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Wave 1.2：用一份 generator 解 4 種時間維度報表（季/半年/年/TTM）。 generate_period_review_ppt（services/ppt_generator.py）— 12 頁 - 期間類型徽章自動切換（季報/半年報/年報/TTM 滾動） - P1 封面：含業績狀態徽章 + elevator pitch + 期間動能 - P2 執行摘要：KPI v2 含 △% (vs 上季/上半/去年) + AI 解讀 + YoY 對比帶 - P3 月度業績走勢（matplotlib 折線：本期 + 上期 + 月均線 + 高低點）+ 4 卡指標 - P4 品類分析：橫條 + 帕雷托雙視圖 - P5-P7 TOP 50 商品（自動分頁） - P8 TOP 30 廠商 - P9 MCP 市場情報 - P10 AI 結構化洞察 - P11 附錄 query_period_summary（routes/openclaw_bot_routes.py） - 一次拉齊：kpis / monthly_breakdown / top_products(50) / top_categories(8) / top_vendors(30) - 自動算月度聚合（PostgreSQL TO_CHAR） 路由層加 4 種 sub_type 分支： - /ppt quarterly [YYYY/Q1-4] 當季或指定季 - /ppt half_yearly [YYYY/H1-2] 當半年或指定半年 - /ppt annual [YYYY] 當年或指定年 - /ppt ttm 最近 12 個月（自動算到本月底） 每種自動抓三段資料：本期 + 上期同等 + 去年同期 - quarterly: 上季 + 去年同季 - half_yearly: 上半年 + 去年同半年 - annual: 去年 + 前年 - ttm: 上一個 TTM (24-12 月前) + 同上 _ppt_ai_analysis 加 is_period 分支 - 角色：策略顧問 + BU 主管 + CFO 三合一視角 - 結構：整體解讀 / 市場趨勢對位 / 月度走勢分析 / 品類結構 / 戰略級 SMART / 風險預警 - SMART 行動分三層：下期立即啟動（30 天）/ 下期戰略（60-90 天）/ 下下期預備（6-12 月） - max_tokens 2600，字數 1000-1300 Telegram 按鈕：報表選單加 4 顆季/半/年/TTM 按鈕 bump TEMPLATE_VERSIONS：quarterly / half_yearly / annual / ttm 全 v3.1.0 煙霧測試：4 種全綠，每份 12 頁 220KB。 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
6956 lines
321 KiB
Python
6956 lines
321 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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OpenClaw Telegram 群組智能助理 v5
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─────────────────────────────────────────
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核心功能：
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• 群組自然對話，Gemini Flash 主引擎（2~5s）
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• Inline Keyboard 15 個功能入口
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• 全商品查詢帶出商品ID
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• AI 分析強制比對內部DB + 外部MCP情報
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v5 新增（2026-04-16）：
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• 每日早報 08:30 / 晚報 21:00 / 週一週報
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• 異常偵測（4次/日，偏差>30%即告警）
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• 目標達成率（日/月目標設定）
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• 分類業績、同期比較（上週/上月）
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• 商品策略矩陣（加碼/機會/收割/觀察/持穩）
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• 商品健康分析（異常+策略分佈）
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• 趨勢圖 + 熱銷商品橫條圖（matplotlib）
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• 完整報表 PDF 下載（fpdf2 → reportlab → CSV）
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"""
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import os
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import json
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import re
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import threading
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from contextvars import ContextVar
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from contextlib import contextmanager
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import requests
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from datetime import datetime, timezone, timedelta
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from flask import Blueprint, request, jsonify
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from sqlalchemy import text, or_
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from database.manager import DatabaseManager
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from services.logger_manager import SystemLogger
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from services.telegram_update_guard import is_duplicate_update as is_global_duplicate_update
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from services.mcp_context_service import (
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build_mcp_context, MCPRouter, query_mcp,
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get_tw_media_news, get_ecommerce_news, get_taiwan_trends,
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get_dcard_trends, get_youtube_trending, get_taiwan_weather,
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get_twbank_exchange_rates, get_upcoming_events,
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)
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from services.openclaw_bot.telegram_api import (
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_tg,
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answer_callback,
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edit_message_text,
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send_document,
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send_message,
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send_photo,
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send_typing,
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)
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from services.openclaw_bot.menu_keyboards import (
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_BACK,
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_SUBMENUS,
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_chunk_rows,
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_submenu_goals,
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_submenu_market,
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_submenu_sales,
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_submenu_trend,
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_row,
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configure_menu_keyboards,
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quick_menu_keyboard,
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main_menu_keyboard,
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)
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try:
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from services.openclaw_learning_service import (
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build_rag_context, store_conversation, store_insight,
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update_feedback, get_learning_stats,
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)
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_LEARNING_ENABLED = True
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except Exception as _le:
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_LEARNING_ENABLED = False
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sys_log = __import__('logging').getLogger('openclaw')
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sys_log.warning(f"[OCLearn] learning service unavailable: {_le}")
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try:
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from services.pchome_crawler import (
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compare_product as pchome_compare,
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batch_compare_top as pchome_batch,
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save_matches as pchome_save,
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fmt_compare_msg as pchome_fmt_compare,
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fmt_daily_report as pchome_fmt_report,
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search_pchome as pchome_search,
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ensure_tables as pchome_ensure_tables,
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)
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_PCHOME_AVAILABLE = True
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except ImportError:
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_PCHOME_AVAILABLE = False
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# AI 引擎：Gemini Flash（主，2~5秒）→ NIM（備援，45~90秒）
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GEMINI_API_KEY = os.getenv('GEMINI_API_KEY', '')
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GEMINI_BASE_URL = 'https://generativelanguage.googleapis.com/v1beta/models'
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GEMINI_MODEL = 'gemini-2.0-flash'
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PPT_CACHE_TTL_HOURS = max(1, int(os.getenv('OPENCLAW_PPT_CACHE_TTL_HOURS', '24')))
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TAIPEI_TZ = timezone(timedelta(hours=8))
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sys_log = SystemLogger("OpenClawBot").get_logger()
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openclaw_bot_bp = Blueprint('openclaw_bot', __name__)
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# ── Telegram retry 去重 (update_id 快取，最多保留 500 筆) ─────
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BOT_TOKEN = os.getenv('OPENCLAW_BOT_TOKEN') or os.getenv('TELEGRAM_BOT_TOKEN', '')
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BOT_API_URL = f"https://api.telegram.org/bot{BOT_TOKEN}"
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ALLOWED_GROUP = int(os.getenv('OPENCLAW_GROUP_ID', '-1003940688311'))
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MOMO_BASE_URL = os.getenv('MOMO_BASE_URL', 'https://mo.wooo.work')
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NVIDIA_API_KEY = os.getenv('NVIDIA_API_KEY', '')
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NVIDIA_BASE_URL = 'https://integrate.api.nvidia.com/v1'
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CHAT_MODEL = 'deepseek-ai/deepseek-v3.2'
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BOT_USERNAME = os.getenv('OPENCLAW_BOT_USERNAME') or os.getenv('TELEGRAM_BOT_USERNAME', '@OpenClawAwoooI_Bot')
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# ── 存取控制白名單 ─────────────────────────────────────────────
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# OPENCLAW_ALLOWED_USERS：逗號分隔的 Telegram user_id（整數）
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# 空字串 + OPENCLAW_ALLOW_PRIVATE_WITHOUT_WHITELIST=1 => 允許任何私訊（舊行為）
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# OPENCLAW_ALLOW_PRIVATE_WITHOUT_WHITELIST 未開啟時，空白名單會拒絕私訊
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# 例：'123456789,987654321'
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_allowed_users_raw = os.getenv('OPENCLAW_ALLOWED_USERS', '')
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ALLOWED_USERS: set = (
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{int(uid.strip()) for uid in _allowed_users_raw.split(',') if uid.strip().isdigit()}
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if _allowed_users_raw.strip() else set()
