app.py

from agent import SGDataAnalystAgent
import streamlit as st

# Thanks to https://blog.streamlit.io/how-to-build-an-llm-powered-chatbot-with-streamlit/

# App title
st.set_page_config(page_title="👩‍🔬📊 SG Data Analyst")

# Hugging Face Credentials
with st.sidebar:
    st.title('👩‍🔬📊 SG Data Analyst')
    
# Store LLM generated responses
if "messages" not in st.session_state.keys():
    st.session_state.messages = [{"role": "assistant", "content": "How may I help you?"}]

# Display chat messages
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.write(message["content"])

# Function for generating LLM response
def generate_response(prompt_input):
    # Create ChatBot                        
    return SGDataAnalystAgent.run(prompt_input)

# User-provided prompt
if prompt := st.chat_input():
    st.session_state.messages.append({"role": "user", "content": prompt})
    with st.chat_message("user"):
        st.write(prompt)

# Generate a new response if last message is not from assistant
if st.session_state.messages[-1]["role"] != "assistant":
    with st.chat_message("assistant"):
        with st.spinner("Thinking..."):
            response = generate_response(prompt) 
            st.write(response) 
    message = {"role": "assistant", "content": response}
    st.session_state.messages.append(message)