#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
services/mcp_collector_service.py
MCP 外部情報收集層

透過 Gemini Google Search Grounding 收集外部市場情報，供 OpenClaw 戰略分析使用：
  - 台灣電商市場趨勢
  - 節日 / 促銷行事曆
  - 季節性消費洞察
  - 競品動態（蝦皮/PChome/momo/Yahoo）
  - 消費者情緒與熱銷品類

結果快取至 ai_insights（type='mcp_cache'），24h TTL 避免重複呼叫。
"""

import json
import logging
import os
import time
from datetime import datetime, timedelta
from typing import Any, Dict, List, Optional

from database.manager import get_session
from sqlalchemy import text

logger = logging.getLogger(__name__)

GEMINI_API_KEY = os.getenv("GEMINI_API_KEY", "")
MCP_CACHE_TTL_HOURS = int(os.getenv("MCP_CACHE_TTL_HOURS", "24"))
MCP_MODEL = os.getenv("MCP_GEMINI_MODEL", "gemini-2.0-flash")

# ── 查詢主題定義 ────────────────────────────────────────────────────────────
_SEARCH_TOPICS = {
    "market_trends": (
        "台灣電商 momo購物網 2026年熱銷商品趨勢 消費者行為 美妝保養 家電 生活用品"
    ),
    "holiday_calendar": (
        "2026年台灣重要節日促銷行事曆 母親節 618購物節 雙11 雙12 中秋 跨年 電商大促"
    ),
    "seasonal_insights": (
        "台灣電商季節性銷售趨勢 換季商品 夏季防曬 冬季保暖 Q3 Q4 消費高峰"
    ),
    "competitor_intel": (
        "momo購物網 PChome 蝦皮 Yahoo購物 2026年競爭策略 促銷活動 物流比較"
    ),
    "consumer_sentiment": (
        "台灣消費者 2026 購物偏好 低價高CP 品牌忠誠度 直播購物 社群電商 KOL影響"
    ),
    "pricing_strategy": (
        "台灣電商定價策略 動態定價 競品比價 心理定價 促銷折扣最佳時機"
    ),
}


class MCPCollectorService:
    """
    外部情報收集服務（MCP 節點）
    使用 Gemini Search Grounding 抓取即時市場資訊
    """

    def __init__(self):
        self._initialized = False
        self._genai = None

    def _ensure_init(self) -> bool:
        if self._initialized:
            return True
        if not GEMINI_API_KEY:
            logger.warning("[MCP] GEMINI_API_KEY 未設定，跳過外部情報收集")
            return False
        try:
            import google.generativeai as genai
            genai.configure(api_key=GEMINI_API_KEY)
            self._genai = genai
            self._initialized = True
            return True
        except ImportError:
            logger.error("[MCP] google-generativeai 未安裝")
            return False
        except Exception as e:
            logger.error("[MCP] Gemini 初始化失敗: %s", e)
            return False

    # ── 快取讀寫 ────────────────────────────────────────────────────────────

    def _read_cache(self, topic: str) -> Optional[str]:
        session = get_session()
        try:
            row = session.execute(
                text(f"""
                    SELECT content FROM ai_insights
                    WHERE insight_type = 'mcp_cache'
                      AND created_by = 'mcp_collector'
                      AND metadata_json::jsonb ->> 'topic' = :topic
                      AND created_at >= NOW() - INTERVAL '{MCP_CACHE_TTL_HOURS} hours'
                    ORDER BY created_at DESC LIMIT 1
                """),
                {"topic": topic},
            ).fetchone()
            if row:
                logger.debug("[MCP] 快取命中: %s", topic)
                return row[0]
            return None
        except Exception:
            return None
        finally:
            session.close()

    def _write_cache(self, topic: str, content: str) -> None:
        session = get_session()
        try:
            row = session.execute(text("""
                INSERT INTO ai_insights
                    (insight_type, content, confidence, created_by, status, metadata_json)
                VALUES ('mcp_cache', :content, 0.9, 'mcp_collector', 'active', :meta)
                RETURNING id
            """), {
                "content": content[:4000],
                "meta": json.dumps({"topic": topic, "model": MCP_MODEL, "cached_at": datetime.now().isoformat()})
            }).fetchone()
            session.commit()
            if row:
                try:
                    from services.openclaw_learning_service import enqueue_insight_embedding
                    enqueue_insight_embedding(row[0], "mcp_cache", content[:4000])
                except Exception as embed_err:
                    logger.warning("[MCP] embedding queue enqueue failed: %s", embed_err)
        except Exception as e:
            logger.warning("[MCP] 快取寫入失敗: %s", e)
            session.rollback()
        finally:
            session.close()

