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import streamlit as st
import os
from openai import AzureOpenAI
from functions import call_function
st.title("Support Chat UI")
# when will my order be delivered?, [email protected] W123123
functions = [
{
"name": "order_tracking_status",
"description": "Retrieves the status of an order based on **both** the email address and order number.",
"parameters": {
"type": "object",
"properties": {
"email_address": {
"type": "string",
"description": "The email address associated with the order"
},
"order_number": {
"type": "integer",
"description": "The order number."
},
},
"required": ["email_address", "order_number"]
}
},
{
"name": "refer_to_human_agent",
"description": "Use this to refer the customer's question to a human agent. You should only call this "
"function if you don't know how to answer the inquiry?.",
"parameters": {
"type": "object",
"properties": {
"conversation_summary": {
"type": "string",
"description": "A short summary of the current conversation so the agent can quickly get up to "
"speed. Make sure you include all relevant details. "
},
},
"required": ["conversation_summary"]
}
}
]
client = AzureOpenAI(
api_key=os.environ['OPENAI_API_KEY'],
api_version="2023-07-01-preview",
azure_endpoint=os.environ['AZURE_ENDPOINT'],
)
if "openai_model" not in st.session_state:
st.session_state["openai_model"] = "gpt-35-turbo"
if "messages" not in st.session_state:
st.session_state.messages = [{"role": "system", "content": "You are a helpful customer support agent for Lowes."
"Be as helpful as possible and call "
"functions when necessary."},]
for message in st.session_state.messages:
if message["role"] == "assistant" or message["role"] == "user":
with st.chat_message(message["role"]):
st.markdown(message["content"])
if prompt := st.chat_input("How can we help you today?"):
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.markdown(prompt)
with st.chat_message("assistant", avatar="🏠"): # avatar=st.image('Home-Depot-Logo.png', width=50)):
message_placeholder = st.empty()
full_message = ""
func_call = {
"name": None,
"arguments": "",
}
called_function = True
while called_function:
called_function = False
for response in client.chat.completions.create(
model=st.session_state["openai_model"],
messages=[
{"role": m["role"], "content": m["content"], "name": m["name"]} if "name" in m else
{"role": m["role"], "content": m["content"]}
for m in st.session_state.messages
],
functions=functions,
function_call="auto",
stream=True,
):
if len(response.choices) > 0:
delta = response.choices[0].delta
full_message += (delta.content or "")
if delta.function_call is not None:
if delta.function_call.name is not None:
func_call["name"] = delta.function_call.name
if delta.function_call.arguments is not None:
func_call["arguments"] += delta.function_call.arguments
message_placeholder.markdown(full_message + "▌")
if func_call["name"] is not None:
print(f"Function generation requested, calling function")
function_response = call_function(st.session_state.messages, func_call)
print("function response")
print(function_response)
st.session_state.messages.append(function_response)
called_function = True
func_call = {
"name": None,
"arguments": "",
}
message_placeholder.markdown(full_message)
st.session_state.messages.append({"role": "assistant", "content": full_message})
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