from __future__ import annotations

import asyncio
from typing import Any

from core.ai_reply_flow import execute_ai_turn
from core.find_image_flow import handle_find_image_batch_flow
from core.order_flow import handle_order_notification
from core.prompt_flow import build_prompt_bundle
from core.reply_finalize_flow import finalize_ai_reply
from utils.metrics_tracker import emit as metrics_emit
from utils.observability import build_trace_id


async def process_incoming_message(agent: Any, message: Any) -> Any:
    """主消息处理编排：预处理 -> 业务流 -> AI -> 收尾。"""
    trace_id = build_trace_id(message.acc_id, message.from_id, message.msg_id, message.msg[:64])
    agent._activity_log(
        "agent_inbound",
        trace_id=trace_id,
        acc_id=message.acc_id,
        customer_id=message.from_id,
        msg=message.msg,
        msg_type=message.msg_type,
    )
    metrics_emit("inbound_msg", customer_id=message.from_id, acc_id=message.acc_id)

    state = agent._get_conversation_state(message.from_id)
    pre_response = await agent.pre_rule_service.run(message=message, state=state, trace_id=trace_id)
    if pre_response is not None:
        return pre_response

    new_stage = agent._detect_stage(message.msg)
    if new_stage != state.stage:
        state.stage = new_stage

    from datetime import datetime

    state.last_update = datetime.now().isoformat()

    order_response = await handle_order_notification(agent, message=message, state=state)
    if order_response is not None:
        return order_response

    customer_text, _ = agent._split_customer_text(message.msg)
    shop_type = agent._get_shop_type(message.acc_id or "", message.goods_name or "")
    flow_response = await handle_find_image_batch_flow(
        agent,
        message=message,
        state=state,
        customer_text=customer_text,
        shop_type=shop_type,
    )
    if flow_response is not None:
        return flow_response

    prompt_bundle = build_prompt_bundle(agent, message=message, state=state)
    user_prompt = prompt_bundle.user_prompt
    deps = prompt_bundle.deps
    history = prompt_bundle.history

    agent._log_block("PROMPT->AI 前置提示词", user_prompt)

    try:
        reply_text = await execute_ai_turn(
            agent,
            message=message,
            state=state,
            user_prompt=user_prompt,
            deps=deps,
            history=history,
        )
    except Exception as e:
        err_str = str(e)
        print(f"[Agent] AI 调用失败: {e}，使用兜底回复")
        agent._activity_log("agent_ai_error", customer_id=message.from_id, acc_id=message.acc_id, error=err_str)
        metrics_emit("ai_call_failed", customer_id=message.from_id, acc_id=message.acc_id)
        if "AccountOverdueError" in err_str or "overdue" in err_str.lower():
            asyncio.create_task(agent._notify_wechat_overdue())
        else:
            asyncio.create_task(
                agent._notify_wechat(
                    f"⚠️ **AI调用异常**\n"
                    f"客户：{message.from_id}\n"
                    f"店铺：{message.acc_id}\n"
                    f"错误：{err_str[:200]}",
                    tag="AI异常",
                )
            )
        reply_text = None
    else:
        metrics_emit("ai_call_success", customer_id=message.from_id, acc_id=message.acc_id)

    if not reply_text:
        fallback_text = await agent._rewrite_reply_with_ai(
            message=message,
            state=state,
            reply="好嘞，你稍等下，我这边看一下",
            scene="fallback_reply",
        )
        from core.pydantic_ai_agent import AgentResponse

        return AgentResponse(reply=fallback_text, should_reply=True, need_transfer=False)

    return await finalize_ai_reply(
        agent,
        message=message,
        state=state,
        reply_text=reply_text,
    )