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)
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_ALLOW_PRIVATE_WITHOUT_WHITELIST = (
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os.getenv('OPENCLAW_ALLOW_PRIVATE_WITHOUT_WHITELIST', '1').strip().lower()
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in {'1', 'true', 'yes', 'on'}
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)
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# ADR-019 Phase 3: Feature-flagged agent dispatch
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# 預設 OFF；啟用步驟：
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# export OPENCLAW_AGENT_DISPATCH=1
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# export OPENCLAW_AGENT_DISPATCH_CMDS=sales,top,vendor (逗號分隔白名單)
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# 啟用後白名單內 cmd 改走 OpenClaw NL agent，agent 自決查資料/詢問用戶/答覆
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_OPENCLAW_AGENT_DISPATCH_ENABLED = (
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os.getenv('OPENCLAW_AGENT_DISPATCH', '0').strip().lower()
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in {'1', 'true', 'yes', 'on'}
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)
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_AGENT_DISPATCH_CMDS = {
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c.strip() for c in os.getenv('OPENCLAW_AGENT_DISPATCH_CMDS', '').split(',')
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if c.strip()
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}
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# ── fail-closed 統一授權檢查 ───────────────────────────────────
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# 規則（任一滿足即通過，否則一律拒絕）：
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# 1. group/supergroup 且 chat_id == ALLOWED_GROUP
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# 2. private 且 user_id ∈ ALLOWED_USERS（env 未設 → 空 set → 全拒）
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# channel / 未知 chat_type / 缺欄位 → 拒絕
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# 修補 C3：callback handler 原本只擋 group/supergroup 不匹配，private 完全放行；
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# message handler `if ALLOWED_USERS and ...` 空 set 時整段失效。
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def _is_authorized(chat_type: str, chat_id, user_id) -> bool:
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try:
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cid = int(chat_id) if chat_id is not None else None
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uid = int(user_id) if user_id is not None else None
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except (TypeError, ValueError):
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return False
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if chat_type in ('group', 'supergroup'):
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return cid == ALLOWED_GROUP
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if chat_type == 'private':
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if uid is None:
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return False
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if ALLOWED_USERS:
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return uid in ALLOWED_USERS
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return _ALLOW_PRIVATE_WITHOUT_WHITELIST
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return False
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# ── 速率限制（每用戶每分鐘最多 30 次 AI 呼叫）──────────────────
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import time as _time_mod
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_rate_tracker: dict = {} # {user_id: [timestamp, ...]}
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_RATE_LIMIT_PER_MIN = 30 # 每分鐘上限
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_RATE_WINDOW_SEC = 60
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# V-Fix：callback 期間覆寫 send_message 會跨 request 競態，全域鎖可避免重入干擾。
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_CALLBACK_SEND_LOCK = threading.Lock()
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_CMD_FROM_CALLBACK_CTX = ContextVar('openclaw_cmd_from_callback', default=False)
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# critic Medium-3：當前請求的 user_id ContextVar（webhook 入口設定，handle_cmd 讀取）
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# 用 ContextVar 而非改 handle_cmd 簽名 — 避免動到 30+ 處呼叫端。
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_CURRENT_USER_ID_CTX = ContextVar('openclaw_current_user_id', default=None)
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# 管理員白名單：可執行破壞性指令（/cache flush all / cleanup confirm 等）
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# OPENCLAW_ADMIN_USER_IDS 未設 → 退回 ALLOWED_USERS（保持向後兼容）
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# 建議部署時明確設定，避免群組內所有人都能動快取
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_admin_users_raw = os.getenv('OPENCLAW_ADMIN_USER_IDS', '')
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ADMIN_USER_IDS: set = (
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{int(uid.strip()) for uid in _admin_users_raw.split(',') if uid.strip().isdigit()}
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if _admin_users_raw.strip() else set()
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)
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def _is_admin(user_id) -> bool:
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"""判定 user_id 是否為管理員。
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優先用 ADMIN_USER_IDS（明確設定）；未設時退回 ALLOWED_USERS（兼容）。
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回傳 True 才允許執行破壞性指令。
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"""
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try:
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uid = int(user_id) if user_id is not None else None
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except (TypeError, ValueError):
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return False
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if uid is None:
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return False
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if ADMIN_USER_IDS:
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return uid in ADMIN_USER_IDS
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# 兼容：ADMIN 未設時，退回 ALLOWED_USERS（私訊白名單即視為 admin）
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return bool(ALLOWED_USERS) and uid in ALLOWED_USERS
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def _check_rate_limit(user_id: int) -> bool:
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"""回傳 True = 允許，False = 超過速率限制"""
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now = _time_mod.time()
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window = _rate_tracker.setdefault(user_id, [])
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# 清掉 60 秒以前的紀錄
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_rate_tracker[user_id] = [t for t in window if now - t < _RATE_WINDOW_SEC]
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if len(_rate_tracker[user_id]) >= _RATE_LIMIT_PER_MIN:
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return False
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_rate_tracker[user_id].append(now)
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return True
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def _is_duplicate_update(update_id) -> bool:
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"""Telegram webhook 可能重送同一 update_id；優先以 DB 導向去重，再回退本機快取。"""
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return is_global_duplicate_update(update_id, namespace="telegram_update")
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def _is_non_modified_error(response) -> bool:
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"""Telegram API 回覆若是 `message is not modified` 則視為可忽略。"""
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if not isinstance(response, dict):
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return False
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desc = (response.get("description") or "").lower()
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return "message is not modified" in desc
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def _is_non_editable_message_error(response) -> bool:
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"""V-Fix：若訊息已刪除、不可編輯或過舊，僅回報不再 fallback。"""
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if not isinstance(response, dict):
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return False
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desc = (response.get("description") or "").lower()
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markers = (
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"message to edit not found",
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"message can't be edited",
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"message_id_invalid",
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"message id is invalid",
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"message identifier is invalid",
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"message is too old to edit",
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"not found",
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"reply message not found",
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"message to delete not found",
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)
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return any(m in desc for m in markers)
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@contextmanager
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def _run_with_callback_cmd_context():
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"""在 callback 路徑處理指令時，暫時封鎖 NL agent dispatch。"""
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token = _CMD_FROM_CALLBACK_CTX.set(True)
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try:
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yield
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finally:
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_CMD_FROM_CALLBACK_CTX.reset(token)
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def _build_callback_dedupe_key(update_id, cq_id, message_id=None, data=None, chat_id=None, user_id=None) -> str:
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"""V-Fix：多維度組成 callback key，降低重複回報機率。"""
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key_parts = []
