Interactive components

import React, { useRef } from "react";import { CopilotKit } from "@copilotkit/react-core";import {  CopilotChat,  useConfigureSuggestions,  useFrontendTool,} from "@copilotkit/react-core/v2";import { z } from "zod";import { TimePickerCard, TimeSlot } from "./time-picker-card";const DEFAULT_SLOTS: TimeSlot[] = [  { label: "Tomorrow 10:00 AM", iso: "2026-04-25T10:00:00-07:00" },  { label: "Tomorrow 2:00 PM", iso: "2026-04-25T14:00:00-07:00" },  { label: "Monday 9:00 AM", iso: "2026-04-28T09:00:00-07:00" },  { label: "Monday 3:30 PM", iso: "2026-04-28T15:30:00-07:00" },];type PickerResult =  | { chosen_time: string; chosen_label: string }  | { cancelled: true };export default function GenUiInterruptDemo() {  return (    <CopilotKit runtimeUrl="/api/copilotkit" agent="gen-ui-interrupt">      <div className="flex justify-center items-center h-screen w-full">        <div className="h-full w-full max-w-4xl">          <Chat />        </div>      </div>    </CopilotKit>  );}function Chat() {  // Pending-resolver ref: set by the async handler, called by the render  // prop when the user clicks a slot or cancels. This is the PydanticAI  // adaptation of the LangGraph `resolve(...)` callback.  const resolverRef = useRef<((result: PickerResult) => void) | null>(null);  useConfigureSuggestions({    suggestions: [      {        title: "Book a call with sales",        message: "Book an intro call with the sales team to discuss pricing.",      },      {        title: "Schedule a 1:1 with Alice",        message: "Schedule a 1:1 with Alice next week to review Q2 goals.",      },    ],    available: "always",  });  useFrontendTool({    name: "schedule_meeting",    description:      "Ask the user to pick a time slot for a meeting via an in-chat " +      "picker. Blocks until the user chooses a slot or cancels.",    parameters: z.object({      topic: z        .string()        .describe("Short human-readable description of the meeting."),      attendee: z        .string()        .optional()        .describe("Who the meeting is with (optional)."),    }),    // Async handler: returns a Promise that resolves only once the user    // acts on the picker. This is the PydanticAI shim for LangGraph's    // `interrupt()`/`resolve()` pair.    handler: async (): Promise<string> => {      const result = await new Promise<PickerResult>((resolve) => {        resolverRef.current = resolve;      });      if ("cancelled" in result && result.cancelled) {        return "User cancelled. Meeting NOT scheduled.";      }      if ("chosen_label" in result) {        return `Meeting scheduled for ${result.chosen_label}.`;      }      return "User did not pick a time. Meeting NOT scheduled.";    },    render: ({ args, status }) => {      if (status === "complete") return null;      const topic =        (args as { topic?: string } | undefined)?.topic ?? "a meeting";      const attendee = (args as { attendee?: string } | undefined)?.attendee;      return (        <TimePickerCard          topic={topic}          attendee={attendee}          slots={DEFAULT_SLOTS}          onSubmit={(result) => {            const fn = resolverRef.current;            resolverRef.current = null;            fn?.(result);          }}        />      );    },  });

from __future__ import annotationsfrom textwrap import dedentfrom pydantic_ai import Agentfrom pydantic_ai.models.openai import OpenAIResponsesModelSYSTEM_PROMPT = dedent(    """    You are a scheduling assistant. Whenever the user asks you to book a call    or schedule a meeting, you MUST call the `schedule_meeting` tool. Pass a    short `topic` describing the purpose of the meeting and, if known, an    `attendee` describing who the meeting is with.    The `schedule_meeting` tool is implemented on the client: it surfaces a    time-picker UI to the user and returns the user's selection. After the    tool returns, briefly confirm whether the meeting was scheduled and at    what time, or note that the user cancelled. Do NOT ask for approval    yourself -- always call the tool and let the picker handle the decision.    Keep responses short and friendly. After you finish executing tools,    always send a brief final assistant message summarizing what happened so    the message persists.    """.strip())agent = Agent(    model=OpenAIResponsesModel("gpt-4o-mini"),    system_prompt=SYSTEM_PROMPT,)