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Simple_Chatbot.py
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Simple_Chatbot.py
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import streamlit as st
from helpers.llm_helper import chat, stream_parser
from config import Config
from dotenv import load_dotenv
load_dotenv()
st.set_page_config(
page_title="Streamlit OpenAI Chatbot",
initial_sidebar_state="expanded"
)
st.title("Streamlit OpenAI Chatbot")
# sets up sidebar nav widgets
with st.sidebar:
st.markdown("# Chat Options")
# widget - https://docs.streamlit.io/library/api-reference/widgets/st.selectbox
# models - https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo
model = st.selectbox('What model would you like to use?',('gpt-3.5-turbo', 'gpt-4'))
# https://docs.streamlit.io/library/api-reference/widgets/st.number_input
temperature = st.number_input('Temperature', value=0.7, min_value=0.1, max_value=1.0, step=0.1,
help="The temperature setting to be used when generating output from the model.")
max_token_length = st.number_input('Max Token Length', value=1000, min_value=200, max_value=1000, step=100,
help="Maximum number of tokens to be used when generating output.")
# checks for existing messages in session state
# https://docs.streamlit.io/library/api-reference/session-state
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat messages from session state
# https://docs.streamlit.io/library/api-reference/chat/st.chat_message
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
if user_prompt := st.chat_input("What questions do you have about the document?"):
# Display user prompt in chat message container
with st.chat_message("user"):
st.markdown(user_prompt)
# adds user's prompt to session state
st.session_state.messages.append({"role": "user", "content": user_prompt})
with st.spinner('Generating response...'):
# retrieves response from OpenAI
llm_response = chat(user_prompt, model=model, max_tokens=max_token_length,
temp=temperature)
# streams the response back to the screen
stream_output = st.write_stream(stream_parser(llm_response))
# appends response to the message list
st.session_state.messages.append({"role": "assistant", "content": stream_output})
last_response = st.session_state.messages[len(st.session_state.messages)-1]['content']
# Display assistant response in chat message container
if str(last_response) != str(stream_output):
with st.chat_message("assistant"):
st.markdown(stream_output)