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if cq_id:
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key_parts.append(f"cbq:{cq_id}")
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elif update_id is not None:
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key_parts.append(f"uid:{update_id}")
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if chat_id is not None:
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key_parts.append(f"chat:{chat_id}")
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if user_id is not None:
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key_parts.append(f"user:{user_id}")
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if message_id is not None:
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key_parts.append(f"msg:{message_id}")
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if data:
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key_parts.append(f"data:{data}")
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if key_parts:
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return "cb:" + "|".join(key_parts)
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base = f"cb-query:{cq_id}"
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if message_id is not None:
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base += f":msg:{message_id}"
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if data:
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base += f":{data}"
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return base
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||
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def _should_fallback_send_message(edit_result) -> bool:
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"""V-Fix：判斷 editMessageText 失敗是否要改走 sendMessage。"""
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if not isinstance(edit_result, dict):
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return True
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if edit_result.get("ok"):
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return False
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if _is_non_modified_error(edit_result):
|
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return False
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if _is_non_editable_message_error(edit_result):
|
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return False
|
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return True
|
||
|
||
|
||
def _normalize_callback_data(data: str) -> str:
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"""V-Fix：兼容舊版 callback_data（例如 menu_main、menu_trend、await_date...）。"""
|
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if not data:
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return data
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data = data.strip()
|
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if data.startswith(("menu:", "cmd:", "await:")):
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return data
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if data.startswith("menu_"):
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||
key = data[5:]
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if key in _SUBMENUS:
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return f"menu:{key}"
|
||
elif data.startswith("await_"):
|
||
key = data[6:]
|
||
if key in _AWAIT_PROMPTS:
|
||
return f"await:{key}"
|
||
elif data.startswith("cmd_"):
|
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return f"cmd:{data[4:]}"
|
||
return data
|
||
|
||
# 群組內回應觸發（包含這些字才回應；若為空則全部回應）
|
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# 設為空 list = 所有訊息都回應
|
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TRIGGER_KEYWORDS = [] # 空 = 全部回應（小龍蝦是專用業務群組）
|
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|
||
|
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# ── 目標管理（記憶體，跨 session 用 DB 儲存）─────────────────────
|
||
_GOALS: dict = {} # {'daily','monthly','quarterly','half','yearly': float}
|
||
_scheduler = None
|
||
|
||
# ── 輸入等待狀態機（chat_id → pending action）────────────────────
|
||
_input_pending: dict = {} # {chat_id: {'action': str, 'label': str}}
|
||
|
||
# ── Excel 匯入暫存（chat_id → pending import info）────────────────
|
||
_excel_pending: dict = {} # {chat_id: {'file_path': str, 'filename': str, 'preview': str}}
|
||
|
||
|
||
# ── 中文字型搜尋 ──────────────────────────────────────────────
|
||
_CHINESE_FONT_PATHS = [
|
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'/usr/share/fonts/truetype/wqy/wqy-zenhei.ttc',
|
||
'/usr/share/fonts/truetype/wqy/wqy-microhei.ttc',
|
||
'/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc',
|
||
'/usr/share/fonts/truetype/noto/NotoSansCJK-Regular.ttc',
|
||
'/usr/share/fonts/noto-cjk/NotoSansCJK-Regular.ttc',
|
||
'/tmp/ocbot_chinese_font.ttf',
|
||
]
|
||
_FONT_DL_URL = (
|
||
'https://github.com/googlefonts/noto-cjk/raw/main/'
|
||
'Sans/SubsetOTF/SC/NotoSansSC-Regular.otf'
|
||
)
|
||
|
||
|
||
def _get_chinese_font() -> str:
|
||
"""取得中文字型路徑（找不到就嘗試下載），回傳路徑或空字串"""
|
||
for p in _CHINESE_FONT_PATHS:
|
||
if os.path.exists(p):
|
||
return p
|
||
# 嘗試下載 Noto Sans SC（約 3MB）
|
||
cache = '/tmp/ocbot_chinese_font.ttf'
|
||
try:
|
||
r = requests.get(_FONT_DL_URL, timeout=20)
|
||
if r.ok:
|
||
with open(cache, 'wb') as f:
|
||
f.write(r.content)
|
||
return cache
|
||
except Exception:
|
||
pass
|
||
return ''
|
||
|
||
|
||
# ── 報表產生：Excel（主）→ CSV（備援）────────────────────────────
|
||
def generate_daily_pdf(date_str: str) -> str:
|
||
"""
|
||
產生日報：優先用 openpyxl 生成 .xlsx（支援中文、可直接在 Excel/Numbers 開啟）。
|
||
openpyxl 未安裝時 fallback CSV。
|
||
"""
|
||
import tempfile
|
||
|
||
sales = query_sales(date_str)
|
||
products = query_top_products(date_str, 20)
|
||
vendors = query_top_vendors(date_str, 10)
|
||
weekly = query_weekly_trend()
|
||
now_str = datetime.now(TAIPEI_TZ).strftime('%Y-%m-%d %H:%M')
|
||
safe_date = date_str.replace('/', '-')
|
||
|
||
# ── openpyxl Excel（主要，支援中文，秒產生）─────────────────
|
||
try:
|
||
from openpyxl import Workbook
|
||
from openpyxl.styles import (Font, PatternFill, Alignment,
|
||
Border, Side, numbers)
|
||
from openpyxl.utils import get_column_letter
|
||
|
||
wb = Workbook()
|
||
HDR = Font(bold=True, color='FFFFFF', size=11)
|
||
HDR_FILL = PatternFill('solid', fgColor='1565C0') # 深藍
|
||
SUB_FILL = PatternFill('solid', fgColor='E3F2FD') # 淡藍
|
||
ORG_FILL = PatternFill('solid', fgColor='FFF8E1') # 淡黃
|
||
thin = Side(style='thin', color='BDBDBD')
|
||
border = Border(left=thin, right=thin, top=thin, bottom=thin)
|
||
center = Alignment(horizontal='center', vertical='center')
|
||
right = Alignment(horizontal='right', vertical='center')
|
||
|
||
def _hdr(ws, row, cols, fills=HDR_FILL):
|
||
for c in cols:
|
||
ws.cell(row, c).font = HDR
|
||
ws.cell(row, c).fill = fills
|
||
ws.cell(row, c).alignment = center
|
||
|
||
def _border_row(ws, row, max_col):
|
||
for c in range(1, max_col + 1):
|
||
ws.cell(row, c).border = border
|
||
|
||
# ════ Sheet 1：業績摘要 ════
|
||
ws1 = wb.active
|
||
ws1.title = '業績摘要'
|
||
ws1.column_dimensions['A'].width = 18
|
||
ws1.column_dimensions['B'].width = 22
|
||
|
||
ws1.merge_cells('A1:B1')
|
||
ws1['A1'] = f'📊 業績報表 — {date_str}'
|
||
ws1['A1'].font = Font(bold=True, size=14, color='1565C0')
|
||
ws1['A1'].alignment = center
|
||
|
||
ws1.merge_cells('A2:B2')
|
||
ws1['A2'] = f'產生時間：{now_str} (UTC+8) | OpenClaw AI'
|
||
ws1['A2'].font = Font(size=9, color='757575')
|
||
ws1['A2'].alignment = center
|
||
|
||
r = 4
|
||
if sales.get('found'):
|
||
headers = ['項目', '數值']
|
||
for i, h in enumerate(headers, 1):
|
||
ws1.cell(r, i, h)
|
||
_hdr(ws1, r, [1, 2])
|
||
_border_row(ws1, r, 2)
|
||
r += 1
|
||
|
||
rows_data = [
|
||
('💰 總業績', f"NT$ {float(sales.get('revenue', 0)):,.0f}"),
|
||
('📦 訂單數', f"{sales.get('orders', '-')} 筆"),
|
||
('🛒 客單價', f"NT$ {sales.get('avg_order', 0):,.0f}"),
|
||
('📈 毛利率', f"{sales.get('gross_margin', 0):.1f}%"),
|
||
('🛍️ 商品數', f"{sales.get('products', '-')} 件"),
|
||
]
|
||
for i, (k, v) in enumerate(rows_data):
|
||
ws1.cell(r, 1, k)
|
||
ws1.cell(r, 2, v)
|
||
fill = SUB_FILL if i % 2 == 0 else None
|
||
if fill:
|
||
ws1.cell(r, 1).fill = fill
|
||
ws1.cell(r, 2).fill = fill
|
||
ws1.cell(r, 2).alignment = right
|
||
_border_row(ws1, r, 2)
|
||
r += 1
|
||
|
||
# ── 近7日趨勢 ──
|
||
if weekly:
|
||
r += 1
|
||
ws1.cell(r, 1, '📅 近7日業績趨勢').font = Font(bold=True, size=11, color='1565C0')
|
||
r += 1
|
||
for h, hdr in enumerate(['日期', '業績', '環比'], 1):
|
||
ws1.cell(r, h, hdr)
|
||
_hdr(ws1, r, [1, 2, 3])
|
||
_border_row(ws1, r, 3)
|
||
r += 1
|
||
for idx, w in enumerate(weekly):
|
||
prev = weekly[idx - 1]['revenue'] if idx > 0 else None
|
||
rev = w['revenue']
|
||
pct = f"{'▲' if rev >= prev else '▼'}{abs((rev - prev) / prev * 100):.1f}%" \
|
||
if prev else '—'
|
||
ws1.cell(r, 1, w['date'])
|
||
ws1.cell(r, 2, f"NT$ {rev:,.0f}")
|
||
ws1.cell(r, 3, pct)
|
||
ws1.cell(r, 2).alignment = right
|
||
ws1.cell(r, 3).alignment = center
|
||
if idx % 2 == 0:
|
||
for c in range(1, 4):
|
||
ws1.cell(r, c).fill = ORG_FILL
|
||
_border_row(ws1, r, 3)
|
||
r += 1
|
||
ws1.column_dimensions['C'].width = 10
|
||
|
||
# ════ Sheet 2：熱銷商品 ════
|
||
if products:
|
||
ws2 = wb.create_sheet('熱銷商品 TOP20')
|
||
cols = ['排名', '商品ID', '商品名稱', '業績 (NT$)', '數量 (件)', '佔比 %']
|
||
widths = [6, 28, 30, 16, 10, 10]
|
||
for i, (h, w) in enumerate(zip(cols, widths), 1):
|
||
ws2.cell(1, i, h)
|
||
ws2.column_dimensions[get_column_letter(i)].width = w
|
||
_hdr(ws2, 1, list(range(1, len(cols) + 1)))
|
||
_border_row(ws2, 1, len(cols))
|
||
|
||
total_rev = sum(p['revenue'] for p in products)
|
||
for idx, p in enumerate(products, 1):
|
||
pct = p['revenue'] / total_rev * 100 if total_rev else 0
|
||
row_data = [idx, p.get('id', ''), p['name'],
|
||
p['revenue'], p['qty'], round(pct, 2)]
|
||
for c, val in enumerate(row_data, 1):
|
||
ws2.cell(idx + 1, c, val)
|
||
ws2.cell(idx + 1, c).border = border
|
||
ws2.cell(idx + 1, 4).number_format = '#,##0'
|
||
ws2.cell(idx + 1, 5).number_format = '#,##0'
|
||
ws2.cell(idx + 1, 6).number_format = '0.00"%"'
|
||
ws2.cell(idx + 1, 4).alignment = right
|
||
ws2.cell(idx + 1, 5).alignment = right
|
||
ws2.cell(idx + 1, 6).alignment = right
|
||
if idx % 2 == 0:
|
||
for c in range(1, len(cols) + 1):
|
||
ws2.cell(idx + 1, c).fill = SUB_FILL
|
||
|
||
# ════ Sheet 3：熱銷廠商 ════
|
||
if vendors:
|
||
ws3 = wb.create_sheet('熱銷廠商 TOP10')
|
||
vcols = ['排名', '廠商名稱', '業績 (NT$)', '佔比 %']
|
||
vwidths = [6, 32, 16, 10]
|
||
for i, (h, w) in enumerate(zip(vcols, vwidths), 1):
|
||
ws3.cell(1, i, h)
|
||
ws3.column_dimensions[get_column_letter(i)].width = w
