"""
客服工作流 + 图片任务状态机

架构说明：
- CustomerServiceWorkflow 负责管理图片处理任务的完整生命周期
- 图片AI接入点：调用 workflow.image_ai_submit_result(task_id, result_url)
- 消息回调接口：通过 register_send_callback 注入发送函数
"""
import asyncio
import os
import uuid
from enum import Enum
from typing import Optional, Dict, Callable, Awaitable, Any
from datetime import datetime
from dataclasses import dataclass, field

_WECHAT_WEBHOOK = os.getenv("WECHAT_WEBHOOK", "")


async def _wechat_notify(content: str):
    """workflow 内部异常推送企业微信"""
    if not _WECHAT_WEBHOOK:
        return
    try:
        import httpx
        async with httpx.AsyncClient(timeout=10) as client:
            resp = await client.post(_WECHAT_WEBHOOK, json={
                "msgtype": "markdown",
                "markdown": {"content": content}
            })
            data = resp.json()
            if data.get("errcode") == 0:
                print(f"[Workflow通知] 企业微信推送成功 ✓")
            else:
                print(f"[Workflow通知] 企业微信推送失败: {data}")
    except Exception as e:
        print(f"[Workflow通知] 推送异常: {e}")

from db.customer_db import db


# ========== 任务状态 ==========

class TaskStatus(Enum):
    PENDING          = "待处理"       # 任务已创建，等待图片AI处理
    PROCESSING       = "处理中"       # 图片AI正在处理
    AWAITING_CONFIRM = "等待客户确认"  # 结果已发给客户，等待确认
    REVISION         = "修改中"       # 客户要求修改，重新处理
    COMPLETED        = "已完成"       # 客户确认，邮件已发
    FAILED           = "失败"         # 处理失败


# ========== 任务数据结构 ==========

@dataclass
class ImageTask:
    task_id: str
    customer_id: str
    customer_name: str
    original_image: str          # 原图路径或URL
    operation: str               # 处理操作类型
    requirements: str = ""       # 客户原始需求描述
    result_url: str = ""         # 处理结果URL
    email: str = ""              # 客户邮箱
    status: TaskStatus = TaskStatus.PENDING
    revision_count: int = 0      # 修改次数
    created_at: str = field(default_factory=lambda: datetime.now().isoformat())
    updated_at: str = field(default_factory=lambda: datetime.now().isoformat())

    def update_status(self, status: TaskStatus):
        self.status = status
        self.updated_at = datetime.now().isoformat()


# ========== 工作流 ==========

class CustomerServiceWorkflow:
    """
    客服工作流

    图片AI对接方式：
        1. 调用 create_image_task() 创建任务，获取 task_id
        2. 图片AI处理完成后调用 image_ai_submit_result(task_id, result_url)
        3. 工作流自动发图给客户确认，并等待客户回复
    """

    def __init__(self):
        self.tasks: Dict[str, ImageTask] = {}              # task_id -> ImageTask
        self.customer_active_task: Dict[str, str] = {}    # customer_id -> 最新 task_id
        self._send_message: Optional[Callable] = None     # 注入的消息发送函数
        self._agent_notify: Optional[Callable] = None     # 注入的 AI 通知函数
        self._pending_analysis: Dict[str, dict] = {}      # 待报价的识别结果

    # ========== 回调注册（由 websocket_client 调用）==========

    def register_agent_notify_callback(self, callback: Callable):
        """
        注册 AI 通知回调，图片处理完成时调用 AI 生成消息发给客户。

        callback 签名：
            async def notify(customer_id, acc_id, acc_type, system_prompt)
        """
        self._agent_notify = callback

    def register_send_callback(self, callback: Callable[[str, str, str, int], Awaitable[None]]):
        """
        注册消息发送回调函数

        callback 签名：
            async def send(customer_id, acc_id, acc_type, content, msg_type=0)
        """
        self._send_message = callback

    # ========== 任务管理 ==========

    def create_image_task(
        self,
        customer_id: str,
        customer_name: str,
        original_image: str,
        operation: str,
        requirements: str = ""
    ) -> str:
        """
        创建图片处理任务，返回 task_id

