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import streamlit as st
from app import rag_query, process_feedback
st.title("RAG Chatbot")
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat messages from history on app rerun
for i, message in enumerate(st.session_state.messages):
with st.chat_message(message["role"]):
st.markdown(message["content"])
if message["role"] == "assistant":
col1, col2 = st.columns([1,15])
with col1:
if st.button("👍", key=f"thumbs_up_{i}"):
process_feedback(st.session_state.messages[i-1]["content"], message["content"], True)
with col2:
if st.button("👎", key=f"thumbs_down_{i}"):
process_feedback(st.session_state.messages[i-1]["content"], message["content"], False)
# st.session_state.messages.pop() # Remove the last assistant message
#st.rerun() # Rerun the app to regenerate the response
# React to user input
if prompt := st.chat_input("What is your question?"):
# Display user message in chat message container
st.chat_message("user").markdown(prompt)
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
response = rag_query(prompt)
# Display assistant response in chat message container
with st.chat_message("assistant"):
st.markdown(response)
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": response})
# Rerun the app to display the feedback buttons
st.experimental_rerun()
# Sidebar for additional controls
with st.sidebar:
st.header("Options")
if st.button("Clear Chat History"):
st.session_state.messages = []
st.experimental_rerun() |