app.py

import streamlit as st
import pandas as pd
import numpy as np
import datetime
import pickle
import os
import csv
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.markdown(f"Number of Documents: {len(ait_web_documents)}")
    # st.markdown(f"Number of chunked texts: {len(chunked_text)}")




@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", embedding_model)
    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

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


if "history" not in st.session_state:
    st.session_state.history = []
if "session_rating" not in st.session_state:
    st.session_state.session_rating = 0
    
def update_score():
    st.session_state.session_rating = st.session_state.rating


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")



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)
    
    st.markdown('---')
    score = st.radio(label = 'please select the overall satifaction and helpfullness of the bot answer', options=[1,2,3,4,5], horizontal=True,
                     on_change=update_score, key='rating')

    return answer
    



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("---")


query_input = st.text_area(label= 'What would you like to know about AIT?')
generate_button = st.button(label = 'Submit!')

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

    st.session_state.history.append(log)



if st.session_state.session_rating == 0:
    pass
else:
    with open('test_db', 'a') as csvfile:
        writer = csv.writer(csvfile)
        writer.writerow([st.session_state.history[-1]['timestamp'], st.session_state.history[-1]['question'],
                            st.session_state.history[-1]['generated_answer'], st.session_state.session_rating ])
        st.session_state.session_rating = 0

test_df = pd.read_csv("test_db", index_col=0)
test_df.sort_values(by = ['timestamp'],
                    axis=0,
                    ascending=False,
                    inplace=True)
st.dataframe(test_df)