|
||
_hdr(ws3, 1, list(range(1, len(vcols) + 1)))
|
||
_border_row(ws3, 1, len(vcols))
|
||
|
||
total_vrev = sum(v['revenue'] for v in vendors)
|
||
for idx, v in enumerate(vendors, 1):
|
||
pct = v['revenue'] / total_vrev * 100 if total_vrev else 0
|
||
for c, val in enumerate([idx, v['name'], v['revenue'], round(pct, 2)], 1):
|
||
ws3.cell(idx + 1, c, val)
|
||
ws3.cell(idx + 1, c).border = border
|
||
ws3.cell(idx + 1, 3).number_format = '#,##0'
|
||
ws3.cell(idx + 1, 4).number_format = '0.00"%"'
|
||
ws3.cell(idx + 1, 3).alignment = right
|
||
ws3.cell(idx + 1, 4).alignment = right
|
||
if idx % 2 == 0:
|
||
for c in range(1, len(vcols) + 1):
|
||
ws3.cell(idx + 1, c).fill = ORG_FILL
|
||
|
||
# ════ Sheet 4：分類業績 ════
|
||
try:
|
||
cats = query_category_sales(date_str, lim=15)
|
||
if cats:
|
||
ws4 = wb.create_sheet('分類業績 TOP15')
|
||
ccols = ['排名', '分類名稱', '業績 (NT$)', '訂單數', '佔比 %']
|
||
cwidths = [6, 28, 16, 10, 10]
|
||
for i, (h, w) in enumerate(zip(ccols, cwidths), 1):
|
||
ws4.cell(1, i, h)
|
||
ws4.column_dimensions[get_column_letter(i)].width = w
|
||
_hdr(ws4, 1, list(range(1, len(ccols) + 1)))
|
||
_border_row(ws4, 1, len(ccols))
|
||
total_crev = sum(c.get('revenue', 0) for c in cats)
|
||
for idx, c in enumerate(cats, 1):
|
||
pct = c.get('revenue', 0) / total_crev * 100 if total_crev else 0
|
||
for col, val in enumerate([idx, c.get('cat', c.get('category','')),
|
||
c.get('revenue',0), c.get('qty', c.get('orders',0)),
|
||
round(pct,2)], 1):
|
||
ws4.cell(idx+1, col, val)
|
||
ws4.cell(idx+1, col).border = border
|
||
ws4.cell(idx+1, 3).number_format = '#,##0'
|
||
ws4.cell(idx+1, 5).number_format = '0.00"%"'
|
||
if idx % 2 == 0:
|
||
for col in range(1, len(ccols)+1):
|
||
ws4.cell(idx+1, col).fill = SUB_FILL
|
||
except Exception as _e:
|
||
sys_log.warning(f"[Excel] 分類業績 sheet 失敗: {_e}")
|
||
|
||
# ════ Sheet 5：同期比較 ════
|
||
try:
|
||
cmp = query_comparison(date_str)
|
||
if cmp:
|
||
ws5 = wb.create_sheet('同期比較')
|
||
ws5.column_dimensions['A'].width = 18
|
||
ws5.column_dimensions['B'].width = 20
|
||
ws5.column_dimensions['C'].width = 20
|
||
ws5.column_dimensions['D'].width = 12
|
||
ws5.cell(1, 1, '指標'); ws5.cell(1, 2, '本期'); ws5.cell(1, 3, '對比期'); ws5.cell(1, 4, '增減 %')
|
||
_hdr(ws5, 1, [1,2,3,4])
|
||
_border_row(ws5, 1, 4)
|
||
periods = ['today', 'yesterday', 'last_week', 'last_month']
|
||
labels = ['今日', '昨日', '上週同日', '上月同日']
|
||
metrics = [('業績 (NT$)', 'revenue'), ('訂單數', 'orders'),
|
||
('客單價 (NT$)', 'avg_order'), ('毛利率 (%)', 'gross_margin')]
|
||
base = cmp.get('today', {})
|
||
r = 2
|
||
for period, label in zip(periods[1:], labels[1:]):
|
||
comp = cmp.get(period, {})
|
||
if not comp:
|
||
continue
|
||
ws5.cell(r, 1, f'vs {label}').font = Font(bold=True, color='1565C0')
|
||
ws5.cell(r, 1).fill = PatternFill('solid', fgColor='E3F2FD')
|
||
_border_row(ws5, r, 4)
|
||
r += 1
|
||
for mlabel, mkey in metrics:
|
||
bv = float(base.get(mkey, 0) or 0)
|
||
cv = float(comp.get(mkey, 0) or 0)
|
||
pct = f"{'▲' if bv >= cv else '▼'}{abs((bv-cv)/cv*100):.1f}%" if cv else '—'
|
||
ws5.cell(r, 1, mlabel); ws5.cell(r, 2, f'{bv:,.1f}')
|
||
ws5.cell(r, 3, f'{cv:,.1f}'); ws5.cell(r, 4, pct)
|
||
for col in range(1,5): ws5.cell(r,col).border = border
|
||
ws5.cell(r,4).alignment = center
|
||
r += 1
|
||
except Exception as _e:
|
||
sys_log.warning(f"[Excel] 同期比較 sheet 失敗: {_e}")
|
||
|
||
# ════ Sheet 6：目標達成率 ════
|
||
try:
|
||
goal = get_goal_status(date_str)
|
||
if goal:
|
||
ws6 = wb.create_sheet('目標達成率')
|
||
ws6.column_dimensions['A'].width = 16
|
||
ws6.column_dimensions['B'].width = 20
|
||
ws6.column_dimensions['C'].width = 20
|
||
ws6.column_dimensions['D'].width = 14
|
||
ws6.cell(1, 1, '週期'); ws6.cell(1, 2, '目標 (NT$)'); ws6.cell(1, 3, '實際 (NT$)'); ws6.cell(1, 4, '達成率')
|
||
_hdr(ws6, 1, [1,2,3,4])
|
||
_border_row(ws6, 1, 4)
|
||
r = 2
|
||
for period, label in [('daily','日'), ('monthly','月'), ('quarterly','季'),
|
||
('half','半年'), ('yearly','年')]:
|
||
tgt = float(goal.get(f'{period}_target', 0) or 0)
|
||
act = float(goal.get(f'{period}_actual', 0) or 0)
|
||
if tgt <= 0: continue
|
||
rate = act / tgt * 100
|
||
ws6.cell(r,1,label); ws6.cell(r,2,tgt); ws6.cell(r,3,act)
|
||
ws6.cell(r,4,f'{rate:.1f}%')
|
||
ws6.cell(r,2).number_format = '#,##0'
|
||
ws6.cell(r,3).number_format = '#,##0'
|
||
color = '1B5E20' if rate >= 100 else ('E65100' if rate < 70 else '1565C0')
|
||
ws6.cell(r,4).font = Font(bold=True, color=color)
|
||
for col in range(1,5): ws6.cell(r,col).border = border
|
||
r += 1
|
||
except Exception as _e:
|
||
sys_log.warning(f"[Excel] 目標達成率 sheet 失敗: {_e}")
|
||
|
||
tmp = tempfile.NamedTemporaryFile(
|
||
suffix=f'_{safe_date}.xlsx', prefix='momo_report_', delete=False, dir='/tmp'
|
||
)
|
||
wb.save(tmp.name)
|
||
sys_log.info(f"[OpenClawBot] Excel report generated: {tmp.name}")
|
||
return tmp.name
|
||
|
||
except ImportError:
|
||
sys_log.warning("[OpenClawBot] openpyxl 未安裝，fallback CSV")
|
||
except Exception as e:
|
||
sys_log.error(f"[OpenClawBot] openpyxl error: {e}")
|
||
|
||
# ── Fallback：CSV ──────────────────────────────────────────
|
||
try:
|
||
import csv
|
||
tmp = tempfile.NamedTemporaryFile(
|
||
suffix=f'_{safe_date}.csv', prefix='momo_report_', delete=False,
|
||
dir='/tmp', mode='w', encoding='utf-8-sig', newline=''
|
||
)
|
||
writer = csv.writer(tmp)
|
||
writer.writerow(['業績報表', date_str, f'產生時間：{now_str}'])
|
||
writer.writerow([])
|
||
if sales.get('found'):
|
||
writer.writerow(['=== 業績摘要 ==='])
|
||
writer.writerow(['總業績', float(sales.get('revenue', 0))])
|
||
writer.writerow(['訂單數', sales.get('orders', '-')])
|
||
writer.writerow(['商品數', sales.get('products', '-')])
|
||
writer.writerow([])
|
||
if products:
|
||
writer.writerow(['=== 熱銷商品 TOP20 ==='])
|
||
writer.writerow(['排名', '商品ID', '商品名稱', '業績', '數量'])
|
||
for i, p in enumerate(products, 1):
|
||
writer.writerow([i, p.get('id', ''), p['name'],
|
||
p['revenue'], p['qty']])
|
||
writer.writerow([])
|
||
if vendors:
|
||
writer.writerow(['=== 熱銷廠商 TOP10 ==='])
|
||
writer.writerow(['排名', '廠商名稱', '業績'])
|
||
for i, v in enumerate(vendors, 1):
|
||
writer.writerow([i, v['name'], v['revenue']])
|
||
tmp.close()
|
||
sys_log.info(f"[OpenClawBot] CSV fallback generated: {tmp.name}")
|
||
return tmp.name
|
||
except Exception as e:
|
||
sys_log.error(f"[OpenClawBot] CSV fallback error: {e}")
|
||
return ''
|
||
|
||
|
||
# ══════════════════════════════════════════════════════════════
|
||
# v5 — 進階業績智能功能
|
||
# ══════════════════════════════════════════════════════════════
|
||
|
||
# ── 新增 DB 查詢 ──────────────────────────────────────────────
|
||
|
||
def query_category_sales(date_str, lim=10):
|
||
"""按商品分類查業績（優先用 商品分類L1，fallback 小分類）"""
|
||
d = normalize_date(date_str)
|
||
for col in ('"商品分類L1"', '"商品分類L2"', '"小分類"', '"商品分類"', '"類別"', '"category"'):
|
||
try:
|
||
with _db().connect() as c:
|
||
rows = c.execute(text(f"""
|
||
SELECT COALESCE({col}, '未分類') as cat,
|
||
COUNT(DISTINCT "商品ID") as products,
|
||
SUM(CAST("總業績" AS FLOAT)) as revenue,
|
||
SUM(CAST("數量" AS INTEGER)) as qty
|
||
FROM realtime_sales_monthly WHERE "日期"=:d
|
||
GROUP BY cat ORDER BY revenue DESC LIMIT :lim
|
||
"""), {'d': d, 'lim': lim}).fetchall()
|
||
if rows:
|
||
return [{'cat': r[0], 'products': r[1], 'revenue': r[2], 'qty': r[3]}
|
||
for r in rows]
|
||
except Exception:
|
||
continue
|
||
return []
|
||
|
||
|
||
def query_category_monthly(year: int, month: int, lim: int = 10) -> list:
|
||
"""按月份查分類業績（用 LIKE YYYY/MM/%）"""
|
||
prefix = f"{year}/{month:02d}/%"
|
||
for col in ('"商品分類L1"', '"商品分類L2"', '"小分類"', '"商品分類"'):
|
||
try:
|
||
with _db().connect() as c:
|
||
rows = c.execute(text(f"""
|
||
SELECT COALESCE({col}, '未分類') as cat,
|
||
COUNT(DISTINCT "商品ID") as products,
|
||
SUM(CAST("總業績" AS FLOAT)) as revenue,
|
||
SUM(CAST("數量" AS INTEGER)) as qty
|
||
FROM realtime_sales_monthly WHERE "日期" LIKE :prefix
|
||
GROUP BY cat ORDER BY revenue DESC LIMIT :lim
|
||
"""), {'prefix': prefix, 'lim': lim}).fetchall()
|
||
if rows:
|
||
return [{'cat': r[0], 'products': int(r[1]), 'revenue': float(r[2]), 'qty': int(r[3])}
|
||
for r in rows]
|
||
except Exception:
|
||
continue
|
||
return []
|
||
|
||
|
||
def query_comparison(date_str):
|
||
"""今日 vs 上週同日 vs 上月同日"""
|
||
from datetime import datetime as dt
|
||
try:
|
||
d = dt.strptime(normalize_date(date_str).replace('/', '-'), '%Y-%m-%d').date()
|
||
lw_str = (d - timedelta(days=7)).strftime('%Y/%m/%d')
|
||
lm_str = (d - timedelta(days=30)).strftime('%Y/%m/%d')
|
||
|
||
def _fetch(day_s):
|
||
try:
|
||
with _db().connect() as c:
|
||
row = c.execute(text("""
|
||
SELECT SUM(CAST("總業績" AS FLOAT)),
|
||
COUNT(DISTINCT "商品ID")
|
||
FROM realtime_sales_monthly WHERE "日期"=:d
|
||
"""), {'d': day_s}).fetchone()
|
||
if row and row[0]:
|
||
return {'date': day_s, 'revenue': float(row[0]), 'products': row[1]}
|
||
except Exception:
|
||
pass
|
||
return {'date': day_s, 'revenue': 0, 'products': 0}
|
||
|
||
return {
|
||
'today': _fetch(normalize_date(date_str)),
|
||
'last_week': _fetch(lw_str),
|
||
'last_month': _fetch(lm_str),
|
||
}
|
||
except Exception as e:
|
||
sys_log.error(f"[OpenClawBot] query_comparison: {e}")
|
||
return None
|
||
|
||
|
||
def query_daily_history(days=14):
|
||
"""取得近N天日業績（用於趨勢圖）"""
|
||
try:
|
||
with _db().connect() as c:
|
||
rows = c.execute(text(f"""
|
||
SELECT "日期", SUM(CAST("總業績" AS FLOAT)) as rev
|
||
FROM realtime_sales_monthly
|
||
WHERE CAST("日期" AS DATE) >= CURRENT_DATE - INTERVAL '{int(days)} days'
|
||
GROUP BY "日期" ORDER BY "日期" ASC
|
||
""")).fetchall()
|
||
return [{'date': r[0], 'revenue': float(r[1])} for r in rows if r[1]]
|
||
except Exception as e:
|
||
sys_log.error(f"[OpenClawBot] query_daily_history: {e}")
|
||
return []
|
||
|
||
|
||
def query_restock_forecast(top_n: int = 20) -> list:
|
||
"""基於近7日銷售速度的補貨預測（高速+加速商品優先）"""
|
||
try:
|
||
with _db().connect() as c:
|
||
rows = c.execute(text("""
|
||
WITH recent AS (
|
||
SELECT "商品ID", "商品名稱",
|
||
SUM(CAST("數量" AS INTEGER)) AS qty7,
|
||
SUM(CAST("總業績" AS FLOAT)) AS rev7,
|
||
COUNT(DISTINCT "日期") AS active_days
|
||
FROM realtime_sales_monthly
|
||
WHERE CAST("日期" AS DATE) >= CURRENT_DATE - INTERVAL '7 days'
|
||
GROUP BY "商品ID", "商品名稱"
|
||
),
|
||
older AS (
|
||
SELECT "商品ID",
|
||
COALESCE(SUM(CAST("數量" AS INTEGER)), 0) AS qty_prev7
|
||
FROM realtime_sales_monthly
|
||
WHERE CAST("日期" AS DATE) BETWEEN
|
||
CURRENT_DATE - INTERVAL '14 days' AND
|
||
CURRENT_DATE - INTERVAL '8 days'
|
||
GROUP BY "商品ID"
|
||
)
|
||
SELECT r."商品ID", r."商品名稱",
|
||
r.qty7, r.active_days, r.rev7,
|
||
COALESCE(o.qty_prev7, 0) AS qty_prev7
|
||
FROM recent r
|
||
LEFT JOIN older o ON r."商品ID" = o."商品ID"
|
||
WHERE r.active_days >= 3 AND r.qty7 > 0
