Create conversational-app-with.py

Change the UI form from one-query-based into chat-like flow

conversational-app-with.py

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+# 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()
+