        图片AI收到此 task_id 后开始处理，完成后调用 image_ai_submit_result
        """
        task_id = str(uuid.uuid4())
        task = ImageTask(
            task_id=task_id,
            customer_id=customer_id,
            customer_name=customer_name,
            original_image=original_image,
            operation=operation,
            requirements=requirements,
        )
        self.tasks[task_id] = task
        self.customer_active_task[customer_id] = task_id

        # 记录需求到客户画像
        if requirements:
            db.add_requirement(customer_id, requirements)

        print(f"[Workflow] 创建任务 {task_id} | 客户: {customer_name} | 操作: {operation}")
        return task_id

    def get_task(self, task_id: str) -> Optional[ImageTask]:
        return self.tasks.get(task_id)

    def get_customer_active_task(self, customer_id: str) -> Optional[ImageTask]:
        task_id = self.customer_active_task.get(customer_id)
        return self.tasks.get(task_id) if task_id else None

    # ========== 图片识别AI接入点（报价用）==========

    async def image_analysis_result(
        self,
        customer_id: str,
        image_url: str,
        complexity: str,
        acc_id: str = "",
        acc_type: str = "AliWorkbench",
        gemini_prompt: str = "",
        aspect_ratio: str = "1:1",
        perspective: str = "no",
        proc_type: str = "",
        subject: str = "",
        quality: str = "",
    ) -> bool:
        """
        【图片识别AI专用接口】分析完成后调用此方法，触发客服AI报价

        Args:
            customer_id: 客户ID
            image_url:   图片URL（原图）
            complexity:  复杂度评估结果，枚举值：
                         "simple"  → 10-20元
                         "normal"  → 20-30元
                         "complex" → 30元
                         "hard"    → 40元
            acc_id:      店铺账号ID
            acc_type:    平台类型

        Returns:
            True = 成功触发报价，False = 客户不存在
        """
        price_map = {
            "simple":  "10-15元，这张比较简单",
            "normal":  "15-20元",
            "complex": "20-25元",
            "hard":    "25-30元",
        }
        price_hint = price_map.get(complexity, "20元")

        # 把所有分析字段存入任务
        requirements = f"complexity:{complexity}"
        if gemini_prompt:
            requirements += f"|prompt:{gemini_prompt}"
        if aspect_ratio:
            requirements += f"|ratio:{aspect_ratio}"
        if perspective and perspective != "no":
            requirements += f"|perspective:{perspective}"
        if proc_type:
            requirements += f"|proc_type:{proc_type}"
        if subject:
            requirements += f"|subject:{subject}"
        if quality:
            requirements += f"|quality:{quality}"

        task_id = self.create_image_task(
            customer_id=customer_id,
            customer_name=customer_id,
            original_image=image_url,
            operation="enhance",
            requirements=requirements,
        )

        print(f"[Workflow] 图片识别完成 | 客户:{customer_id} | 复杂度:{complexity} | 建议报价:{price_hint}")

        # 通知客服AI报价（把识别结果注入消息，让AI根据结果报价）
        if self._send_message:
            # 这里不直接发价格，而是触发 agent 重新处理一条带识别结果的内部消息
            # 实际报价由客服AI根据 complexity 生成，保持口吻一致
            self._pending_analysis[customer_id] = {
                "task_id": task_id,
                "complexity": complexity,
                "price_hint": price_hint,
                "image_url": image_url,
            }
        return True

    def get_pending_analysis(self, customer_id: str) -> dict:
        """
        客服AI处理消息时调用，检查该客户是否有待报价的识别结果
        取出后自动清除（一次性）
        """
        return self._pending_analysis.pop(customer_id, None)

    # ========== 付款后触发 Gemini 作图 ==========

    async def trigger_processing_on_payment(
        self,
        customer_id: str,
        acc_id: str = "",
        acc_type: str = "AliWorkbench"
    ) -> bool:
        try:
            from config.config import IMAGE_MODULE_ENABLED
            if not IMAGE_MODULE_ENABLED:
                await _wechat_notify(
                    f"ℹ️ **付款触发但已暂停自动作图**\n客户：{customer_id}\n店铺：{acc_id}\n请人工安排处理"
                )
                return False
        except Exception:
            return False
        """
        客户付款后调用此方法，找到该客户待处理的任务并启动 Gemini 作图。
        由 pydantic_ai_agent 在识别到"已付款"订单通知时调用。
        也可作为 tool 由 AI 主动触发。

