screens/chat_bot_2.py

import streamlit as st
#Import library
import yaml
#load config.yml and parse into variables 
with open("config.yml", "r") as ymlfile:
    cfg = yaml.safe_load(ymlfile)
_BARD_API_KEY = cfg["API_KEY"]["Bard"]
main_path = cfg["LOCAL_PATH"]["main_path"]
chat_context_length = cfg["CHAT"]["chat_context_length"]
model_name = cfg["EMBEDDINGS"]["HuggingFaceEmbeddings"]["model_name"]
model_kwargs = cfg["EMBEDDINGS"]["HuggingFaceEmbeddings"]["model_kwargs"]
chunk_size = cfg["CHUNK"]["chunk_size"]
chunk_overlap = cfg["CHUNK"]["chunk_overlap"]

import os
from langchain.vectorstores import Chroma
import streamlit.components.v1 as components
import streamlit as st
import sys
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.chains import ConversationalRetrievalChain
from langchain.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate
# Bard
from bardapi import Bard
from typing import Any, List, Mapping, Optional
from getpass import getpass
import os
from langchain.llms.base import LLM
from langchain.callbacks.manager import CallbackManagerForLLMRun

from streamlit_feedback import streamlit_feedback


#define Bard
class BardLLM(LLM):

    @property
    def _llm_type(self) -> str:
        return "custom"

    def _call(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
    ) -> str:
        response = Bard(token=_BARD_API_KEY).get_answer(prompt)['content']
        return response

    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        """Get the identifying parameters."""
        return {}



def load_embeddings(): 
    embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)
    chroma_index = Chroma(persist_directory="./chroma_index_1", embedding_function=embeddings)
    print("Successfully loading embeddings and indexing")
    return chroma_index


def ask_with_memory(vector_store, question, chat_history=[], document_description=""):

    llm=BardLLM()
    retriever = vector_store.as_retriever( # now the vs can return documents
    search_type='similarity', search_kwargs={'k': 3})
 
    general_system_template = f"""
    Use the following pieces of context to answer the question at the end. 
    If you don't know the answer, just say that you don't know, don't try to 
    make up an answer.
    Imagine you're talking to a friend and use natural language and phrasing.
    You can only use Vietnamese do not use other languages.
    Suggest using out searching function for more information.
    ----
    CONTEXT: {{context}}
    ----
    """
    general_user_template = """Here is the next question, remember to only answer if you can from the provided context. 
    If the question is not relevant to real estate , just answer that you do not know, do not create your own answer.
    Do not recommend or propose any infomation of the properties. 
    Be sure to respond in a complete sentence, being comprehensive, including all information in the provided context.
    Imagine you're talking to a friend and use natural language and phrasing.
    Only respond in Vietnamese.
     QUESTION:```{question}```"""

    messages = [
                SystemMessagePromptTemplate.from_template(general_system_template),
                HumanMessagePromptTemplate.from_template(general_user_template)
    ]
    qa_prompt = ChatPromptTemplate.from_messages( messages )


    crc = ConversationalRetrievalChain.from_llm(llm, retriever, combine_docs_chain_kwargs={'prompt': qa_prompt})
    result = crc({'question': question, 'chat_history': chat_history})
    return result


def clear_history():
    if "history" in st.session_state:
        st.session_state.history = []
        st.session_state.messages = []

# Define a function for submitting feedback
def _submit_feedback(user_response, emoji=None):
    st.toast(f"Feedback submitted: {user_response}", icon=emoji)
    return user_response.update({"some metadata": 123})


def format_chat_history(chat_history):
    formatted_history = ""
    for entry in chat_history:
        question, answer = entry
        # Added an extra '\n' for the blank line
        formatted_history += f"Question: {question}\nAnswer: {answer}\n\n"
    return formatted_history

def run_chatbot_2():
    with st.sidebar.title("Sidebar"):
        if st.button("Clear History"):
            clear_history()

    st.title("🤖 Real Estate chatbot")

    # Initialize the chatbot and load embeddings
    if "messages" not in st.session_state:
        with st.spinner("Initializing, please wait a moment!!!"):
            st.session_state.vector_store = load_embeddings()
            st.success("Finish!!!")
        st.session_state["messages"] = [{"role": "assistant", "content": "Tôi có thể giúp gì được cho bạn?"}]

    messages = st.session_state.messages
    feedback_kwargs = {
        "feedback_type": "thumbs",
        "optional_text_label": "Please provide extra information",
        "on_submit": _submit_feedback,
    }

    for n, msg in enumerate(messages):
        st.chat_message(msg["role"]).write(msg["content"])

        if msg["role"] == "assistant" and n > 1:
            feedback_key = f"feedback_{int(n/2)}"

            if feedback_key not in st.session_state:
                st.session_state[feedback_key] = None

            streamlit_feedback(
                **feedback_kwargs,
                key=feedback_key,
            )

    chat_history_placeholder = st.empty()
    if "history" not in st.session_state:
        st.session_state.history = []

    if prompt := st.chat_input():
        if "vector_store" in st.session_state:
            vector_store = st.session_state["vector_store"]
            
            q = prompt

            st.session_state.messages.append({"role": "user", "content": prompt})
            st.chat_message("user").write(prompt)

            response = ask_with_memory(vector_store, q, st.session_state.history)

            if len(st.session_state.history) >= chat_context_length:
                st.session_state.history = st.session_state.history[1:]

            st.session_state.history.append((q, response['answer']))

            chat_history_str = format_chat_history(st.session_state.history)

            msg = {"role": "assistant", "content": response['answer']}
            st.session_state.messages.append(msg)
            st.chat_message("assistant").write(msg["content"])

            # Display the feedback component after the chatbot responds
            feedback_key = f"feedback_{len(st.session_state.messages) - 1}"
            streamlit_feedback(
                **feedback_kwargs,
                key=feedback_key,
            )