I am trying to add thumbs up/down buttons and user’s feedback into Streamlit based chatbot.
I use st.chat_message to create chatbot with Streamlit. For thumbs up/down buttons and user’s feedback I use streamlit-feedback python package becouse I did not find any other way to include it into Streamlit based chatbot.
My application code looks like:
import streamlit as st
from streamlit_feedback import streamlit_feedback
...
def handle_feedback():
st.write(st.session_state.fb_k)
st.toast("✔️ Feedback received!")
if "df" in st.session_state:
if prompt := st.chat_input(placeholder=""):
...
with st.form('form'):
streamlit_feedback(feedback_type="thumbs",
optional_text_label="Enter your feedback here",
align="flex-start",
key='fb_k')
st.form_submit_button('Save feedback', on_click=handle_feedback)
For some reason streamlit_feedback works only inside st.form. It creates two problems:
- To get it work user needs first click on “SUBMIT” button and only then to “Save feedback” button.
If user click “Save feedback” without using “SUBMIT” button then st.session_state.fb_k will be None.
- Feedback inside
st.formdoes not look very appealing and I am looking to ways to get rid ofst.form.
I am looking for a way to resolve those problems with streamlit_feedback package or without it.
Note that streamlit_feedback package has on_submit parameter where handle_feedback could be included:
streamlit_feedback(feedback_type="faces",
optional_text_label="[Optional] Please provide an explanation",
align="flex-start",
key='fb_k',
on_submit = handle_feedback)
but function:
def handle_feedback():
st.write(st.session_state.fb_k)
st.toast("✔️ Feedback received!")
does not output anything (i do not see printed st.write or st.toast pop-up). So on_submit does not work for some reason.
for reference here is full application code:
from langchain.chat_models import AzureChatOpenAI
from langchain.memory import ConversationBufferWindowMemory # ConversationBufferMemory
from langchain.agents import ConversationalChatAgent, AgentExecutor, AgentType
from langchain.callbacks import StreamlitCallbackHandler
from langchain.memory.chat_message_histories import StreamlitChatMessageHistory
from langchain.agents import Tool
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
import pprint
import streamlit as st
import os
import pandas as pd
from streamlit_feedback import streamlit_feedback
def handle_feedback():
st.write(st.session_state.fb_k)
st.toast("✔️ Feedback received!")
os.environ["OPENAI_API_KEY"] = ...
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_BASE"] = ...
os.environ["OPENAI_API_VERSION"] = "2023-08-01-preview"
@st.cache_data(ttl=72000)
def load_data_(path):
return pd.read_csv(path)
uploaded_file = st.sidebar.file_uploader("Choose a CSV file", type="csv")
if uploaded_file is not None:
# If a file is uploaded, load the uploaded file
st.session_state["df"] = load_data_(uploaded_file)
if "df" in st.session_state:
msgs = StreamlitChatMessageHistory()
memory = ConversationBufferWindowMemory(chat_memory=msgs,
return_messages=True,
k=5,
memory_key="chat_history",
output_key="output")
if len(msgs.messages) == 0 or st.sidebar.button("Reset chat history"):
msgs.clear()
msgs.add_ai_message("How can I help you?")
st.session_state.steps = {}
avatars = {"human": "user", "ai": "assistant"}
for idx, msg in enumerate(msgs.messages):
with st.chat_message(avatars[msg.type]):
# Render intermediate steps if any were saved
for step in st.session_state.steps.get(str(idx), []):
if step[0].tool == "_Exception":
continue
# Insert a status container to display output from long-running tasks.
with st.status(f"**{step[0].tool}**: {step[0].tool_input}", state="complete"):
st.write(step[0].log)
st.write(step[1])
st.write(msg.content)
if prompt := st.chat_input(placeholder=""):
st.chat_message("user").write(prompt)
llm = AzureChatOpenAI(
deployment_name = "gpt-4",
model_name = "gpt-4",
openai_api_key = os.environ["OPENAI_API_KEY"],
openai_api_version = os.environ["OPENAI_API_VERSION"],
openai_api_base = os.environ["OPENAI_API_BASE"],
temperature = 0,
streaming=True
)
prompt_ = PromptTemplate(
input_variables=["query"],
template="{query}"
)
chain_llm = LLMChain(llm=llm, prompt=prompt_)
tool_llm_node = Tool(
name='Large Language Model Node',
func=chain_llm.run,
description='This tool is useful when you need to answer general purpose queries with a large language model.'
)
tools = [tool_llm_node]
chat_agent = ConversationalChatAgent.from_llm_and_tools(llm=llm, tools=tools)
executor = AgentExecutor.from_agent_and_tools(
agent=chat_agent,
tools=tools,
memory=memory,
return_intermediate_steps=True,
handle_parsing_errors=True,
verbose=True,
)
with st.chat_message("assistant"):
st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
response = executor(prompt, callbacks=[st_cb, st.session_state['handler']])
st.write(response["output"])
st.session_state.steps[str(len(msgs.messages) - 1)] = response["intermediate_steps"]
response_str = f'{response}'
pp = pprint.PrettyPrinter(indent=4)
pretty_response = pp.pformat(response_str)
with st.form('form'):
streamlit_feedback(feedback_type="thumbs",
optional_text_label="[Optional] Please provide an explanation",
align="flex-start",
key='fb_k')
st.form_submit_button('Save feedback', on_click=handle_feedback)