        Returns:
            True=已启动处理, False=无待处理任务
        """
        task = self.get_customer_active_task(customer_id)

        if not task:
            # 内存任务丢失（重启场景）→ 从客户档案重建
            print(f"[Workflow] 付款触发：内存无任务，尝试从客户档案重建 | 客户: {customer_id}")
            task = await self._rebuild_task_from_profile(customer_id, acc_id, acc_type)
            if not task:
                print(f"[Workflow] 付款触发：客户 {customer_id} 无图片记录，无法重建任务，跳过")
                await _wechat_notify(
                    f"⚠️ **付款但无图片**\n"
                    f"客户：{customer_id}\n"
                    f"店铺：{acc_id}\n"
                    f"已付款但找不到待处理图片，请人工发图处理"
                )
                return False

        if task.status not in (TaskStatus.PENDING,):
            print(f"[Workflow] 付款触发：任务 {task.task_id[:8]}... 状态={task.status.value}，跳过")
            return False

        task.operation = task.operation or "enhance"
        print(f"[Workflow] 付款确认，启动 Gemini 处理 | 客户: {customer_id} | 任务: {task.task_id[:8]}...")
        asyncio.create_task(self._auto_process(task.task_id, acc_id=acc_id, acc_type=acc_type))
        return True

    async def _rebuild_task_from_profile(
        self, customer_id: str, acc_id: str, acc_type: str
    ) -> Optional["ImageTask"]:
        """
        重启后任务丢失时，从客户档案里读取 last_image_url 重建一个 PENDING 任务。
        """
        try:
            from db.customer_db import db
            profile = db.get_customer(customer_id)
            image_url = profile.last_image_url
            if not image_url:
                return None

            complexity     = profile.complexity_history[-1] if profile.complexity_history else ""
            gemini_prompt  = getattr(profile, "last_gemini_prompt", "")
            aspect_ratio   = getattr(profile, "last_aspect_ratio", "1:1")
            perspective    = getattr(profile, "last_perspective", "no")

            requirements = f"complexity:{complexity}" if complexity else ""
            if gemini_prompt:
                requirements += f"|prompt:{gemini_prompt}"
            if aspect_ratio:
                requirements += f"|ratio:{aspect_ratio}"
            if perspective and perspective != "no":
                requirements += f"|perspective:{perspective}"

            task_id = str(uuid.uuid4())
            task = ImageTask(
                task_id=task_id,
                customer_id=customer_id,
                customer_name=profile.name or customer_id,
                original_image=image_url,
                operation="enhance",
                requirements=requirements,
                status=TaskStatus.PENDING,
            )
            self.tasks[task_id] = task
            self.customer_active_task[customer_id] = task_id
            print(f"[Workflow] 任务已重建 | 客户: {customer_id} | 图片: {image_url[:60]}...")
            return task
        except Exception as e:
            print(f"[Workflow] 任务重建失败: {e}")
            return None

    @staticmethod
    def _parse_requirements(requirements: str) -> dict:
        """从 requirements 字符串解析各字段，格式: complexity:xxx|prompt:xxx|ratio:xxx"""
        parsed = {}
        for part in (requirements or "").split("|"):
            part = part.strip()
            if ":" in part:
                k, v = part.split(":", 1)
                parsed[k.strip()] = v.strip()
        return parsed

    async def _auto_process(self, task_id: str, acc_id: str = "", acc_type: str = "AliWorkbench"):
        """付款确认后自动调用 Gemini 处理图片，完成后通知客户"""
        try:
            from config.config import IMAGE_MODULE_ENABLED
            if not IMAGE_MODULE_ENABLED:
                return
        except Exception:
            return
        task = self.tasks.get(task_id)
        if not task:
            return
        task.update_status(TaskStatus.PROCESSING)

        req = self._parse_requirements(task.requirements)
        gemini_prompt = req.get("prompt", "")
        aspect_ratio  = req.get("ratio", "1:1")
        perspective   = req.get("perspective", "no")
        proc_type     = req.get("proc_type", "")
        subject       = req.get("subject", "")
        quality       = req.get("quality", "")
        revision_note = req.get("revision", "")
        # 客户修改意见追加到 prompt 末尾
        if revision_note:
            gemini_prompt = (gemini_prompt or "") + f"\n【客户修改要求】{revision_note}"

