Spaces:
Runtime error
Runtime error
| 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}) | |