|
||
ORDER BY r.qty7 DESC
|
||
LIMIT :n
|
||
"""), {'n': top_n}).fetchall()
|
||
result = []
|
||
for row in rows:
|
||
pid, name, qty7, active, rev7, qty_prev7 = row
|
||
daily_vel = qty7 / 7
|
||
prev_vel = qty_prev7 / 7 if qty_prev7 else daily_vel
|
||
accel = (daily_vel - prev_vel) / prev_vel * 100 if prev_vel > 0 else 0
|
||
urgency = ('HIGH' if daily_vel >= 5 and accel >= 15
|
||
else 'MED' if daily_vel >= 2 or accel >= 30
|
||
else 'LOW')
|
||
result.append({
|
||
'id': pid, 'name': name,
|
||
'daily_vel': round(daily_vel, 1),
|
||
'qty7': int(qty7), 'rev7': rev7,
|
||
'accel': round(accel, 1),
|
||
'urgency': urgency,
|
||
})
|
||
return result
|
||
except Exception as e:
|
||
sys_log.error(f"[restock] {e}")
|
||
return []
|
||
|
||
|
||
def query_category_detail(category: str, date_str: str = '', limit: int = 10) -> list:
|
||
"""查詢指定L1分類的L2細項業績"""
|
||
try:
|
||
# 決定查詢範圍：有日期查單日，否則查近7天
|
||
if date_str:
|
||
d = normalize_date(date_str)
|
||
where_clause = '"日期" = :d'
|
||
params = {'d': d, 'lim': limit, 'cat': category}
|
||
else:
|
||
where_clause = 'CAST("日期" AS DATE) >= CURRENT_DATE - INTERVAL \'7 days\''
|
||
params = {'lim': limit, 'cat': category}
|
||
|
||
for l2_col in ('"商品分類L2"', '"小分類"', '"商品分類"'):
|
||
try:
|
||
with _db().connect() as c:
|
||
rows = c.execute(text(f"""
|
||
SELECT COALESCE({l2_col}, '其他') AS sub_cat,
|
||
COUNT(DISTINCT "商品ID") AS products,
|
||
SUM(CAST("總業績" AS FLOAT)) AS revenue,
|
||
SUM(CAST("數量" AS INTEGER)) AS qty
|
||
FROM realtime_sales_monthly
|
||
WHERE "商品分類L1" = :cat AND {where_clause}
|
||
GROUP BY sub_cat ORDER BY revenue DESC LIMIT :lim
|
||
"""), params).fetchall()
|
||
if rows:
|
||
return [{'cat': r[0], 'products': r[1], 'revenue': float(r[2]), 'qty': int(r[3])}
|
||
for r in rows]
|
||
except Exception:
|
||
continue
|
||
return []
|
||
except Exception as e:
|
||
sys_log.error(f"[category_detail] {e}")
|
||
return []
|
||
|
||
|
||
def query_promo_comparison(start_str: str, end_str: str) -> dict:
|
||
"""查詢促銷期間 vs 前同天數業績比較"""
|
||
try:
|
||
from datetime import datetime as _dt
|
||
start = _dt.strptime(start_str.replace('/', '-'), '%Y-%m-%d').date()
|
||
end = _dt.strptime(end_str.replace('/', '-'), '%Y-%m-%d').date()
|
||
days = (end - start).days + 1
|
||
# 前期：同等天數
|
||
from datetime import timedelta as _td
|
||
pre_end = start - _td(days=1)
|
||
pre_start = pre_end - _td(days=days - 1)
|
||
|
||
def _fetch_period(s, e):
|
||
try:
|
||
with _db().connect() as c:
|
||
row = c.execute(text("""
|
||
SELECT COUNT(DISTINCT "訂單編號"),
|
||
COALESCE(SUM(CAST("總業績" AS FLOAT)), 0),
|
||
COALESCE(SUM(CAST("總成本" AS FLOAT)), 0),
|
||
COUNT(DISTINCT "商品ID")
|
||
FROM realtime_sales_monthly
|
||
WHERE CAST("日期" AS DATE) BETWEEN CAST(:s AS DATE) AND CAST(:e AS DATE)
|
||
"""), {'s': str(s), 'e': str(e)}).fetchone()
|
||
tops = c.execute(text("""
|
||
SELECT "商品名稱", SUM(CAST("總業績" AS FLOAT)) AS rev
|
||
FROM realtime_sales_monthly
|
||
WHERE CAST("日期" AS DATE) BETWEEN CAST(:s AS DATE) AND CAST(:e AS DATE)
|
||
GROUP BY "商品名稱" ORDER BY rev DESC LIMIT 5
|
||
"""), {'s': str(s), 'e': str(e)}).fetchall()
|
||
orders, rev, cost, prods = row
|
||
margin = (rev - cost) / rev * 100 if rev else 0
|
||
return {
|
||
'start': str(s), 'end': str(e), 'days': days,
|
||
'orders': int(orders or 0), 'revenue': float(rev),
|
||
'margin': round(margin, 1), 'products': int(prods or 0),
|
||
'top_products': [{'name': r[0], 'revenue': float(r[1])} for r in tops],
|
||
}
|
||
except Exception as ex:
|
||
sys_log.error(f"[promo_cmp] {ex}")
|
||
return {'start': str(s), 'end': str(e), 'days': days,
|
||
'orders': 0, 'revenue': 0, 'margin': 0, 'products': 0, 'top_products': []}
|
||
|
||
promo = _fetch_period(start, end)
|
||
pre = _fetch_period(pre_start, pre_end)
|
||
rev_lift = (promo['revenue'] - pre['revenue']) / pre['revenue'] * 100 if pre['revenue'] else 0
|
||
ord_lift = (promo['orders'] - pre['orders']) / pre['orders'] * 100 if pre['orders'] else 0
|
||
return {
|
||
'promo': promo, 'pre': pre,
|
||
'rev_lift': round(rev_lift, 1),
|
||
'ord_lift': round(ord_lift, 1),
|
||
}
|
||
except Exception as e:
|
||
sys_log.error(f"[promo_comparison] {e}")
|
||
return {}
|
||
|
||
|
||
def query_anomalies(date_str):
|
||
"""偵測當日業績異常商品（vs 7日均值，偏差>30%）"""
|
||
d = normalize_date(date_str)
|
||
try:
|
||
with _db().connect() as c:
|
||
rows = c.execute(text("""
|
||
WITH today AS (
|
||
SELECT "商品ID", "商品名稱",
|
||
SUM(CAST("總業績" AS FLOAT)) AS today_rev
|
||
FROM realtime_sales_monthly WHERE "日期"=:d
|
||
GROUP BY "商品ID", "商品名稱"
|
||
),
|
||
avg7 AS (
|
||
SELECT "商品ID", AVG(day_rev) AS avg_rev
|
||
FROM (
|
||
SELECT "商品ID", "日期",
|
||
SUM(CAST("總業績" AS FLOAT)) AS day_rev
|
||
FROM realtime_sales_monthly
|
||
WHERE CAST("日期" AS DATE)
|
||
BETWEEN CAST(:d AS DATE) - INTERVAL '7 days'
|
||
AND CAST(:d AS DATE) - INTERVAL '1 day'
|
||
GROUP BY "商品ID", "日期"
|
||
) sub
|
||
GROUP BY "商品ID"
|
||
)
|
||
SELECT t."商品ID", t."商品名稱", t.today_rev, a.avg_rev,
|
||
(t.today_rev - a.avg_rev) / NULLIF(a.avg_rev, 0) * 100 AS pct
|
||
FROM today t
|
||
JOIN avg7 a ON t."商品ID" = a."商品ID"
|
||
WHERE ABS((t.today_rev - a.avg_rev) / NULLIF(a.avg_rev, 0)) > 0.3
|
||
ORDER BY ABS((t.today_rev - a.avg_rev) / NULLIF(a.avg_rev, 0)) DESC
|
||
LIMIT 10
|
||
"""), {'d': d}).fetchall()
|
||
return [{'id': r[0], 'name': r[1], 'today': r[2], 'avg7': r[3], 'pct': r[4]}
|
||
for r in rows]
|
||
except Exception as e:
|
||
sys_log.error(f"[OpenClawBot] query_anomalies: {e}")
|
||
return []
|
||
|
||
|
||
# ── 目標管理 ──────────────────────────────────────────────────
|
||
|
||
def get_goal_status(date_str: str) -> dict:
|
||
d = normalize_date(date_str)
|
||
sales = query_sales(date_str)
|
||
today_rev = float(sales.get('revenue', 0)) if sales.get('found') else 0.0
|
||
|
||
# 月業績
|
||
try:
|
||
month_prefix = d[:7] # e.g. '2026/04'
|
||
with _db().connect() as c:
|
||
row = c.execute(text("""
|
||
SELECT SUM(CAST("總業績" AS FLOAT))
|
||
FROM realtime_sales_monthly WHERE "日期" LIKE :prefix
|
||
"""), {'prefix': f"{month_prefix}%"}).fetchone()
|
||
month_rev = float(row[0]) if row and row[0] else 0.0
|
||
except Exception:
|
||
month_rev = 0.0
|
||
|
||
daily_goal = _GOALS.get('daily', 0)
|
||
monthly_goal = _GOALS.get('monthly', 0)
|
||
quarterly_goal = _GOALS.get('quarterly', 0)
|
||
half_goal = _GOALS.get('half', 0)
|
||
yearly_goal = _GOALS.get('yearly', 0)
|
||
# P9 — 週目標自動推算：若未手動設定，從月目標 ÷ 4.3 推算
|
||
weekly_goal = _GOALS.get('weekly', 0)
|
||
if not weekly_goal and monthly_goal:
|
||
weekly_goal = round(monthly_goal / 4.3)
|
||
|
||
# 季/半年/年業績
|
||
year_s = d[:4]
|
||
try:
|
||
with _db().connect() as c:
|
||
row_y = c.execute(text("""
|
||
SELECT SUM(CAST("總業績" AS FLOAT))
|
||
FROM realtime_sales_monthly WHERE "日期" LIKE :y
|
||
"""), {'y': f"{year_s}/%"}).fetchone()
|
||
year_rev = float(row_y[0]) if row_y and row_y[0] else 0.0
|
||
except Exception:
|
||
year_rev = 0.0
|
||
|
||
# 近7日業績（週目標達成率用）
|
||
try:
|
||
weekly_rows = query_weekly_trend()
|
||
week_rev = sum(w['revenue'] for w in weekly_rows) if weekly_rows else 0.0
|
||
except Exception:
|
||
week_rev = 0.0
|
||
|
||
return {
|
||
'date': d,
|
||
'today_rev': today_rev,
|
||
'daily_goal': daily_goal,
|
||
'daily_pct': today_rev / daily_goal * 100 if daily_goal > 0 else None,
|
||
'week_rev': week_rev,
|
||
'weekly_goal': weekly_goal,
|
||
'weekly_pct': week_rev / weekly_goal * 100 if weekly_goal > 0 else None,
|
||
'weekly_auto': not bool(_GOALS.get('weekly', 0)) and bool(monthly_goal), # 是否自動推算
|
||
'month_rev': month_rev,
|
||
'monthly_goal': monthly_goal,
|
||
'monthly_pct': month_rev / monthly_goal * 100 if monthly_goal > 0 else None,
|
||
'year_rev': year_rev,
|
||
'quarterly_goal': quarterly_goal,
|
||
'quarterly_pct': year_rev / 4 / quarterly_goal * 100 if quarterly_goal > 0 else None,
|
||
'half_goal': half_goal,
|
||
'half_pct': year_rev / 2 / half_goal * 100 if half_goal > 0 else None,
|
||
'yearly_goal': yearly_goal,
|
||
'yearly_pct': year_rev / yearly_goal * 100 if yearly_goal > 0 else None,
|
||
# 月目標追蹤輔助欄位
|
||
'days_elapsed': int(d[8:10]) if len(d) >= 10 else 0,
|
||
'days_in_month': __import__('calendar').monthrange(int(d[:4]), int(d[5:7]))[1] if len(d) >= 7 else 30,
|
||
}
|
||
|
||
|
||
# ── 圖表產生（matplotlib）────────────────────────────────────
|
||
|
||
_MPL_FONT_SETUP_DONE = False
|
||
|
||
def _setup_mpl_chinese():
|
||
"""確保 matplotlib 使用中文字型（只執行一次）"""
|
||
global _MPL_FONT_SETUP_DONE
|
||
if _MPL_FONT_SETUP_DONE:
|
||
return
|
||
try:
|
||
import matplotlib.font_manager as fm
|
||
import matplotlib as mpl
|
||
|
||
font_paths = [
|
||
'/usr/local/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/WQYZenHei.ttf',
|
||
'/usr/share/fonts/truetype/wqy/wqy-zenhei.ttc',
|
||
'/usr/share/fonts/truetype/wqy/wqy-microhei.ttc',
|
||
]
|
||
for fp in font_paths:
|
||
if os.path.exists(fp):
|
||
fm.fontManager.addfont(fp)
|
||
prop = fm.FontProperties(fname=fp)
|
||
mpl.rcParams['font.family'] = prop.get_name()
|
||
mpl.rcParams['axes.unicode_minus'] = False
|
||
_MPL_FONT_SETUP_DONE = True
|
||
sys_log.info(f"[OpenClawBot] matplotlib 中文字型: {fp}")
|
||
return
|
||
sys_log.warning("[OpenClawBot] 找不到中文字型，圖表文字可能亂碼")
|
||
except Exception as e:
|
||
sys_log.warning(f"[OpenClawBot] _setup_mpl_chinese: {e}")
|
||
|
||
|
||
def gen_trend_chart(days=14, data_points=None, title=None) -> str:
|
||
"""產生業績趨勢折線圖 PNG — 上升紅色/下降綠色，每日金額標註
|
||
data_points: 若提供則使用此資料 [{'date': ..., 'revenue': ...}]，否則查近 days 日
|
||
title: 自訂圖表標題
|
||
"""
|
||
try:
|
||
import matplotlib
|
||
matplotlib.use('Agg')
|
||
_setup_mpl_chinese()
|
||
import matplotlib.pyplot as plt
|
||
import matplotlib.dates as mdates
|
||
import matplotlib.patches as mpatches
|
||
from datetime import datetime as dt
|
||
import tempfile
|
||
|
||
history = data_points if data_points else query_daily_history(days)
|
||
if not history:
|
||
return ''
|
||
|
||
dates = [dt.strptime(r['date'].replace('/', '-'), '%Y-%m-%d') for r in history]
|
||
revenues = [r['revenue'] / 10000 for r in history] # 萬元
|
||
|
||
# 台灣股市慣例：上升=紅，下降=綠
|
||
UP_COLOR = '#E53935' # 上升 紅
|
||
DOWN_COLOR = '#43A047' # 下降 綠
|
||
NEUTRAL = '#1565C0' # 第一點 藍
|
||
|
||
fig, ax = plt.subplots(figsize=(14, 6))
|
||
fig.patch.set_facecolor('#FAFAFA')
|
||
ax.set_facecolor('#FAFAFA')
|
||
|
||
# 逐段繪製（每段依漲跌著色）
|
||
for i in range(1, len(dates)):
|
||
seg_color = UP_COLOR if revenues[i] >= revenues[i - 1] else DOWN_COLOR
|
||
ax.plot([dates[i - 1], dates[i]], [revenues[i - 1], revenues[i]],
|
||
'-', color=seg_color, linewidth=2.8, solid_capstyle='round')
|
||
|
||
# 每日節點（顏色依和前日比較）
|
||
for i, (d, r) in enumerate(zip(dates, revenues)):
|
||
if i == 0:
|
||
pt_color = NEUTRAL
|
||
elif r >= revenues[i - 1]:
|
||
pt_color = UP_COLOR
|
||
else:
|
||
pt_color = DOWN_COLOR
