Create app.py

app.py

@@ -0,0 +1,273 @@
+#This version includes the memory and custom prompt, representing the final version
+
+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 import HuggingFacePipeline
+from langchain.chains import RetrievalQA
+from langchain.prompts import PromptTemplate
+from langchain.memory import ConversationBufferWindowMemory
+
+
+from langchain.chains import LLMChain
+from langchain.chains import ConversationalRetrievalChain
+from langchain.chains.question_answering import load_qa_chain
+from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT
+
+
+
+prompt_template = """
+You are the chatbot and the advanced legal assitant that can give answers to all the legal questions a common citizen would have . Your job is to give answers when questions about General legal information, Family law, Employment law, Consumer rights, Housing and tenancy, Personal injury, Wills and estates, Criminal law are asked.
+Your job is to answer questions only and only related to Legal aspect. Anything unrelated should be responded with the fact that your main job is solely to provide assistance regarding Legality.
+MUST only use the following pieces of context to answer the question at the end. If the answers are not in the context or you are not sure of the answer, just say that you don't know, don't try to make up an answer.
+{context}
+Question: {question}
+When encountering abusive, offensive, or harmful language, such as fuck, bitch,etc,  just politely ask the users to maintain appropriate behaviours.
+Always make sure to elaborate your response and use vibrant, positive tone to represent good branding of the school.
+Never answer with any unfinished response
+Answer:
+"""
+PROMPT = PromptTemplate(
+    template=prompt_template, input_variables=["context", "question"]
+)
+chain_type_kwargs = {"prompt": PROMPT}
+
+
+st.set_page_config(
+    page_title = '👨‍⚖️Seon\'s Legal QA For Dummies ⚖️',
+    page_icon = '🕵')
+
+
+
+
+
+
+@st.cache_resource
+def load_llm_model():
+    # llm = HuggingFacePipeline.from_model_id(model_id= 'PyaeSoneK/LlamaV2LegalFineTuned', 
+    #                                         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= 'PyaeSoneK/LlamaV2LegalFineTuned', 
+                                        task= 'text2text-generation',
+                                        
+                                        model_kwargs={ "max_length": 256, "temperature": 0,
+                                                      "torch_dtype":torch.float32,
+                                                    "repetition_penalty": 1.3})
+    return llm
+
+
+@st.cache_resource
+def load_conversational_qa_memory_retriever():
+
+    question_generator = LLMChain(llm=llm_model, prompt=CONDENSE_QUESTION_PROMPT)
+    doc_chain = load_qa_chain(llm_model, chain_type="stuff", prompt = PROMPT)
+    memory = ConversationBufferWindowMemory(k = 3,  memory_key="chat_history", return_messages=True,  output_key='answer')
+    
+    
+    
+    conversational_qa_memory_retriever = ConversationalRetrievalChain(
+        retriever=vector_database.as_retriever(),
+        question_generator=question_generator,
+        combine_docs_chain=doc_chain,
+        return_source_documents=True,
+        memory = memory,
+        get_chat_history=lambda h :h)
+    return conversational_qa_memory_retriever, question_generator
+
+def load_retriever(llm, db):
+    qa_retriever = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff",
+                            retriever=db.as_retriever(),
+                            chain_type_kwargs= chain_type_kwargs)
+
+    return qa_retriever
+
+def retrieve_document(query_input):
+    related_doc = vector_database.similarity_search(query_input)
+    return related_doc
+
+
+
+def retrieve_answer():
+    prompt_answer=  st.session_state.my_text_input
+    answer = qa_retriever.run(prompt_answer)
+    log = {"timestamp": datetime.datetime.now(),
+        "question":st.session_state.my_text_input,
+        "generated_answer": answer[6:],
+        "rating":0 }
+
+    st.session_state.history.append(log)
+    update_worksheet_qa()
+    st.session_state.chat_history.append({"message": st.session_state.my_text_input, "is_user": True})
+    st.session_state.chat_history.append({"message": answer[6:] , "is_user": False})
+
+    st.session_state.my_text_input = ""
+
+    return answer[6:] #this positional slicing helps remove "<pad> " at the beginning
+
+
+def new_retrieve_answer():
+    prompt_answer=  st.session_state.my_text_input + ". Try to be elaborate and informative in your answer."
+    answer = conversational_qa_memory_retriever({"question": prompt_answer, })
+    log = {"timestamp": datetime.datetime.now(),
+        "question":st.session_state.my_text_input,
+        "generated_answer": answer['answer'][6:],
+        "rating":0 }
+
+    print(f"condensed quesion : {question_generator.run({'chat_history': answer['chat_history'], 'question' : prompt_answer})}")
+
+    print(answer["chat_history"])
+    st.session_state.history.append(log)
+    update_worksheet_qa()
+    st.session_state.chat_history.append({"message": st.session_state.my_text_input, "is_user": True})
+    st.session_state.chat_history.append({"message": answer['answer'][6:] , "is_user": False})
+
+    st.session_state.my_text_input = ""
+
+    return answer['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 = []
+    conversational_qa_memory_retriever.memory.chat_memory.clear() #add this to remove
+
+#--------------
+
+
+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)
+conversational_qa_memory_retriever, question_generator = load_conversational_qa_memory_retriever()
+print("all load done")
+
+
+# Try adding this to set to clear the memory in each session
+if st.session_state.chat_history == []:
+    conversational_qa_memory_retriever.memory.chat_memory.clear()
+#Addional things for Conversation flows
+
+
+
+
+
+
+st.write("# 🦜Legal QA For Dummies 🔗 ")
+st.markdown("""
+         ####This Legal QA is designed for normal people trying to get the legal answers orbiting around in their life.  
+        The goal of this chatbot is to provide answers and advice quick access information about Legality : Law and Regulations and What Ca
+          """)
+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_input(label= 'Boraden Your General Legal Knowledge Here!' , key = 'my_text_input', on_change= new_retrieve_answer )
+# 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})
+
+#     print(st.session_state.chat_history)
+
+
+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()