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| import gradio as gr | |
| import openai, os, time | |
| from agents import create_triage_agent, create_sales_agent, create_issues_repairs_agent | |
| from openai import OpenAI | |
| from utils import show_json | |
| #def create_assistant(client): | |
| # assistant = client.beta.assistants.create( | |
| # name="Math Tutor", | |
| # instructions="You are a personal math tutor. Answer questions briefly, in a sentence or less.", | |
| # model="gpt-4-1106-preview", | |
| # tools=[{"type": "code_interpreter"}], | |
| # ) | |
| # show_json("assistant", assistant) | |
| # return assistant | |
| 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 | |
| _client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY")) | |
| #_assistant, _thread = None, None | |
| ### | |
| 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, _thread | |
| #if _assistant == None: | |
| # _assistant = create_assistant(_client) | |
| #if _thread == None: | |
| # _thread = create_thread(_client) | |
| 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() |