
    page_icon = '✅')




@st.cache_data
def load_scraped_web_info():
    with open("ait-web-document", "rb") as fp:
        ait_web_documents = pickle.load(fp)
        
        
    text_splitter = RecursiveCharacterTextSplitter(
        # Set a really small chunk size, just to show.
        chunk_size = 500,
        chunk_overlap  = 100,
        length_function = len,
    )

    chunked_text = text_splitter.create_documents([doc for doc in tqdm(ait_web_documents)])


@st.cache_resource
def load_embedding_model():
    embedding_model = HuggingFaceInstructEmbeddings(model_name='hkunlp/instructor-base',
                                                model_kwargs = {'device': torch.device('cuda' if torch.cuda.is_available() else 'cpu')})
    return embedding_model

@st.cache_data
def load_faiss_index():
    vector_database = FAISS.load_local("faiss_index_web_and_curri_new", embedding_model) #CHANGE THIS FAISS EMBEDDED KNOWLEDGE
    return vector_database

@st.cache_resource
def load_llm_model():
    # llm = HuggingFacePipeline.from_model_id(model_id= 'lmsys/fastchat-t5-3b-v1.0', 
    #                                         task= 'text2text-generation',
    #                                         model_kwargs={ "device_map": "auto",
    #                                                     "load_in_8bit": True,"max_length": 256, "temperature": 0,
    #                                                     "repetition_penalty": 1.5})
    
    
    llm = HuggingFacePipeline.from_model_id(model_id= 'lmsys/fastchat-t5-3b-v1.0', 
                                        task= 'text2text-generation',
                                        
                                        model_kwargs={ "max_length": 256, "temperature": 0,
                                                      "torch_dtype":torch.float32,
                                                    "repetition_penalty": 1.3})
    return llm


def load_retriever(llm, db):
    qa_retriever = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff",
                            retriever=db.as_retriever())

    return qa_retriever

def retrieve_document(query_input):
    related_doc = vector_database.similarity_search(query_input)
    return related_doc

def retrieve_answer(query_input):
    prompt_answer=  query_input + " " + "Try to elaborate as much as you can."
    answer = qa_retriever.run(prompt_answer)
    output = st.text_area(label="Retrieved documents", value=answer[6:]) #this positional slicing helps remove "<pad> " at the beginning
    
    st.markdown('---')
    # score = st.radio(label = 'please select the rating score for overall satifaction and helpfullness of the bot answer', options=[0, 1,2,3,4,5], horizontal=True,
    #                  on_change=update_worksheet_qa, key='rating')

    return answer[6:] #this positional slicing helps remove "<pad> " at the beginning
    
# def update_score():
#     st.session_state.session_rating = st.session_state.rating


def update_worksheet_qa():
    # st.session_state.session_rating = st.session_state.rating
    #This if helps validate the initiated rating, if 0, then the google sheet would not be updated
    #(edited) now even with the score of 0, we still want to store the log because some users do not give the score to complete the logging
    # if st.session_state.session_rating  == 0:
    worksheet_qa.append_row([st.session_state.history[-1]['timestamp'].strftime(datetime_format), 
                            st.session_state.history[-1]['question'],
                            st.session_state.history[-1]['generated_answer'],
                             0])
    # else:
    #     worksheet_qa.append_row([st.session_state.history[-1]['timestamp'].strftime(datetime_format), 
    #                             st.session_state.history[-1]['question'],
    #                             st.session_state.history[-1]['generated_answer'], 
    #                             st.session_state.session_rating 
    #                             ])
        
def update_worksheet_comment():
    worksheet_comment.append_row([datetime.datetime.now().strftime(datetime_format),
                                feedback_input])
    success_message = st.success('Feedback successfully submitted, thank you', icon="✅",
               )
    time.sleep(3)
    success_message.empty()


def clean_chat_history():
    st.session_state.chat_history = []

#--------------


if "history" not in st.session_state: #this one is for the google sheet logging
    st.session_state.history = []


if "chat_history" not in st.session_state: #this one is to pass previous messages into chat flow
    st.session_state.chat_history = []
# if "session_rating" not in st.session_state:
#     st.session_state.session_rating = 0


credentials= json.loads(st.secrets['google_sheet_credential'])

service_account = gspread.service_account_from_dict(credentials)
workbook= service_account.open("aitGPT-qa-log")
worksheet_qa = workbook.worksheet("Sheet1")
worksheet_comment = workbook.worksheet("Sheet2")
datetime_format= "%Y-%m-%d %H:%M:%S"



load_scraped_web_info()
embedding_model = load_embedding_model()
vector_database = load_faiss_index()
llm_model = load_llm_model()
qa_retriever = load_retriever(llm= llm_model, db= vector_database)


print("all load done")




    



st.write("# aitGPT 🤖 ")
st.markdown("""
         #### The aitGPT project is a virtual assistant developed by the :green[Asian Institute of Technology] that contains a vast amount of information gathered from 205 AIT-related websites.  
        The goal of this chatbot is to provide an alternative way for applicants and current students to access information about the institute, including admission procedures, campus facilities, and more.  
          """)
st.write(' ⚠️ Please expect to wait **~ 10 - 20 seconds per question** as thi app is running on CPU against 3-billion-parameter LLM')

st.markdown("---")
st.write(" ")
st.write("""
         ### ❔ Ask a question
         """)


for chat in st.session_state.chat_history:
    st_message(**chat)

query_input = st.text_area(label= 'What would you like to know about AIT?' , key = 'my_text_input')
generate_button = st.button(label = 'Ask question!')

if generate_button:
    answer = retrieve_answer(query_input)
    log = {"timestamp": datetime.datetime.now(),
        "question":query_input,
        "generated_answer": answer,
        "rating":0 }

    st.session_state.history.append(log)
    update_worksheet_qa()
    st.session_state.chat_history.append({"message": query_input, "is_user": True})
    st.session_state.chat_history.append({"message": answer, "is_user": False})


clear_button = st.button("Start new convo",
                         on_click=clean_chat_history)


st.write(" ")
st.write(" ")

st.markdown("---")
st.write("""
         ### 💌 Your voice matters
         """)

feedback_input = st.text_area(label= 'please leave your feedback or any ideas to make this bot more knowledgeable and fun')
feedback_button = st.button(label = 'Submit feedback!')

if feedback_button:
    update_worksheet_comment()