|
||
ax.plot(d, r, 'o', color=pt_color, markersize=8, zorder=5,
|
||
markeredgecolor='white', markeredgewidth=1.5)
|
||
|
||
# 每日金額標籤
|
||
for i, (d, r) in enumerate(zip(dates, revenues)):
|
||
# 決定標籤位置（奇偶交錯避免重疊）
|
||
y_offset = 12 if i % 2 == 0 else -22
|
||
ha = 'center'
|
||
if i == 0:
|
||
label_color = NEUTRAL
|
||
elif r >= revenues[i - 1]:
|
||
label_color = UP_COLOR
|
||
else:
|
||
label_color = DOWN_COLOR
|
||
ax.annotate(f'NT${r:,.1f}萬',
|
||
(d, r),
|
||
textcoords='offset points', xytext=(0, y_offset),
|
||
ha=ha, fontsize=8.5, color=label_color, fontweight='bold',
|
||
bbox=dict(boxstyle='round,pad=0.2', facecolor='white',
|
||
edgecolor=label_color, alpha=0.85, linewidth=0.8))
|
||
|
||
# 全域最高/最低特別標記
|
||
if revenues:
|
||
max_i = revenues.index(max(revenues))
|
||
min_i = revenues.index(min(revenues))
|
||
ax.scatter([dates[max_i]], [revenues[max_i]], s=180, color=UP_COLOR,
|
||
zorder=6, marker='*', edgecolors='white', linewidths=0.8)
|
||
ax.scatter([dates[min_i]], [revenues[min_i]], s=180, color=DOWN_COLOR,
|
||
zorder=6, marker='v', edgecolors='white', linewidths=0.8)
|
||
|
||
# X 軸：日期 + 星期
|
||
WEEKDAYS_ZH = ['週一', '週二', '週三', '週四', '週五', '週六', '週日']
|
||
ax.xaxis.set_major_formatter(mdates.DateFormatter('%m/%d'))
|
||
ax.xaxis.set_major_locator(mdates.DayLocator(interval=1))
|
||
|
||
# 替換 x tick labels 加上星期
|
||
tick_dates = dates
|
||
tick_labels = [f"{d.strftime('%m/%d')}\n{WEEKDAYS_ZH[d.weekday()]}" for d in tick_dates]
|
||
ax.set_xticks(tick_dates)
|
||
ax.set_xticklabels(tick_labels, fontsize=9)
|
||
|
||
ax.set_ylabel('業績（萬元）', fontsize=12)
|
||
today_str = datetime.now(TAIPEI_TZ).strftime('%Y/%m/%d')
|
||
chart_title = title or f'業績趨勢走勢圖 — 近 {days} 日 （截至 {today_str}）'
|
||
ax.set_title(chart_title, fontsize=14, fontweight='bold', pad=14)
|
||
ax.grid(True, alpha=0.25, linestyle='--', color='#BDBDBD')
|
||
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'{x:,.0f}'))
|
||
|
||
# 圖例
|
||
up_patch = mpatches.Patch(color=UP_COLOR, label='▲ 較前日上升')
|
||
down_patch = mpatches.Patch(color=DOWN_COLOR, label='▼ 較前日下降')
|
||
ax.legend(handles=[up_patch, down_patch], loc='upper left', fontsize=10,
|
||
framealpha=0.85)
|
||
|
||
# 統計副標題
|
||
if len(revenues) >= 2:
|
||
total_rev = sum(revenues)
|
||
avg_rev = total_rev / len(revenues)
|
||
max_rev = max(revenues)
|
||
min_rev = min(revenues)
|
||
last_change_pct = (revenues[-1] - revenues[-2]) / revenues[-2] * 100 if revenues[-2] else 0
|
||
arrow = '▲' if last_change_pct >= 0 else '▼'
|
||
stats_text = (f"總計 NT${total_rev:,.1f}萬 | "
|
||
f"日均 NT${avg_rev:,.1f}萬 | "
|
||
f"最高 NT${max_rev:,.1f}萬 | 最低 NT${min_rev:,.1f}萬 | "
|
||
f"昨日 {arrow}{abs(last_change_pct):.1f}%")
|
||
fig.text(0.5, 0.01, stats_text, ha='center', fontsize=9,
|
||
color='#616161', style='italic')
|
||
|
||
plt.tight_layout(rect=[0, 0.04, 1, 1])
|
||
tmp = tempfile.NamedTemporaryFile(
|
||
suffix='.png', prefix='ocbot_trend_', delete=False, dir='/tmp')
|
||
plt.savefig(tmp.name, dpi=130, bbox_inches='tight')
|
||
plt.close()
|
||
return tmp.name
|
||
except ImportError:
|
||
sys_log.warning('[OpenClawBot] matplotlib not installed — chart disabled')
|
||
return ''
|
||
except Exception as e:
|
||
sys_log.error(f'[OpenClawBot] gen_trend_chart: {e}')
|
||
return ''
|
||
|
||
|
||
def gen_products_chart(date_str, n=10) -> str:
|
||
"""產生熱銷商品橫條圖 PNG — 上升紅/下降綠，標示業績金額"""
|
||
try:
|
||
import matplotlib
|
||
matplotlib.use('Agg')
|
||
_setup_mpl_chinese()
|
||
import matplotlib.pyplot as plt
|
||
import matplotlib.patches as mpatches
|
||
from datetime import datetime as dt
|
||
import tempfile
|
||
|
||
products = query_top_products(date_str, n)
|
||
if not products:
|
||
return ''
|
||
|
||
# 取得上週同日資料做顏色比較
|
||
try:
|
||
d = dt.strptime(date_str.replace('/', '-'), '%Y-%m-%d').date()
|
||
lw_str = (d - timedelta(days=7)).strftime('%Y/%m/%d')
|
||
lw_products = query_top_products(lw_str, n * 2)
|
||
lw_map = {p.get('id'): p['revenue'] for p in lw_products if p.get('id')}
|
||
except Exception:
|
||
lw_map = {}
|
||
|
||
UP_COLOR = '#E53935' # 上升/優 紅
|
||
DOWN_COLOR = '#43A047' # 下降 綠
|
||
NEUTRAL = '#1976D2' # 無前期資料 藍
|
||
|
||
# 短名稱（限14字）+ ID
|
||
labels = [f"{p['name'][:13]}…\n[{_short_id(p.get('id',''))}]"
|
||
if len(p['name']) > 13
|
||
else f"{p['name']}\n[{_short_id(p.get('id',''))}]"
|
||
for p in products]
|
||
revenues = [p['revenue'] / 10000 for p in products]
|
||
|
||
# 決定每條顏色
|
||
colors = []
|
||
for p in products:
|
||
pid = p.get('id')
|
||
lw_rev = lw_map.get(pid)
|
||
if lw_rev is None:
|
||
colors.append(NEUTRAL)
|
||
elif p['revenue'] >= lw_rev:
|
||
colors.append(UP_COLOR)
|
||
else:
|
||
colors.append(DOWN_COLOR)
|
||
|
||
# 翻轉（matplotlib barh 從下往上）
|
||
labels.reverse(); revenues.reverse(); colors.reverse()
|
||
|
||
fig, ax = plt.subplots(figsize=(13, max(6, n * 0.75)))
|
||
fig.patch.set_facecolor('#FAFAFA')
|
||
ax.set_facecolor('#FAFAFA')
|
||
|
||
bars = ax.barh(labels, revenues, color=colors, alpha=0.88,
|
||
height=0.62, edgecolor='white', linewidth=0.8)
|
||
|
||
max_rev = max(revenues) if revenues else 1
|
||
for bar, rev, col in zip(bars, revenues, colors):
|
||
# 金額標籤（在 bar 右側）
|
||
ax.text(bar.get_width() + max_rev * 0.008,
|
||
bar.get_y() + bar.get_height() / 2,
|
||
f'NT${rev:,.2f}萬',
|
||
va='center', ha='left', fontsize=9.5,
|
||
color=col, fontweight='bold')
|
||
|
||
ax.set_xlabel('業績（萬元）', fontsize=11)
|
||
ax.set_title(f'🏆 熱銷商品 TOP{n} — {date_str}',
|
||
fontsize=14, fontweight='bold', pad=12)
|
||
ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'{x:,.0f}'))
|
||
ax.grid(True, alpha=0.25, linestyle='--', color='#BDBDBD', axis='x')
|
||
|
||
# 設定 xlim 留右側空間給標籤
|
||
ax.set_xlim(0, max_rev * 1.35)
|
||
|
||
# 圖例
|
||
up_p = mpatches.Patch(color=UP_COLOR, label='▲ 週環比上升')
|
||
dn_p = mpatches.Patch(color=DOWN_COLOR, label='▼ 週環比下降')
|
||
ne_p = mpatches.Patch(color=NEUTRAL, label='— 無前期資料')
|
||
ax.legend(handles=[up_p, dn_p, ne_p], loc='lower right', fontsize=9,
|
||
framealpha=0.85)
|
||
|
||
plt.tight_layout()
|
||
tmp = tempfile.NamedTemporaryFile(
|
||
suffix='.png', prefix='ocbot_products_', delete=False, dir='/tmp')
|
||
plt.savefig(tmp.name, dpi=130, bbox_inches='tight')
|
||
plt.close()
|
||
return tmp.name
|
||
except ImportError:
|
||
return ''
|
||
except Exception as e:
|
||
sys_log.error(f'[OpenClawBot] gen_products_chart: {e}')
|
||
return ''
|
||
|
||
|
||
# ── 商品策略矩陣 ───────────────────────────────────────────────
|
||
|
||
def analyze_product_strategy(date_str: str, top_n=10) -> list:
|
||
"""內部業績週環比 × 外部MCP趨勢 → 策略標籤"""
|
||
products = query_top_products(date_str, top_n * 2)
|
||
if not products:
|
||
return []
|
||
|
||
from datetime import datetime as dt
|
||
try:
|
||
d = dt.strptime(normalize_date(date_str).replace('/', '-'), '%Y-%m-%d').date()
|
||
lw_str = (d - timedelta(days=7)).strftime('%Y/%m/%d')
|
||
lw_products = query_top_products(lw_str, top_n * 2)
|
||
lw_map = {p.get('id'): p['revenue'] for p in lw_products if p.get('id')}
|
||
except Exception:
|
||
lw_map = {}
|
||
|
||
try:
|
||
from services.mcp_context_service import get_taiwan_trends
|
||
trend_kws = [t['keyword'] for t in get_taiwan_trends().get('trends', [])][:20]
|
||
except Exception:
|
||
trend_kws = []
|
||
|
||
result = []
|
||
for p in products[:top_n]:
|
||
pid, name, rev = p.get('id', ''), p['name'], p['revenue']
|
||
lw_rev = lw_map.get(pid, 0)
|
||
growth = (rev - lw_rev) / lw_rev if lw_rev > 0 else 0
|
||
ext_hot = any(kw in name for kw in trend_kws) if trend_kws else None
|
||
|
||
if growth > 0.1 and (ext_hot or ext_hot is None):
|
||
tag, strat, advice = '🔥', '加碼', '內外皆熱，立即加大廣告投放'
|
||
elif growth >= -0.1 and ext_hot:
|
||
tag, strat, advice = '💡', '機會', '外部熱搜但放量不足，提升曝光'
|
||
elif growth > 0.1 and not ext_hot:
|
||
tag, strat, advice = '⚡', '收割', '內部增長強，外部降溫前先收割'
|
||
elif growth < -0.1 and not ext_hot:
|
||
tag, strat, advice = '⚠️', '觀察', '內外皆疲，考慮轉移資源'
|
||
else:
|
||
tag, strat, advice = '✅', '持穩', '表現平穩，維持現狀'
|
||
|
||
result.append({
|
||
'id': pid, 'name': name, 'revenue': rev,
|
||
'growth': growth, 'ext_hot': ext_hot,
|
||
'tag': tag, 'strategy': strat, 'advice': advice,
|
||
})
|
||
return result
|
||
|
||
|
||
def _analyze_strategy_range(start_str: str, end_str: str, products: list) -> list:
|
||
"""區間版策略分析（週/月/季/年用）：比對前一等長區間的成長率"""
|
||
if not products:
|
||
return []
|
||
try:
|
||
s = datetime.strptime(start_str.replace('/', '-'), '%Y-%m-%d')
|
||
e = datetime.strptime(end_str.replace('/', '-'), '%Y-%m-%d')
|
||
delta = (e - s).days + 1
|
||
prev_end = (s - timedelta(days=1)).strftime('%Y/%m/%d')
|
||
prev_start = (s - timedelta(days=delta)).strftime('%Y/%m/%d')
|
||
prev_prods = query_top_products_range(prev_start, prev_end, len(products) * 2)
|
||
prev_map = {p.get('id'): p['revenue'] for p in prev_prods if p.get('id')}
|
||
except Exception:
|
||
prev_map = {}
|
||
|
||
try:
|
||
from services.mcp_context_service import get_taiwan_trends
|
||
trend_kws = [t['keyword'] for t in get_taiwan_trends().get('trends', [])][:20]
|
||
except Exception:
|
||
trend_kws = []
|
||
|
||
result = []
|
||
for p in products:
|
||
pid, name, rev = p.get('id', ''), p['name'], p['revenue']
|
||
prev_rev = prev_map.get(pid, 0)
|
||
growth = (rev - prev_rev) / prev_rev if prev_rev > 0 else 0
|
||
ext_hot = any(kw in name for kw in trend_kws) if trend_kws else None
|
||
|
||
if growth > 0.1 and (ext_hot or ext_hot is None):
|
||
tag, strat, advice = '🔥', '加碼', '內外皆熱，立即加大廣告投放'
|
||
elif growth >= -0.1 and ext_hot:
|
||
tag, strat, advice = '💡', '機會', '外部熱搜但放量不足，提升曝光'
|
||
elif growth > 0.1 and not ext_hot:
|
||
tag, strat, advice = '⚡', '收割', '內部增長強，外部降溫前先收割'
|
||
elif growth < -0.1 and not ext_hot:
|
||
tag, strat, advice = '⚠️', '觀察', '內外皆疲，考慮轉移資源'
|
||
else:
|
||
tag, strat, advice = '✅', '持穩', '表現平穩，維持現狀'
|
||
|
||
result.append({
|
||
'id': pid, 'name': name, 'revenue': rev,
|
||
'growth': growth, 'ext_hot': ext_hot,
|
||
'tag': tag, 'strategy': strat, 'advice': advice,
|
||
})
|
||
return result
|
||
|
||
|
||
# ── 新增格式化函數 ─────────────────────────────────────────────
|
||
|
||
def fmt_category(cats, date_str):
|
||
if not cats:
|
||
return (f"⚠️ *分類資料不足*\n\n"
|
||
f"`{date_str}` 無分類業績資料\n"
|
||
f"_請確認資料是否包含「商品分類」欄位_")
|
||
total = sum(c['revenue'] for c in cats)
|
||
lines = [
|
||
f"🗂 *{date_str} 分類業績分析*",
|
||
f"合計 `NT$ {total:,.0f}` | 共 {len(cats)} 個分類",
|
||
f"{'─' * 30}",
|
||
"",
|
||
]
|
||
for i, c in enumerate(cats):
|
||
rev = c['revenue']
|
||
pct = rev / total * 100 if total > 0 else 0
|
||
medal = MEDALS[i] if i < len(MEDALS) else f"`{i+1}.`"
|
||
bar_len = max(1, int(pct / 100 * 10))
|
||
mini_bar = '█' * bar_len + '░' * (10 - bar_len)
|
||
lines.append(f"{medal} *{c['cat']}*")
|
||
lines.append(f" 💰 `NT$ {rev:,.0f}` 🛍 {c['products']}款")