    # ── 單主題搜尋 ──────────────────────────────────────────────────────────

    def _search_topic(self, topic: str, query: str) -> str:
        if not self._ensure_init():
            return ""

        cached = self._read_cache(topic)
        if cached:
            return cached

        try:
            model = self._genai.GenerativeModel(
                model_name=MCP_MODEL,
                tools=["google_search_retrieval"],
            )
            response = model.generate_content(
                f"請用繁體中文整理以下主題的最新資訊，提供具體數據與洞察，500字以內：\n{query}"
            )
            content = response.text or ""
            if content:
                self._write_cache(topic, content)
            return content
        except Exception as e:
            logger.warning("[MCP] 搜尋失敗 topic=%s: %s", topic, e)
            return ""

    # ── 公開介面 ────────────────────────────────────────────────────────────

    def collect_all(self) -> Dict[str, str]:
        """
        收集所有外部情報主題，回傳 {topic: content} 字典。
        各主題獨立失敗不影響整體。
        """
        results = {}
        for topic, query in _SEARCH_TOPICS.items():
            try:
                results[topic] = self._search_topic(topic, query)
                time.sleep(0.5)  # 避免 Gemini rate limit
            except Exception as e:
                logger.error("[MCP] topic=%s 收集失敗: %s", topic, e)
                results[topic] = ""
        logger.info("[MCP] 收集完成，有效主題=%d/%d", sum(1 for v in results.values() if v), len(results))
        return results

    def collect_topic(self, topic: str) -> str:
        """收集單一主題"""
        query = _SEARCH_TOPICS.get(topic, topic)
        return self._search_topic(topic, query)

    def get_holiday_context(self) -> str:
        """取得節日行事曆（供 Prompt 注入）"""
        now = datetime.now()
        month = now.month
        day = now.day

        # 靜態台灣電商節日知識庫
        static_calendar = {
            1:  "元旦促銷、農曆新年備貨期",
            2:  "農曆新年、情人節（2/14）",
            3:  "婦女節（3/8）、開學季",
            4:  "清明連假（4月初）、春季大促、換季服飾高峰",
            5:  "母親節（5月第2週，年度大促）、520情人節、勞動節（5/1）",
            6:  "618購物節（年中最大促銷）、端午節",
            7:  "暑假開端、父親節前哨站",
            8:  "父親節（8/8）、七夕情人節",
            9:  "中秋節、開學季、百貨週年慶預熱",
            10: "雙10國慶、百貨週年慶高峰期",
            11: "雙11光棍節（全年最強電商季）、黑五大促",
            12: "雙12年終慶、聖誕節、跨年備貨",
        }
        
        current_focus = static_calendar.get(month, "")
        next_month = (month % 12) + 1
        next_focus = static_calendar.get(next_month, "")
        
        # 月底優化：若超過 20 號，自動將焦點轉向「下月預告」，避免產生如「4月底還在過清明節」的幻覺
        if day > 20:
            header = f"當前日期：{now.strftime('%Y/%m/%d')} (月底轉場期)"
            body = f"本月即將結束，目前重點已轉向：{next_focus}"
            footer = f"下月詳細預告：{next_focus}"
        else:
            header = f"當前日期：{now.strftime('%Y/%m/%d')}"
            body = f"本月電商重點：{current_focus}"
            footer = f"下月預告：{next_focus}"

        return f"{header}\n{body}\n{footer}"

    def get_seasonal_context(self) -> str:
        """季節性消費情境"""
        month = datetime.now().month
        seasons = {
            (3, 4, 5): "春季：換季保養、外出服飾、春遊裝備",
            (6, 7, 8): "夏季：防曬/美白、涼感寢具、戶外運動、冷氣清潔",
            (9, 10, 11): "秋季：保濕修護、秋冬服飾、保健養生、熱飲週邊",
            (12, 1, 2): "冬季：保暖寢具、暖身家電、年節禮品、養生補品",
        }
        for months, desc in seasons.items():
            if month in months:
                return desc
        return ""


# 模組單例
mcp_collector = MCPCollectorService()