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import streamlit as st
import os
from openai import AzureOpenAI
from functions import call_function
import firebase_admin
from firebase_admin import credentials, firestore
st.title("SupportFlow Demo")
# when will my order be delivered?, colin.flueck@gmail.com W123123
functions = [
{
"name": "lookup_order_status",
"description": "Retrieves the status, location, etc. 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": "lookup_product",
# "description": "Returns a detailed list of products based on a product query.",
# "parameters": {
# "type": "object",
# "properties": {
# "query": {
# "type": "string",
# "description": "Product query to search for like drills, lights, or hammers"
# },
# },
# "required": ["query"]
# }
# },
# {
# "name": "get_product_listing",
# "description": "Returns information about the product based on the SKU.",
# "parameters": {
# "type": "object",
# "properties": {
# "sku": {
# "type": "integer",
# "description": "Product sku to search for like 123123"
# },
# },
# "required": ["sku"]
# }
# },
{
"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 there is no way for you to answer their question.",
"parameters": {
"type": "object",
"properties": {
"conversation_summary": {
"type": "string",
"description": "A short summary of the current conversation so the human agent can quickly get up "
"to speed. Make sure you include all relevant details."
},
},
"required": ["conversation_summary"]
}
}
]
cred = credentials.Certificate("supportflow-4851d-firebase-adminsdk-cdrzu-bf620a4b52.json")
try:
app = firebase_admin.initialize_app(cred)
except Exception:
pass
db = firestore.client()
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 The Home "
"Depot. Your goal is to answer as many questions as "
"possible without escalating to a human agent. "
"However, if necessary, you can refer the customer to "
"a human agent if you do not know the answer to their "
"question. For example, you can help users track their orders, but you **cannot** help with returns."},]
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": "",
}
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 and func_call["arguments"] != "":
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)
if function_response["name"] is not None and function_response["name"] == "refer_to_human_agent":
print("connect to human agent")
print(function_response["name"])
st.info('You will be connected with an agent shortly', icon="ℹ️")
# Get the document to update
doc_ref = db.collection('handoffs').document('conversation')
# Update the document
doc_ref.update({'summary': str(function_response["content"]), 'message_history': st.session_state.messages})
else:
message_placeholder = st.empty()
full_message = ""
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 + "")
message_placeholder.markdown(full_message)
st.session_state.messages.append({"role": "assistant", "content": full_message})
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