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") query_input = st.text_input(label= 'your question') 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)