|
||
lines.append(f" `{mini_bar}` 佔比 *{pct:.1f}%*")
|
||
lines.append("")
|
||
|
||
# 前3名佔比
|
||
top3_pct = sum(c['revenue'] for c in cats[:3]) / total * 100 if total else 0
|
||
lines.append(f"_📌 前3類合計佔比：{top3_pct:.1f}%_")
|
||
return "\n".join(lines)
|
||
|
||
|
||
def fmt_comparison(data, date_str):
|
||
if not data:
|
||
return "⚠️ *無法取得比較資料*\n\n請確認歷史資料已匯入。"
|
||
|
||
def _delta_str(a, b):
|
||
if not b:
|
||
return '`—`'
|
||
pct = (a - b) / b * 100
|
||
arrow = '▲' if pct >= 0 else '▼'
|
||
emoji = '🟢' if pct >= 0 else '🔴'
|
||
return f"{emoji} `{arrow}{abs(pct):.1f}%` (`NT$ {a - b:+,.0f}`)"
|
||
|
||
today = data.get('today', {})
|
||
lw = data.get('last_week', {})
|
||
lm = data.get('last_month', {})
|
||
r_t = today.get('revenue', 0)
|
||
r_lw = lw.get('revenue', 0)
|
||
r_lm = lm.get('revenue', 0)
|
||
|
||
lines = [
|
||
f"🔄 *{date_str} 業績同期比較*",
|
||
f"{'─' * 30}",
|
||
"",
|
||
f"📅 *本日業績* `NT$ {r_t:,.0f}`",
|
||
"",
|
||
]
|
||
if r_lw:
|
||
lines.append(f"📌 *vs 上週同日*（{lw['date']}）")
|
||
lines.append(f" 上週：`NT$ {r_lw:,.0f}`")
|
||
lines.append(f" 差異：{_delta_str(r_t, r_lw)}")
|
||
lines.append("")
|
||
if r_lm:
|
||
lines.append(f"📌 *vs 上月同日*（{lm['date']}）")
|
||
lines.append(f" 上月：`NT$ {r_lm:,.0f}`")
|
||
lines.append(f" 差異：{_delta_str(r_t, r_lm)}")
|
||
lines.append("")
|
||
|
||
# 趨勢判斷
|
||
if r_lw and r_lm:
|
||
both_up = r_t >= r_lw and r_t >= r_lm
|
||
both_down = r_t < r_lw and r_t < r_lm
|
||
if both_up:
|
||
lines.append("✅ *整體趨勢*：本日業績優於上週及上月同期，表現亮眼！")
|
||
elif both_down:
|
||
lines.append("⚠️ *整體趨勢*：本日業績低於上週及上月同期，需關注原因。")
|
||
else:
|
||
lines.append("📊 *整體趨勢*：表現不一，建議深入分析商品結構。")
|
||
|
||
return "\n".join(lines)
|
||
|
||
|
||
def fmt_goal_status(status):
|
||
if not status:
|
||
return "⚠️ *無法取得目標資料*"
|
||
rev = status.get('today_rev', 0) or 0
|
||
dg = status.get('daily_goal', 0) or 0
|
||
mg = status.get('monthly_goal', 0) or 0
|
||
|
||
lines = [
|
||
f"🎯 *目標達成率* _({status.get('date','')})_",
|
||
f"{'─' * 30}",
|
||
"",
|
||
]
|
||
|
||
lines.append(f"💰 *今日業績* `NT$ {rev:,.0f}`")
|
||
lines.append("")
|
||
|
||
if dg:
|
||
pct = status.get('daily_pct', 0) or 0
|
||
gap = dg - rev
|
||
bar_len = min(10, max(0, int(pct // 10)))
|
||
bar = '█' * bar_len + '░' * (10 - bar_len)
|
||
|
||
# 顏色 emoji
|
||
if pct >= 100:
|
||
status_emoji = '🏆'
|
||
status_text = f"*超標達成！* 🎉 超出 `NT$ {abs(gap):,.0f}`"
|
||
elif pct >= 80:
|
||
status_emoji = '🟢'
|
||
status_text = f"接近達標 還差 `NT$ {gap:,.0f}`"
|
||
elif pct >= 50:
|
||
status_emoji = '🟡'
|
||
status_text = f"進度落後 還差 `NT$ {gap:,.0f}`"
|
||
else:
|
||
status_emoji = '🔴'
|
||
status_text = f"需要加速 還差 `NT$ {gap:,.0f}`"
|
||
|
||
lines.append(f"📌 *日目標* `NT$ {dg:,.0f}`")
|
||
lines.append(f" `[{bar}]` *{pct:.1f}%* {status_emoji}")
|
||
lines.append(f" {status_text}")
|
||
lines.append("")
|
||
else:
|
||
lines.append("_⚙️ 尚未設定日目標_")
|
||
lines.append("_用 `/goal 200000` 設定每日目標_")
|
||
lines.append("")
|
||
|
||
def _goal_bar(pct, goal, actual, label, emoji):
|
||
if not goal:
|
||
return None
|
||
gap = goal - actual
|
||
bar_len = min(10, max(0, int(pct // 10)))
|
||
bar = '█' * bar_len + '░' * (10 - bar_len)
|
||
suffix = f" 🎉 超標 `NT$ {abs(gap):,.0f}`" if gap <= 0 else f" 還差 `NT$ {gap:,.0f}`"
|
||
return [
|
||
f"{emoji} *{label}* `NT$ {goal:,.0f}` → 已達 `NT$ {actual:,.0f}`",
|
||
f" `[{bar}]` *{pct:.1f}%*{suffix}",
|
||
]
|
||
|
||
# 週目標（P9）
|
||
wg = status.get('weekly_goal', 0) or 0
|
||
if wg:
|
||
week_rows = _goal_bar(status.get('weekly_pct') or 0, wg,
|
||
status.get('week_rev', 0) or 0, '週目標', '📅')
|
||
auto_tag = ' _(自動推算)_' if status.get('weekly_auto') else ''
|
||
if week_rows:
|
||
week_rows[0] += auto_tag
|
||
lines += week_rows
|
||
lines.append("")
|
||
|
||
for rows in [
|
||
_goal_bar(status.get('monthly_pct') or 0, mg,
|
||
status.get('month_rev', 0) or 0, '月目標', '📅'),
|
||
_goal_bar(status.get('quarterly_pct') or 0, status.get('quarterly_goal', 0) or 0,
|
||
(status.get('year_rev', 0) or 0) / 4, '季目標', '📆'),
|
||
_goal_bar(status.get('half_pct') or 0, status.get('half_goal', 0) or 0,
|
||
(status.get('year_rev', 0) or 0) / 2, '半年目標', '🗓'),
|
||
_goal_bar(status.get('yearly_pct') or 0, status.get('yearly_goal', 0) or 0,
|
||
status.get('year_rev', 0) or 0, '年度目標', '🏁'),
|
||
]:
|
||
if rows:
|
||
lines += rows
|
||
lines.append("")
|
||
|
||
# ── 月目標倒計時 ─────────────────────────────────────────
|
||
if mg:
|
||
month_rev = status.get('month_rev', 0) or 0
|
||
days_elapsed = status.get('days_elapsed', 0) or 0
|
||
days_total = status.get('days_in_month', 30) or 30
|
||
days_remain = days_total - days_elapsed
|
||
if days_remain > 0 and month_rev < mg:
|
||
needed_daily = (mg - month_rev) / days_remain
|
||
gap_pct = (mg - month_rev) / mg * 100
|
||
lines.append(f"{'─' * 26}")
|
||
lines.append(f"📆 *月目標倒計時*")
|
||
lines.append(f" 已過 {days_elapsed} 天 / 剩 {days_remain} 天")
|
||
lines.append(f" 還差 `NT$ {mg - month_rev:,.0f}`（{gap_pct:.0f}%）")
|
||
lines.append(f" ✅ 每日至少需達 `NT$ {needed_daily:,.0f}`")
|
||
if days_elapsed > 0:
|
||
actual_daily = month_rev / days_elapsed
|
||
diff = actual_daily - needed_daily
|
||
if diff >= 0:
|
||
lines.append(f" 📈 日均 `NT$ {actual_daily:,.0f}` — *超標 +`NT$ {diff:,.0f}`*")
|
||
else:
|
||
lines.append(f" 📉 日均 `NT$ {actual_daily:,.0f}` — 落後 `NT$ {abs(diff):,.0f}`")
|
||
elif days_remain > 0 and month_rev >= mg:
|
||
lines.append(f"🎉 *月目標已達成！* 超出 `NT$ {month_rev - mg:,.0f}`，剩 {days_remain} 天繼續衝！")
|
||
|
||
if not any([mg, status.get('quarterly_goal'), status.get('half_goal'), status.get('yearly_goal')]):
|
||
lines.append("_⚙️ 尚未設定長期目標_")
|
||
lines.append("_點選 🎯 目標管理 → 設定各週期目標_")
|
||
|
||
return "\n".join(lines)
|
||
|
||
|
||
def _short_id(pid: str) -> str:
|
||
"""DDABGC-A900H854P-000 → A900H854P（取中段，較易讀）"""
|
||
parts = str(pid).split('-')
|
||
return parts[1] if len(parts) >= 3 else str(pid)[:12]
|
||
|
||
|
||
def fmt_restock_forecast(items: list) -> str:
|
||
"""補貨預測報告格式化"""
|
||
if not items:
|
||
return "⚠️ *補貨預測*\n\n暫無足夠銷售資料（需 3 天以上紀錄）"
|
||
high = [i for i in items if i['urgency'] == 'HIGH']
|
||
med = [i for i in items if i['urgency'] == 'MED']
|
||
low = [i for i in items if i['urgency'] == 'LOW']
|
||
lines = [
|
||
"📦 *補貨預測報告*",
|
||
f"{'─' * 26}",
|
||
f"_基於近 7 日銷售速度分析_",
|
||
"",
|
||
]
|
||
if high:
|
||
lines.append("🔴 *緊急補貨（高銷速 + 加速中）*")
|
||
for i in high[:6]:
|
||
accel_s = f"↑{i['accel']:.0f}%" if i['accel'] > 0 else f"↓{abs(i['accel']):.0f}%"
|
||
lines.append(
|
||
f" 🔥 *{i['name'][:22]}*\n"
|
||
f" 日均 {i['daily_vel']:.1f} 件 趨勢 {accel_s} 週業績 `NT$ {i['rev7']:,.0f}`"
|
||
)
|
||
lines.append("")
|
||
if med:
|
||
lines.append("🟡 *建議補貨（中速 or 快速加速）*")
|
||
for i in med[:5]:
|
||
accel_s = f"↑{i['accel']:.0f}%" if i['accel'] > 0 else f"↓{abs(i['accel']):.0f}%"
|
||
lines.append(f" ⚡ *{i['name'][:22]}* 日均 {i['daily_vel']:.1f} 件 {accel_s}")
|
||
lines.append("")
|
||
if low:
|
||
lines.append(f"🟢 *持續追蹤（{len(low)} 件）* 銷售平穩，暫無急迫性")
|
||
lines.append("")
|
||
lines.append("_💡 高速商品建議備貨 ≥ 14 日量；中速備貨 ≥ 7 日_")
|
||
return "\n".join(lines)
|
||
|
||
|
||
def fmt_category_detail(category: str, items: list, date_label: str = '') -> str:
|
||
"""分類業績鑽取格式化"""
|
||
if not items:
|
||
return f"⚠️ *{category}* 分類無細項資料"
|
||
total_rev = sum(i['revenue'] for i in items)
|
||
period = f" _{date_label}_" if date_label else ''
|
||
lines = [
|
||
f"🗂 *{category} 分類業績*{period}",
|
||
f"{'─' * 26}",
|
||
f"共 {len(items)} 個子類 合計 `NT$ {total_rev:,.0f}`",
|
||
"",
|
||
]
|
||
medals = ['🥇','🥈','🥉','④','⑤','⑥','⑦','⑧','⑨','⑩']
|
||
for i, item in enumerate(items[:10]):
|
||
pct = item['revenue'] / total_rev * 100 if total_rev else 0
|
||
bar_len = min(8, max(1, int(pct / 12.5)))
|
||
bar = '█' * bar_len + '░' * (8 - bar_len)
|
||
medal = medals[i] if i < 10 else str(i + 1)
|
||
lines.append(
|
||
f" {medal} *{item['cat']}*\n"
|
||
f" `{bar}` {pct:.1f}% `NT$ {item['revenue']:,.0f}` {item['products']}商品 {item['qty']}件"
|
||
)
|
||
return "\n".join(lines)
|
||
|
||
|
||
def fmt_promo_comparison(data: dict, label: str = '') -> str:
|
||
"""促銷效果比較格式化"""
|
||
if not data or not data.get('promo'):
|
||
return "⚠️ *促銷比較*\n\n查無資料，請確認日期範圍"
|
||
promo = data['promo']
|
||
pre = data['pre']
|
||
rev_lift = data.get('rev_lift', 0)
|
||
ord_lift = data.get('ord_lift', 0)
|
||
lift_emoji = '📈' if rev_lift >= 0 else '📉'
|
||
lines = [
|
||
f"🎉 *促銷活動效益分析*",
|
||
f"{'─' * 26}",
|
||
f"活動期間：`{promo['start']}` ~ `{promo['end']}`（{promo['days']}天）",
|
||
f"對比前期：`{pre['start']}` ~ `{pre['end']}`",
|
||
"",
|
||
f"{'─' * 26}",
|
||
f"💰 *業績比較*",
|
||
f" 活動期：`NT$ {promo['revenue']:,.0f}` 訂單 {promo['orders']} 筆",
|
||
f" 前同期：`NT$ {pre['revenue']:,.0f}` 訂單 {pre['orders']} 筆",
|
||
f" {lift_emoji} 業績成長 *{rev_lift:+.1f}%* 訂單成長 *{ord_lift:+.1f}%*",
|
||
"",
|
||
]
|
||
if promo.get('top_products'):
|
||
lines.append("🏆 *活動期間熱銷 TOP5*")
|
||
for i, p in enumerate(promo['top_products'][:5]):
|
||
medals = ['🥇','🥈','🥉','④','⑤']
|
||
lines.append(f" {medals[i]} {p['name'][:22]} `NT$ {p['revenue']:,.0f}`")
|
||
lines.append("")
|
||
# 效益評估
|
||
if rev_lift >= 20:
|
||
lines.append("✅ *效益優良！* 建議複製此促銷模式")
|
||
elif rev_lift >= 5:
|
||
lines.append("🟡 *效益普通* 可調整折扣力度或品項")
|
||
else:
|
||
lines.append("🔴 *效益不佳* 建議檢視促銷定價與商品組合")
|
||
return "\n".join(lines)
|
||
|
||
|
||
def track_competitor_price_changes(results: list) -> list:
|
||
"""追蹤 momo 競品價格變動，回傳降價警報清單"""
|
||
if not results:
|
||
return []
|
||
try:
|
||
import redis as _redis
|
||
r = _redis.Redis(host='localhost', port=6379, db=12, socket_connect_timeout=2)
|
||
r.ping()
|
||
changes = []
|
||
for item in results:
|
||
momo_id = str(item.get('momo_icode') or item.get('momo_id') or item.get('id', ''))
|
||
if not momo_id:
|
||
continue
|
||
curr_price = float(item.get('momo_price') or 0)
|
||
if curr_price <= 0:
|
||
continue
|
||
key = f"price_hist:{momo_id}"
|
||
prev_raw = r.get(key)
|
||
if prev_raw:
|
||
prev_price = float(prev_raw)
|
||
if prev_price > 0:
|
||
pct = (curr_price - prev_price) / prev_price * 100
|
||
if pct <= -5: # momo 降價 ≥5% → PChome 需注意
|
||
changes.append({
|
||
'name': item.get('momo_name', '')[:28],
|
||
'prev_price': prev_price,
|
||
'curr_price': curr_price,
|
||
'pct': round(pct, 1),
|
||
'momo_id': momo_id,
|
||
})
|
||
# 更新快照（保留 8 天）
|
||
r.setex(key, 8 * 86400, str(curr_price))
|
||
return changes
|
||
except Exception as _e:
|
||
sys_log.warning(f"[price_track] Redis 不可用：{_e}")
|
||
return []
|
||
|
||
|
||
def fmt_monthly(ms: dict) -> str:
|
||
"""月份業績格式化"""
|
||
if not ms.get('found'):
|
||
return (f"⚠️ *{ms.get('month','?')} 月份無業績資料*\n\n"
|
||
f"此月份資料尚未匯入，或超出資料範圍。\n"
|
||
f"_使用 /history 查看所有可用月份_")
|
||
|
||
month = ms['month']
|
||
rev = ms['revenue']
|
||
orders = ms['orders']
|
||
days = ms['days_with_data']
|
||
avg_d = rev / days if days else 0
|
||
