app.py

import gradio as gr
import openai, os, time

from openai import OpenAI
from utils import function_to_schema, show_json

# Tools

sales_agent, issues_repairs_agent, triage_agent = None, None, None

def transfer_to_sales_agent():
    """Use for anything sales or buying related."""
    set_current_agent(sales_agent)

def transfer_to_issues_repairs_agent():
    """Use for issues, repairs, or refunds."""
    set_current_agent(issues_repairs_agent)

def transfer_to_triage_agent():
    """Call this if the user brings up a topic outside of your purview,
    including escalating to human."""
    set_current_agent(triage_agent)

def escalate_to_human(summary):
    """Only call this if explicitly asked to."""
    print("Escalating to human agent...")
    print("\n=== Escalation Report ===")
    print(f"Summary: {summary}")
    print("=========================\n")
    exit()

def execute_order(product, price: int):
    """Price should be in USD."""
    print("\n\n=== Order Summary ===")
    print(f"Product: {product}")
    print(f"Price: ${price}")
    print("=================\n")
    confirm = input("Confirm order? y/n: ").strip().lower()
    if confirm == "y":
        print("Order execution successful!")
        return "Success"
    else:
        print(color("Order cancelled!", "red"))
        return "User cancelled order."
    
def look_up_item(search_query):
    """Use to find item ID.
    Search query can be a description or keywords."""
    item_id = "item_132612938"
    print("Found item:", item_id)
    return item_id

def execute_refund(item_id, reason="not provided"):
    print("\n\n=== Refund Summary ===")
    print(f"Item ID: {item_id}")
    print(f"Reason: {reason}")
    print("=================\n")
    print("Refund execution successful!")
    return "Success"

# Agents

MODEL = "gpt-4o-mini"

def create_triage_agent(client):
    return client.beta.assistants.create(
        name="Triage Agent",
        instructions=(
            "You are a customer service bot for ACME Inc. "
            "Introduce yourself. Always be very brief. "
            "Gather information to direct the customer to the right department. "
            "But make your questions subtle and natural."
        ),
        model=MODEL,
        tools=[{"type": "function", "function": function_to_schema(transfer_to_sales_agent)},
               {"type": "function", "function": function_to_schema(transfer_to_issues_repairs_agent)},
               {"type": "function", "function": function_to_schema(escalate_to_human)}],
    )

def create_sales_agent(client):
    return client.beta.assistants.create(
        name="Sales Agent",
        instructions=(
            "You are a sales agent for ACME Inc."
            "Always answer in a sentence or less."
            "Follow the following routine with the user:"
            "1. Ask them about any problems in their life related to catching roadrunners.\n"
            "2. Casually mention one of ACME's crazy made-up products can help.\n"
            " - Don't mention price.\n"
            "3. Once the user is bought in, drop a ridiculous price.\n"
            "4. Only after everything, and if the user says yes, "
            "tell them a crazy caveat and execute their order.\n"
            ""
        ),
        model=MODEL,
        tools=[{"type": "function", "function": function_to_schema(execute_order)},
               {"type": "function", "function": function_to_schema(transfer_to_triage_agent)}],
    )
    
def create_issues_repairs_agent(client):
    return client.beta.assistants.create(
        name="Issues and Repairs Agent",
        instructions=(
            "You are a customer support agent for ACME Inc."
            "Always answer in a sentence or less."
            "Follow the following routine with the user:"
            "1. First, ask probing questions and understand the user's problem deeper.\n"
            " - unless the user has already provided a reason.\n"
            "2. Propose a fix (make one up).\n"
            "3. ONLY if not satesfied, offer a refund.\n"
            "4. If accepted, search for the ID and then execute refund."
            ""
        ),
        model=MODEL,
        tools=[{"type": "function", "function": function_to_schema(look_up_item)},
               {"type": "function", "function": function_to_schema(execute_refund)},
               {"type": "function", "function": function_to_schema(transfer_to_triage_agent)}],
    )

#

def create_thread(client):
    thread = client.beta.threads.create()
    #show_json("thread", thread)
    return thread

def create_message(client, thread, msg):
    message = client.beta.threads.messages.create(
        role="user",
        thread_id=thread.id,
        content=msg,
    )
    #show_json("message", message)
    return message

def create_run(client, assistant, thread):
    run = client.beta.threads.runs.create(
        assistant_id=assistant.id,
        thread_id=thread.id,
    )
    #show_json("run", run)
    return run

def wait_on_run(client, thread, run):
    while run.status == "queued" or run.status == "in_progress":
        run = client.beta.threads.runs.retrieve(
            thread_id=thread.id,
            run_id=run.id,
        )
        time.sleep(0.25)
    #show_json("run", run)
    return run

def list_run_steps(client, thread, run):
    run_steps = client.beta.threads.runs.steps.list(
        thread_id=thread.id,
        run_id=run.id,
        order="asc",
    )
    for step in run_steps.data:
        step_details = step.step_details
        show_json("step_details", step_details)
    return run_steps
    
def list_messages(client, thread):
    messages = client.beta.threads.messages.list(
        thread_id=thread.id
    )
    #show_json("messages", messages)
    return messages
    
def extract_content_values(data):
    content_values = []
    for item in data.data:
        for content in item.content:
            if content.type == 'text':
                content_values.append(content.text.value)
    return content_values

#

current_agent, current_thread = None, None

def set_current_agent(agent):
    current_agent = agent

def set_current_thread(thread):
    current_thread = thread

def get_current_agent():
    return current_agent

def get_current_thread():
    return current_thread

#

_client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))

triage_agent = create_triage_agent(_client)
sales_agent = create_sales_agent(_client)
issues_repairs_agent = create_issues_repairs_agent(_client)

set_current_agent(triage_agent)

triage_thread = create_thread(_client)
sales_thread = create_thread(_client)
issues_repairs_thread = create_thread(_client)

set_current_thread(triage_thread)

def chat(message, history, openai_api_key):
    global _client

    _assistant = get_current_agent
    _thread = get_current_thread
            
    create_message(_client, _thread, message)

    # async
    run = create_run(_client, _assistant, _thread)
    run = wait_on_run(_client, _thread, run)

    list_run_steps(_client, _thread, run)
    
    messages = list_messages(_client, _thread)

    return extract_content_values(messages)[0]
        
gr.ChatInterface(
    chat,
    chatbot=gr.Chatbot(height=300),
    textbox=gr.Textbox(placeholder="Question", container=False, scale=7),
    title="Multi-Agent Orchestration",
    description="Demo using hand-off pattern: triage agent, sales agent, and issues & repairs agent",
    retry_btn=None,
    undo_btn=None,
    clear_btn="Clear",
    #examples=[["Generate the first 10 Fibbonaci numbers with code.", "sk-<BringYourOwn>"]],
    #cache_examples=False,
    additional_inputs=[
        gr.Textbox("sk-", label="OpenAI API Key", type = "password"),
    ],
).launch()