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##############################################################################
# Main script that builds the UI & connects the logic for an LLM-driven
# query frontend to a "Global Commerce" demo app.
#
# @philmui
# Mon May 1 18:34:45 PDT 2023
##############################################################################
import streamlit as st
from pprint import pprint
from agents import agentController , salesAgent, chinookAgent, chatAgent
##############################################################################
st.set_page_config(page_title="Global",
page_icon=":cart:",
layout="wide")
st.header("📦 Global 🛍️")
col1, col2 = st.columns([1,1])
with col1:
option_llm = st.selectbox(
"Model",
('text-davinci-003',
'text-babbage-001',
'text-curie-001',
'text-ada-001',
'gpt-4',
'gpt-3.5-turbo',
'google/flan-t5-xl',
'databricks/dolly-v2-3b',
'bigscience/bloom-1b7')
)
with col2:
option_mode = st.selectbox(
"LLM mode",
("Instruct (all)",
"Chat (high temperature)",
"Wolfram-Alpha",
"Internal-Sales",
"Internal-Merchant"
)
)
def get_question():
input_text = st.text_area(label="Your question ...",
placeholder="Ask me anything ...",
key="question_text", label_visibility="collapsed")
return input_text
question_text = get_question()
if question_text and len(question_text) > 1:
output=""
if option_mode == "Internal-Sales":
# not going through the agentController
output = salesAgent(question_text)
elif option_mode == "Internal-Merchant":
# not going through the agentController
output = chinookAgent(question_text, option_llm)
elif option_mode.startswith("Chat"):
# not going through the agentController
response = chatAgent(question_text)
if response and response.content:
output = response.content
else:
output = response
else: # DEFAULT DEMO: through agentController
output = agentController(question_text, option_llm)
height = min(2*len(output), 280)
st.text_area(label="In response ...",
value=output, height=height)
# st.code(pprint(output), language="markdown")
##############################################################################
st.markdown(
"""
<style>
textarea[aria-label^="ex"] {
font-size: 0.8em !important;
font-family: Arial, sans-serif !important;
color: gray !important;
}
</style>
""",
unsafe_allow_html=True,
)
st.markdown("#### 3 types of reasoning:")
col1, col2, col3 = st.columns([1,1,1])
with col1:
st.markdown("__Common sense reasoning__")
st.text_area(label="ex1", label_visibility="collapsed", height=120,
value="🔹 Why is the sky blue?\n" +
"🔹 How to avoid touching a hot stove?\n" +
"🔹 Please give tips to win a 3200m track race?\n" +
"🔹 Please advise on how best to prepare for retirement?"
)
with col2:
st.markdown("__Local ('secure') reasoning__")
st.text_area(label="ex2", label_visibility="collapsed", height=120,
value="🔹 For my company, what is the total sales " +
"broken down by month, labeled by months?\n" +
"🔹 How many total artists are there in each "+
"genres in our digital media database?\n" +
"🔹 How to best govern a city? (The Prince)\n" +
"🔹 How to win a war? (Art of War)",
)
with col3:
st.markdown("__Enhanced reasoning__ [🎵](https://www.youtube.com/watch?v=hTTUaImgCyU&t=62s)")
st.text_area(label="ex3", label_visibility="collapsed", height=120,
value="🔹 Who is the president of South Korea? " +
"What is his favorite song? How old is he? " +
"What is the smallest prime greater than his age?\n" +
"🔹 What is the derivative of f(x)=3*log(x)*sin(x)?")
st.image(image="images/plugins.png", width=700, caption="salesforce.com")
st.image(image="images/chinook.png", width=420, caption="Digital Media Schema")
##############################################################################
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