import streamlit as st 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", 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 " " 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 " " 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'], st.session_state.session_rating ]) 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() #-------------- 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 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 """) 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":st.session_state.session_rating } st.session_state.history.append(log) update_worksheet_qa() 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() # 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)