# Copyright (c) Microsoft. All rights reserved.

import asyncio
import logging
from typing import cast

from agent_framework import (
    ChatAgent,
    HostedCodeInterpreterTool,
    MagenticAgentDeltaEvent,
    MagenticAgentMessageEvent,
    MagenticBuilder,
    MagenticFinalResultEvent,
    MagenticOrchestratorMessageEvent,
    MagenticPlanReviewDecision,
    MagenticPlanReviewReply,
    MagenticPlanReviewRequest,
    RequestInfoEvent,
    WorkflowOutputEvent,
)
from agent_framework.openai import OpenAIChatClient, OpenAIResponsesClient

logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)

"""
Sample: Magentic Orchestration + Human Plan Review

What it does:
- Builds a Magentic workflow with two agents and enables human plan review.
  A human approves or edits the plan via `RequestInfoEvent` before execution.

- researcher: ChatAgent backed by OpenAIChatClient (web/search-capable model)
- coder: ChatAgent backed by OpenAIAssistantsClient with the Hosted Code Interpreter tool

Key behaviors demonstrated:
- with_plan_review(): requests a PlanReviewRequest before coordination begins
- Event loop that waits for RequestInfoEvent[PlanReviewRequest], prints the plan, then
    replies with PlanReviewReply (here we auto-approve, but you can edit/collect input)
- Callbacks: on_agent_stream (incremental chunks), on_agent_response (final messages),
    on_result (final answer), and on_exception
- Workflow completion when idle

Prerequisites:
- OpenAI credentials configured for `OpenAIChatClient` and `OpenAIResponsesClient`.
"""


async def main() -> None:
    researcher_agent = ChatAgent(
        name="ResearcherAgent",
        description="Specialist in research and information gathering",
        instructions=(
            "You are a Researcher. You find information without additional computation or quantitative analysis."
        ),
        # This agent requires the gpt-4o-search-preview model to perform web searches.
        # Feel free to explore with other agents that support web search, for example,
        # the `OpenAIResponseAgent` or `AzureAgentProtocol` with bing grounding.
        chat_client=OpenAIChatClient(model_id="gpt-4o-search-preview"),
    )

    coder_agent = ChatAgent(
        name="CoderAgent",
        description="A helpful assistant that writes and executes code to process and analyze data.",
        instructions="You solve questions using code. Please provide detailed analysis and computation process.",
        chat_client=OpenAIResponsesClient(),
        tools=HostedCodeInterpreterTool(),
    )

    # Callbacks
    def on_exception(exception: Exception) -> None:
        print(f"Exception occurred: {exception}")
        logger.exception("Workflow exception", exc_info=exception)

    last_stream_agent_id: str | None = None
    stream_line_open: bool = False

    print("\nBuilding Magentic Workflow...")

    workflow = (
        MagenticBuilder()
        .participants(researcher=researcher_agent, coder=coder_agent)
        .with_standard_manager(
            chat_client=OpenAIChatClient(),
            max_round_count=10,
            max_stall_count=3,
            max_reset_count=2,
        )
        .with_plan_review()
        .build()
    )

    task = (
        "I am preparing a report on the energy efficiency of different machine learning model architectures. "
        "Compare the estimated training and inference energy consumption of ResNet-50, BERT-base, and GPT-2 "
        "on standard datasets (e.g., ImageNet for ResNet, GLUE for BERT, WebText for GPT-2). "
        "Then, estimate the CO2 emissions associated with each, assuming training on an Azure Standard_NC6s_v3 "
        "VM for 24 hours. Provide tables for clarity, and recommend the most energy-efficient model "
        "per task type (image classification, text classification, and text generation)."
    )

    print(f"\nTask: {task}")
    print("\nStarting workflow execution...")

    try:
        pending_request: RequestInfoEvent | None = None
        pending_responses: dict[str, MagenticPlanReviewReply] | None = None
        completed = False
        workflow_output: str | None = None

        while not completed:
            # Use streaming for both initial run and response sending
            if pending_responses is not None:
                stream = workflow.send_responses_streaming(pending_responses)
            else:
                stream = workflow.run_stream(task)

            # Collect events from the stream
            async for event in stream:
                if isinstance(event, MagenticOrchestratorMessageEvent):
                    print(f"\n[ORCH:{event.kind}]\n\n{getattr(event.message, 'text', '')}\n{'-' * 26}")
                elif isinstance(event, MagenticAgentDeltaEvent):
                    if last_stream_agent_id != event.agent_id or not stream_line_open:
                        if stream_line_open:
                            print()
                        print(f"\n[STREAM:{event.agent_id}]: ", end="", flush=True)
                        last_stream_agent_id = event.agent_id
                        stream_line_open = True
                    if event.text:
                        print(event.text, end="", flush=True)
                elif isinstance(event, MagenticAgentMessageEvent):
                    if stream_line_open:
                        print(" (final)")
                        stream_line_open = False
                        print()
                    msg = event.message
                    if msg is not None:
                        response_text = (msg.text or "").replace("\n", " ")
                        print(f"\n[AGENT:{event.agent_id}] {msg.role.value}\n\n{response_text}\n{'-' * 26}")
                elif isinstance(event, MagenticFinalResultEvent):
                    print("\n" + "=" * 50)
                    print("FINAL RESULT:")
                    print("=" * 50)
                    if event.message is not None:
                        print(event.message.text)
                    print("=" * 50)
                if isinstance(event, RequestInfoEvent) and event.request_type is MagenticPlanReviewRequest:
                    pending_request = event
                    review_req = cast(MagenticPlanReviewRequest, event.data)
                    if review_req.plan_text:
                        print(f"\n=== PLAN REVIEW REQUEST ===\n{review_req.plan_text}\n")
                elif isinstance(event, WorkflowOutputEvent):
                    # Capture workflow output during streaming
                    workflow_output = str(event.data) if event.data else None
                    completed = True

            if stream_line_open:
                print()
                stream_line_open = False
            pending_responses = None

            # Handle pending plan review request
            if pending_request is not None:
                # Get human input for plan review decision
                print("Plan review options:")
                print("1. approve - Approve the plan as-is")
                print("2. revise - Request revision of the plan")
                print("3. exit - Exit the workflow")

                while True:
                    choice = input("Enter your choice (approve/revise/exit): ").strip().lower()  # noqa: ASYNC250
                    if choice in ["approve", "1"]:
                        reply = MagenticPlanReviewReply(decision=MagenticPlanReviewDecision.APPROVE)
                        break
                    if choice in ["revise", "2"]:
                        reply = MagenticPlanReviewReply(decision=MagenticPlanReviewDecision.REVISE)
                        break
                    if choice in ["exit", "3"]:
                        print("Exiting workflow...")
                        return
                    print("Invalid choice. Please enter 'approve', 'revise', or 'exit'.")

                pending_responses = {pending_request.request_id: reply}
                pending_request = None

        # Show final result from captured workflow output
        if workflow_output:
            print(f"Workflow completed with result:\n\n{workflow_output}")

    except Exception as e:
        print(f"Workflow execution failed: {e}")
        on_exception(e)


if __name__ == "__main__":
    asyncio.run(main())