        print(f"[Workflow] Gemini 开始处理 | 任务: {task_id[:8]}... | 比例: {aspect_ratio} | 透视: {perspective} | 图片: {task.original_image}")
        try:
            from image.image_processor import image_processor
            from utils.image_queue import run_with_queue
            result = await run_with_queue(image_processor.process_image(
                task.original_image,
                task.operation,
                requirements=task.requirements,
                gemini_prompt=gemini_prompt,
                aspect_ratio=aspect_ratio,
                perspective=perspective,
                proc_type=proc_type,
                subject=subject,
                quality=quality,
            ))
            if result["success"]:
                attempts  = result.get("attempts", 1)
                qa_score  = result.get("qa_score", 0)
                qa_pass   = result.get("qa_pass", True)
                qa_issue  = result.get("qa_issue", "")
                print(f"[Workflow] Gemini 处理完成 | 任务: {task_id[:8]}... | 质检: {qa_score}分 | 尝试: {attempts}次")

                # 质检未通过（已达重试上限，保留结果但人工跟进）
                if not qa_pass:
                    await _wechat_notify(
                        f"⚠️ **图片质检未通过，请人工核查**\n"
                        f"客户：{task.customer_id}\n"
                        f"店铺：{acc_id}\n"
                        f"质检得分：{qa_score}/100\n"
                        f"问题：{qa_issue}\n"
                        f"已处理 {attempts} 次，结果已发出，请人工确认质量"
                    )

                await self.image_ai_submit_result(
                    task_id=task_id,
                    result_url=result["result_path"],
                    acc_id=acc_id,
                    acc_type=acc_type,
                )
            else:
                err_msg = result['message']
                print(f"[Workflow] Gemini 处理失败: {err_msg}")
                task.update_status(TaskStatus.FAILED)
                # 企业微信预警
                await _wechat_notify(
                    f"⚠️ **Gemini作图失败**\n"
                    f"客户：{task.customer_id}\n"
                    f"店铺：{acc_id}\n"
                    f"原因：{err_msg[:200]}\n"
                    f"请人工跟进"
                )
                # 通知客户稍等，人工跟进
                if self._send_message:
                    await self._send_message(
                        customer_id=task.customer_id,
                        acc_id=acc_id,
                        acc_type=acc_type,
                        content="您好，图片正在处理中，稍后发您，请稍等",
                        msg_type=0,
                    )
        except Exception as e:
            print(f"[Workflow] 自动处理异常: {e}")
            task.update_status(TaskStatus.FAILED)
            await _wechat_notify(
                f"⚠️ **Workflow处理异常**\n"
                f"客户：{task.customer_id}\n"
                f"错误：{str(e)[:200]}"
            )

    # ========== 图片AI接入点（作图用）==========

    async def image_ai_submit_result(
        self,
        task_id: str,
        result_url: str,
        acc_id: str = "",
        acc_type: str = "AliWorkbench"
    ) -> bool:
        """
        【图片AI专用接口】处理完成后调用此方法

        Args:
            task_id:    create_image_task 返回的任务ID
            result_url: 处理后的图片URL或本地路径
            acc_id:     店铺账号ID（发消息用）
            acc_type:   平台类型

        Returns:
            True = 成功，False = 任务不存在
        """
        task = self.tasks.get(task_id)
        if not task:
            print(f"[Workflow] 任务不存在: {task_id}")
            return False

        task.result_url = result_url
        task.update_status(TaskStatus.AWAITING_CONFIRM)

        print(f"[Workflow] 任务 {task_id} 处理完成，发送给客户确认")

        # 先发结果图片
        if self._send_message:
            await self._send_message(
                customer_id=task.customer_id,
                acc_id=acc_id,
                acc_type=acc_type,
                content=result_url,
                msg_type=1  # 图片
            )

        # 让客服 AI 生成完成通知话术（自然口吻，询问邮箱）
        if self._agent_notify:
            await self._agent_notify(
                customer_id=task.customer_id,
                acc_id=acc_id,
                acc_type=acc_type,
                system_hint="【图片已处理完成并发给客户】请用自然口吻告诉客户图发好了，让他看一下效果，没问题把邮箱发过来，你来发给他。不超过1句话。",
            )
        elif self._send_message:
            # 兜底：AI 不可用时用固定话术
            await self._send_message(
                customer_id=task.customer_id,
                acc_id=acc_id,
                acc_type=acc_type,
                content="好了，你看一下效果，没问题把邮箱发我",
                msg_type=0,
            )

        return True

    # ========== 客户回复处理 ==========

    async def handle_customer_reply(
        self,
        customer_id: str,
        message: str,
        acc_id: str = "",
        acc_type: str = "AliWorkbench"
    ) -> Optional[str]:
        """
        处理正在等待确认的客户回复

