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import os
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
@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 = OpenAI(
temperature=0,
openai_api_key=os.environ["OPENAI_API_KEY"],
model_name="text-davinci-003",
)
st.session_state.conversation = ConversationChain(
llm=llm,
memory=ConversationSummaryMemory(llm=llm),
)
def on_click_callback():
with get_openai_callback() as cb:
human_prompt = st.session_state.human_prompt
llm_response = st.session_state.conversation.run(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")
information_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="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'}">
​{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,
)
information_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,
)
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