chatbot6.py

from dataclasses import dataclass
from typing import Literal
import streamlit as st

from langchain import OpenAI
from langchain.callbacks import get_openai_callback
from langchain.chains import ConversationChain
from langchain.chains.conversation.memory import ConversationSummaryMemory
import streamlit.components.v1 as components

import openai
from dotenv import load_dotenv
import os
from langchain.chat_models import ChatOpenAI
from langchain.schema import AIMessage, HumanMessage, SystemMessage
from langchain import OpenAI,ConversationChain,LLMChain, PromptTemplate
from langchain.memory import ConversationBufferMemory
from langchain.prompts.chat import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder

load_dotenv()
openai_api_key = os.getenv("OPENAI_API_KEY")

prompt = ChatPromptTemplate.from_messages([
    SystemMessagePromptTemplate.from_template(
        "The following is an informative conversation between a human and an AI financial adviser. The financial adviser will attempt to answer any question asked and will probe for the human's risk appetite by asking questions of its own. If the human's risk appetite os low it will offer conservative financial advice, if the risk appetite of the human is higher it will offer more aggressive advice "
    ),
    MessagesPlaceholder(variable_name="history"),
    HumanMessagePromptTemplate.from_template("{input}")
])

@dataclass
class Message:
    """Class for keeping track of a chat message."""
    origin: Literal["human", "ai"]
    message: str


def load_css():
    with open("static/styles.css", "r") as f:
        css = f"<style>{f.read()}</style>"
        st.markdown(css, unsafe_allow_html=True)


def initialize_session_state():
    if "history" not in st.session_state:
        st.session_state.history = []
    if "token_count" not in st.session_state:
        st.session_state.token_count = 0
    if "conversation" not in st.session_state:
        llm = ChatOpenAI(
            temperature=0,
            openai_api_key=openai_api_key,
            # model_name="text-davinci-003"
        )
        st.session_state.conversation = ConversationChain(
            llm=llm,
            memory=ConversationBufferMemory(return_messages=True),
            prompt=prompt
        )
    print(prompt)


def on_click_callback():
    with get_openai_callback() as cb:
        human_prompt = st.session_state.human_prompt
        print(human_prompt)
        history = st.session_state.history
        print(human_prompt)
        history = st.session_state.history
        llm_response = st.session_state.conversation.predict(
            input=human_prompt
        )
        st.session_state.history.append(
            Message("human", human_prompt)
        )
        st.session_state.history.append(
            Message("ai", llm_response)
        )
        st.session_state.token_count += cb.total_tokens


load_css()
initialize_session_state()

st.title("Hello Custom CSS Chatbot 🤖")

chat_placeholder = st.container()
prompt_placeholder = st.form("chat-form")
credit_card_placeholder = st.empty()

with chat_placeholder:
    for chat in st.session_state.history:
        div = f"""
<div class="chat-row 
    {'' if chat.origin == 'ai' else 'row-reverse'}">
    <img class="chat-icon" src="app/static/{
        'ai_icon.png' if chat.origin == 'ai'
        else 'user_icon.png'}"
         width=32 height=32>
    <div class="chat-bubble
    {'ai-bubble' if chat.origin == 'ai' else 'human-bubble'}">
        &#8203;{chat.message}
    </div>
</div>
        """
        st.markdown(div, unsafe_allow_html=True)

    for _ in range(3):
        st.markdown("")

with prompt_placeholder:
    st.markdown("**Chat**")
    cols = st.columns((6, 1))
    cols[0].text_input(
        "Chat",
        value="Hello bot",
        label_visibility="collapsed",
        key="human_prompt",
    )
    cols[1].form_submit_button(
        "Submit",
        type="primary",
        on_click=on_click_callback,
    )

credit_card_placeholder.caption(f"""
Used {st.session_state.token_count} tokens \n
Debug Langchain conversation: 
{st.session_state.conversation.memory.buffer}
""")

components.html("""
<script>
const streamlitDoc = window.parent.document;

const buttons = Array.from(
    streamlitDoc.querySelectorAll('.stButton > button')
);
const submitButton = buttons.find(
    el => el.innerText === 'Submit'
);

streamlitDoc.addEventListener('keydown', function(e) {
    switch (e.key) {
        case 'Enter':
            submitButton.click();
            break;
    }
});
</script>
""",
                height=0,
                width=0,
                )