        Returns:
            需要回复客户的文本，None 表示不是确认相关消息
        """
        task = self.get_customer_active_task(customer_id)
        if not task or task.status != TaskStatus.AWAITING_CONFIRM:
            return None

        msg = message.strip()

        # 提取邮箱
        import re
        email_match = re.search(r'[\w\.-]+@[\w\.-]+\.\w+', msg)
        if email_match:
            email = email_match.group()
            task.email = email
            db.update_email(customer_id, email)
            # 发送邮件（调用 email_sender）
            result = await self._send_email(task)
            if result:
                task.update_status(TaskStatus.COMPLETED)
                db.update_email_status(task.customer_id, "sent")
                db.complete_order(task.customer_id, had_revision=task.revision_count > 0)
                db.auto_compute_tags(task.customer_id)
                return "发到您邮箱了，注意查收哈"
            else:
                db.update_email_status(task.customer_id, "failed")
                return "邮件发送失败了，您再发一次邮箱试试"

        # 客户说不满意/要改
        negative_keywords = ["不好", "不对", "不满意", "重做", "改一下", "差太多", "不行", "效果不好", "颜色不对"]
        if any(kw in msg for kw in negative_keywords):
            task.revision_count += 1
            task.update_status(TaskStatus.REVISION)
            db.record_revision(task.customer_id)
            # 把客户的修改意见追加进 requirements，下次重做时 Gemini 能看到
            if msg:
                task.requirements += f"|revision:{msg[:100]}"
            return "好，你说一下哪里要改，或者发图告诉我"

        # 客户提供了修改说明（处于 REVISION 状态时）
        if task.status == TaskStatus.REVISION and msg:
            task.requirements += f"|revision:{msg[:100]}"
            task.update_status(TaskStatus.PENDING)
            # 重新触发处理
            asyncio.create_task(
                self._auto_process(task.task_id, acc_id=acc_id, acc_type=acc_type)
            )
            return "好的，重新给你做"

        return None

    async def _send_email(self, task: ImageTask) -> bool:
        """发送完成作品邮件"""
        try:
            from mail.email_sender import email_sender
            profile = db.get_customer(task.customer_id)
            result = email_sender.send_completed_work(
                to_email=task.email,
                customer_name=profile.name or task.customer_name,
                image_description=task.requirements or task.operation,
                result_images=[task.result_url]
            )
            return result.get("success", False)
        except Exception as e:
            print(f"[Workflow] 邮件发送失败: {e}")
            await _wechat_notify(
                f"⚠️ **邮件发送失败**\n"
                f"客户：{task.customer_id}\n"
                f"邮箱：{task.email}\n"
                f"错误：{str(e)[:200]}"
            )
            return False

    # ========== 工具方法 ==========

    def detect_operation(self, message: str) -> str:
        """根据客户描述识别处理操作"""
        msg = message.lower()
        if any(kw in msg for kw in ["模糊", "清晰", "高清", "变清"]):
            return "enhance"
        elif any(kw in msg for kw in ["背景", "去背", "抠图", "透明"]):
            return "remove_bg"
        elif any(kw in msg for kw in ["尺寸", "大小", "缩放", "分辨率"]):
            return "resize"
        elif any(kw in msg for kw in ["老照片", "修复", "发黄", "破损"]):
            return "fix_old_photo"
        elif any(kw in msg for kw in ["分层", "psd"]):
            return "layered"
        else:
            return "enhance"

    def get_task_summary(self) -> str:
        """获取当前所有任务摘要（调试用）"""
        if not self.tasks:
            return "暂无任务"
        lines = []
        for tid, task in self.tasks.items():
            lines.append(
                f"  [{task.status.value}] {task.customer_name} | {task.operation} | {tid[:8]}..."
            )
        return "\n".join(lines)


# ========== 全局实例 ==========
workflow = CustomerServiceWorkflow()