avg_o = ms['avg_order']
|
||
margin = ms['gross_margin']
|
||
|
||
# 月進度 bar（以最高日為基準）
|
||
daily = ms.get('daily', [])
|
||
max_d_rev = max((d['revenue'] for d in daily), default=1)
|
||
WEEKDAYS_ZH = ['週一', '週二', '週三', '週四', '週五', '週六', '週日']
|
||
|
||
lines = [
|
||
f"📅 *{month} 月份業績報告*",
|
||
f"{'─' * 30}",
|
||
"",
|
||
f"💰 *月業績* `NT$ {rev:,.0f}`",
|
||
f"📦 月訂單 `{orders:,}` 筆 | 有資料 `{days}` 天",
|
||
f"🛒 日均業績 `NT$ {avg_d:,.0f}`",
|
||
f"🛒 客單均價 `NT$ {avg_o:,.0f}`",
|
||
f"📈 整月毛利率 `{margin:.1f}%`",
|
||
"",
|
||
]
|
||
|
||
if daily:
|
||
lines.append(f"📊 *逐日業績*")
|
||
for i, d in enumerate(daily):
|
||
bar_len = max(1, int(d['revenue'] / max_d_rev * 8))
|
||
bar = '█' * bar_len + '·' * (8 - bar_len)
|
||
# 漲跌
|
||
if i > 0:
|
||
prev = daily[i - 1]['revenue']
|
||
chg = (d['revenue'] - prev) / prev * 100 if prev else 0
|
||
chg_str = f" {'▲' if chg >= 0 else '▼'}{abs(chg):.0f}%"
|
||
else:
|
||
chg_str = ''
|
||
try:
|
||
from datetime import datetime as dt
|
||
d_obj = dt.strptime(d['date'].replace('/', '-'), '%Y-%m-%d')
|
||
wday = WEEKDAYS_ZH[d_obj.weekday()]
|
||
except Exception:
|
||
wday = ''
|
||
lines.append(
|
||
f" `{d['date']}` {wday} `{bar}` `NT$ {d['revenue']:>10,.0f}`{chg_str}"
|
||
)
|
||
lines.append("")
|
||
|
||
if ms.get('top_products'):
|
||
lines.append(f"🏆 *月熱銷 TOP10*")
|
||
total_rev = rev
|
||
for i, p in enumerate(ms['top_products'][:10]):
|
||
medal = MEDALS[i] if i < len(MEDALS) else f"{i+1}."
|
||
pid = p.get('id', '') or ''
|
||
sid = _short_id(pid)
|
||
link = _pchome_link(pid, p['name'], 22)
|
||
pct = p['revenue'] / total_rev * 100 if total_rev else 0
|
||
lines.append(f" {medal} {link}")
|
||
lines.append(f" 🆔 `{sid}` 💰 `NT$ {p['revenue']:,.0f}` 佔 {pct:.1f}% 📦{p.get('qty',0)}件")
|
||
lines.append("")
|
||
|
||
if ms.get('top_vendors'):
|
||
lines.append(f"🏭 *月熱銷廠商 TOP10*")
|
||
for i, v in enumerate(ms['top_vendors'][:10]):
|
||
medal = MEDALS[i] if i < len(MEDALS) else f"{i+1}."
|
||
pct = v['revenue'] / rev * 100 if rev else 0
|
||
lines.append(f" {medal} {_esc(v['name'][:22])} `NT$ {v['revenue']:,.0f}` {pct:.1f}%")
|
||
|
||
return "\n".join(lines)
|
||
|
||
|
||
def fmt_strategy(items, date_str=''):
|
||
if not items:
|
||
return "⚠️ *暫無策略資料*\n\n請確認商品業績資料已匯入。"
|
||
|
||
GROUP_META = {
|
||
'加碼': ('🔥', '加碼爆量', '內外皆熱 → 立刻加大廣告預算、補庫存', '#FF5722'),
|
||
'機會': ('💡', '把握機會', '外部熱搜 → 快速提升曝光，搶奪市佔', '#2196F3'),
|
||
'收割': ('⚡', '趁勢收割', '內部增長 → 鎖定高毛利，衝刺轉換率', '#9C27B0'),
|
||
'觀察': ('⚠️', '謹慎觀察', '內外皆疲 → 停止加碼，評估庫存去化', '#FF9800'),
|
||
'持穩': ('✅', '穩定經營', '表現平穩 → 維持現狀，小幅優化素材', '#4CAF50'),
|
||
}
|
||
ORDER = ['加碼', '機會', '收割', '觀察', '持穩']
|
||
|
||
groups: dict = {k: [] for k in ORDER}
|
||
for s in items:
|
||
key = s.get('strategy', '持穩')
|
||
if key in groups:
|
||
groups[key].append(s)
|
||
else:
|
||
groups['持穩'].append(s)
|
||
|
||
title = f"🧬 *商品策略矩陣*"
|
||
if date_str:
|
||
title += f" _({date_str})_"
|
||
lines = [title, ""]
|
||
|
||
# 計算各組商品數 & 業績小計
|
||
summary_parts = []
|
||
for k in ORDER:
|
||
if groups[k]:
|
||
meta = GROUP_META[k]
|
||
summary_parts.append(f"{meta[0]}{k}×{len(groups[k])}")
|
||
if summary_parts:
|
||
lines.append("_分佈：" + " ".join(summary_parts) + "_")
|
||
lines.append("")
|
||
|
||
for key in ORDER:
|
||
group_items = groups[key]
|
||
if not group_items:
|
||
continue
|
||
meta = GROUP_META[key]
|
||
emoji, label, advice, _ = meta
|
||
group_rev = sum(s['revenue'] for s in group_items)
|
||
lines.append(f"{emoji} *{label}* _{advice}_")
|
||
lines.append(f" 共 {len(group_items)} 件 | 業績合計 `NT$ {group_rev:,.0f}`")
|
||
for s in group_items:
|
||
pid = s.get('id', '') or ''
|
||
sid = _short_id(pid)
|
||
link = _pchome_link(pid, s['name'], 20)
|
||
rev = s['revenue']
|
||
growth = s.get('growth', 0)
|
||
if growth and growth != 0:
|
||
g_val = abs(growth) * 100
|
||
g_str = f" 週{'▲' if growth > 0 else '▼'}{g_val:.0f}%"
|
||
g_emoji = '📈' if growth > 0 else '📉'
|
||
else:
|
||
g_str = ''
|
||
g_emoji = ''
|
||
lines.append(f" › {link}")
|
||
lines.append(f" 🆔 `{sid}` 💰 `NT$ {rev:,.0f}`{g_str} {g_emoji}")
|
||
lines.append("")
|
||
|
||
lines.append("_💡 策略：內部週環比 × 外部 Google 熱搜雙維度分析_")
|
||
return "\n".join(lines).rstrip()
|
||
|
||
|
||
# ── 簡報生成 ──────────────────────────────────────────────────
|
||
|
||
def _clean_ai_text(text: str) -> str:
|
||
"""清理 AI 輸出：移除 Markdown 語法、去除開場白"""
|
||
import re
|
||
# 去除 **bold**、*italic*、`code`
|
||
text = re.sub(r'\*{1,3}([^*\n]+)\*{1,3}', r'\1', text)
|
||
text = re.sub(r'`([^`\n]+)`', r'\1', text)
|
||
# 去除 ## 標題符號
|
||
text = re.sub(r'^#{1,4}\s*', '', text, flags=re.MULTILINE)
|
||
# 去除 AI 慣用開場白
|
||
text = re.sub(
|
||
r'^(好的[，,。]?|以下是.*?[:：]|針對.*?分析[如下如下]?[:：]?|'
|
||
r'根據.*?資料[，,]?|以下為.*?[:：])\s*\n?',
|
||
'', text, flags=re.IGNORECASE
|
||
)
|
||
return text.strip()
|
||
|
||
|
||
def _ppt_ai_analysis(prompt_data: str, report_type: str = '') -> str:
|
||
"""
|
||
用 NIM DeepSeek 生成簡報 AI 分析文字
|
||
（批次任務用 NIM，節省 Gemini 即時對話額度）
|
||
"""
|
||
is_monthly = '月報' in report_type
|
||
is_strategy = '策略' in report_type
|
||
is_competitor = '競品' in report_type
|
||
is_promo = '促銷' in report_type
|
||
is_vendor = '廠商' in report_type
|
||
is_period = any(k in report_type for k in ('quarterly', 'half_yearly', 'annual', 'ttm', '季報', '半年報', '年報'))
|
||
|
||
# ── 格式鐵律（所有 prompt 共用後綴）────────────────────────
|
||
FORMAT_RULES = (
|
||
"\n\n【輸出格式鐵律 — 絕對遵守】\n"
|
||
"1. 禁止使用任何 Markdown 語法：禁止 **粗體**、*斜體*、`程式碼`、## 標題\n"
|
||
"2. 段落標題用【】全形括號，例如：【整體競爭態勢】\n"
|
||
"3. 開頭直接進入分析內容，禁止「好的」「以下是」「針對您的資料」等 AI 慣用語\n"
|
||
"4. 每個建議條目以 ✅ 開頭\n"
|
||
"5. 繁體中文，語氣專業、精準、業績導向"
|
||
)
|
||
|
||
# ── 2026 台灣電商市場趨勢脈絡（所有 prompt 共用前綴知識）──────
|
||
# 任何 AI 分析必須以此知識為背景，不可違反實際市場趨勢。
|
||
MARKET_TREND_2026 = (
|
||
"\n\n【2026 年台灣電商市場知識基底（必須以此為分析背景）】\n"
|
||
"── 關鍵檔期與品類拉動幅度 ──\n"
|
||
" • 母親節（5 月第 2 週）：年度大促，美妝保養 +30~50%、母嬰 +20~35%\n"
|
||
" • 520 情人節：禮品/香氛/輕珠寶 +25~40%\n"
|
||
" • 618 購物節（年中最大）：全品類 +30~50%，主打囤貨型商品（美妝禮盒、母嬰用品）\n"
|
||
" • 端午節：應景食品 +40~60%、家庭清潔/紙品 +15~25%\n"
|
||
" • 雙11（年度最強）：全品類 +50~80%\n"
|
||
" • 雙12 / 年末：保健食品 +30~50%、年節禮品 +40~60%\n"
|
||
"── 2026 熱門賽道 ──\n"
|
||
" • 永續美妝（無毒/敏弱肌/天然成分）\n"
|
||
" • 母嬰高端化（NT$2000+ 客單帶、日韓品牌、安全認證）\n"
|
||
" • 機能性食品（益生菌、葉黃素、銀髮保健）\n"
|
||
" • IP 聯名（角色聯名增加客單與話題）\n"
|
||
" • 男性保養（從工具型轉精緻型）\n"
|
||
" • 寵物經濟（鮮食、保健、玩具高端化）\n"
|
||
"── 平台競爭態勢 ──\n"
|
||
" • 蝦皮：免運門檻低（NT$99）、直播帶貨強、年輕族群\n"
|
||
" • PChome：3C/家電優勢、24h 到貨、會員忠誠度高\n"
|
||
" • 酷澎：火箭快送、選品精緻、高端客群\n"
|
||
" • momo：生活百貨/美妝強、電視購物頻道整合、會員訂閱推力\n"
|
||
)
|
||
|
||
if is_monthly:
|
||
sys_instruction = (
|
||
"你身兼三職：(1) 資深電商策略顧問（10 年 BCG / 麥肯錫零售諮詢經驗）"
|
||
"(2) momo 平台行銷總監（熟悉台灣電商平台流量分配、廣告投放 ROI、檔期節奏）"
|
||
"(3) 品類採購負責人（精通美妝保養、母嬰、個人清潔等品類的供應鏈與選品邏輯）。\n"
|
||
"你的客戶是 momo 平台的 BU 主管與行銷團隊，他們會用你這份報告做下一步的"
|
||
"庫存決策、廣告預算分配、檔期商品規劃，因此必須給出可直接執行的決策建議，"
|
||
"而非空泛分析。所有判斷必須符合 2026 年台灣電商市場的實際趨勢與消費行為。\n\n"
|
||
"請根據以下月報業績數據與外部市場情報，輸出一份完整月度營運報告，結構嚴格如下：\n\n"
|
||
"【整體業績解讀】（4-5句）\n"
|
||
"引用月業績、訂單、毛利率、客單價四項指標，評估本月整體表現等級（優/良/普/弱），"
|
||
"明確點出最顯著亮點（如品類爆發、客單拉升）與最大警訊（如毛利壓縮、成長放緩）；"
|
||
"與台灣電商市場月均表現比較定位（月成長 5~15% 為健康區間，<5% 偏弱、>20% 強勁）；"
|
||
"若毛利率 <10% 必須點明「毛利偏低、需改善 mix 或議價」。\n\n"
|
||
"【市場趨勢脈絡】（3-4句，本段為新增的關鍵段）\n"
|
||
"結合 2026 年台灣電商當下趨勢，至少觸及以下三項中兩項：\n"
|
||
" (a) 節慶檔期：當月與下月的關鍵檔期（母親節 5/2 週、520、618、雙11、雙12）"
|
||
" 及其對品類拉動的歷史幅度（例如：母親節美妝品類業績通常拉抬 30~50%）\n"
|
||
" (b) 消費行為趨勢：永續美妝、母嬰高端化、IP 聯名、無毒/敏弱肌、機能性食品、"
|
||
" 銀髮保健、寵物經濟、男性保養等熱門賽道；點出哪個趨勢與本月業績共振或背離\n"
|
||
" (c) 平台競爭態勢：與蝦皮、PChome、酷澎的相對位置，特別是「免運門檻」「直播帶貨」"
|
||
" 「會員訂閱」的策略影響\n"
|
||
"本段要讓讀者看完知道「當下市場在發生什麼，我們站在哪裡」。\n\n"
|
||
"【品類結構深度解析】（4-5句）\n"
|
||
"TOP1~2 主力品類：成因（季節 / 檔期 / 商品力 / 廣告投放）+ 該品類在台灣電商整體中的"
|
||
"市場份額位階（例如：美妝在 momo 通常佔 18~22%，本月 X% 屬偏高/偏低）；"
|
||
"成長最快或最具潛力的新興品類，建議是否加碼資源；"
|
||
"若 TOP1 品類佔比 >60% 必須點明「集中度過高、需分散風險」並具體建議下一個應扶植的品類；"
|
||
"若毛利率高的品類佔比偏低，建議調整品類 mix 提升結構毛利。\n\n"
|
||
"【熱銷商品洞察】（3-4句）\n"
|
||
"TOP3 熱銷商品的高業績成因（定價優勢 / 品牌信任 / 廣告推力 / 檔期效應 / 季節剛需）；"
|
||
"說明這些商品對整體客單價與毛利的貢獻或壓力（高業績不等於高毛利）；"
|
||
"識別「新進榜」商品的潛力（值得加碼）與「跌出榜」商品的衰退原因；"
|
||
"建議哪 1 款商品適合做次月主推 hero SKU。\n\n"
|
||
"【MCP 市場情報整合】（3-4句）\n"
|
||
"結合外部市場情報（節日日曆 / 季節情境 / 競品動態），說明當前電商環境對本月業績的"
|
||
"正面/負面影響；指出下月必須卡位的外部機會（具體到檔期與品類組合，例如「618 主打"
|
||
"美妝禮盒套組，瞄準 NT$1500~3000 客單帶」）。\n\n"
|
||
"【行銷與銷售行動建議 — SMART 框架】\n"
|
||
"每段必須符合 SMART：Specific（具體商品/品類）、Measurable（量化目標 %/NT$）、"
|
||
"Achievable（可行動、不空泛）、Relevant（與業績痛點直接相關）、Time-bound（時程）。\n\n"
|
||
"■ 本週立即執行（3 條，以 ✅ 開頭）：庫存補貨 / 廣告投放優化 / 定價或滿額門檻調整 / "
|
||
"下架低毛利長尾。每條須含「商品名/品類 + 量化目標 + 完成期限」。\n"
|
||
"■ 本月優化重點（3 條，以 ✅ 開頭）：品類 mix 調整 / 客單拉升組合 / 毛利改善議價 / "
|
||
"新進榜商品扶植。每條須含「方法 + 預期效益（毛利率 +X% / 客單 +NT$Y / 轉換率 +Z%）」。\n"
|
||
"■ 下月預備部署（3 條，以 ✅ 開頭）：檔期商品規劃 / 預售活動設計 / 廣告預算分配 / "
|
||
"競品阻擊。每條須結合下月關鍵檔期與市場趨勢脈絡，給「卡位時機 + 預估收益」。\n\n"
|
||
"【競爭定位與風險預警】（3-4句）\n"
|
||
"本月在 momo 平台同類賣家中的相對位置（領先 / 並列 / 落後）；"
|
||
"最大三項潛在風險（品類過度集中 / 毛利持續下滑 / 競品價格戰 / 檔期商品庫存不足等）；"
|
||
"對應的「立即啟動」防禦動作（例如：建立次月安全庫存閾值、與 TOP 3 廠商重新議價）。\n\n"
|
||
"要求：每段必須引用至少 2 個具體數字（業績/百分比/排名/客單），全文 900~1200 字，"
|
||
"語氣為資深顧問遞交給 BU 主管的決策報告，不要學術化，要落地。"
|
||
"禁止使用「可能」「也許」「建議考慮」等模糊用詞，要明確「必須做X、目標Y、期限Z」。"
|
||
+ MARKET_TREND_2026
|
||
+ FORMAT_RULES
|
||
)
|
||
max_tokens = 2400
|
||
elif is_promo:
|
||
sys_instruction = (
|
||
"你是資深電商活動行銷策略分析師，擁有 10 年以上台灣電商促銷活動策劃與效益評估實戰經驗，"
|
||
"精通促銷活動 ROI 分析、商品選品策略、消費者行為洞察，深度熟悉美妝、保健、母嬰等品類的"
|
||
"活動規劃模式。\n\n"
|
||
f"請針對以下{report_type}資料，輸出一份完整專業的促銷效益分析報告，結構如下：\n\n"
|
||
"【整體活動評估】（3-4句）\n"
|
||
"以業績成長率、訂單成長率、客單價變化、毛利率表現為核心，綜合評估本次活動效益等級（優良/良好/普通/偏弱/不佳），"
|
||
"點出最顯著的亮點或警訊，並與業界平均促銷效益（10~30%業績提升為正常區間）作比較。\n\n"
|
||
"【關鍵發現】（4-5句）\n"
|
||
"分析活動期間「新晉商品」的表現意義、客單價變化背後的消費行為訊號、"
|
||
"毛利率壓縮或提升的結構性原因，以及訂單與業績成長率的背離（若有）代表什麼。\n\n"
|
||
"【市場信號解讀】（3-4句，本段須結合 2026 當下市場趨勢）\n"
|
||
"(a) 檔期對位：本次活動相對於台灣電商促銷節奏（雙11/母親節/520/618/雙12/年慶）"
|
||
"的時機優劣，以及該檔期歷史拉抬幅度作為對標基準。\n"
|
||
"(b) 品類熱度：當前消費者熱搜賽道（永續美妝、敏弱肌、機能性食品、銀髮保健、IP 聯名、"
|
||
"男性保養、寵物經濟）與本次活動商品的契合度。\n"
|
||
"(c) 價格敏感度：本次客單帶相對該品類的市場常態（如美妝禮盒 NT$1500~3000、母嬰高端"
|
||
"NT$2000+），以及與蝦皮、PChome 的相對位置判斷。\n\n"
|
||
"【方案A：短期優化（活動後1週）】（3條，每條以 ✅ 開頭）\n"
|
||
"針對本次活動結果的立即行動：庫存管理、廣告預算調整、回購引導、售後溝通。\n"
|
||
"每條含具體商品/品類名稱或量化目標。\n\n"
|
||
"【方案B：中期強化（下次活動）】（3條，每條以 ✅ 開頭）\n"
|
||
"針對下次促銷活動的優化方向：商品組合、滿額門檻設計、新晉商品扶植、"
|
||
"跨品類搭配促銷策略，含預期效益（轉換率↑/客單↑/毛利改善）。\n\n"
|
||
"【方案C：長期結構改善（季度層級）】（3條，每條以 ✅ 開頭）\n"
|
||
"從商業模式角度提升促銷健康度：RFM 分群精準投放、忠誠訂閱制降低促銷依賴、"
|
||
"促銷效益基準線 KPI 建立、供應鏈協同提升毛利空間。\n\n"
|
||
"【風險預警】（1-2句）\n"
|
||
"指出最大潛在風險（毛利侵蝕/庫存積壓/價格形象破壞），提出防禦建議。\n\n"
|
||
"要求：每段必須引用至少一個具體數字，全文不超過 600 字，語氣如資深顧問報告。"
|
||
+ MARKET_TREND_2026
|
||
+ FORMAT_RULES
|
||
)
|
||
max_tokens = 1400
|
||
elif is_strategy:
|
||
sys_instruction = (
|
||
"你身兼 (1) 資深電商策略分析師（10 年 BCG / 麥肯錫零售諮詢經驗）"
|
||
"(2) 行銷主管（精通台灣電商 RFM 分群、廣告 ROI、檔期商品規劃）。\n"
|
||
"你的客戶是 momo BU 主管，會用本報告做下週的庫存與行銷預算決策。\n\n"
|
||
f"請針對以下{report_type}資料，輸出一份繁體中文專業策略報告，結構如下：\n\n"
|
||
"【業績解讀】（3-4句）總結核心業績表現，量化點出最關鍵亮點與警訊；"
|
||
"與台灣電商市場月均（5~15% 為健康）作比較定位。\n\n"
|
||
"【市場趨勢脈絡】（3-4句，新增段）\n"
|
||
"結合 2026 年台灣電商當下趨勢，至少觸及兩項：\n"
|
||
" (a) 當期關鍵檔期（母親節 / 520 / 618 / 雙11 / 雙12）對品類拉動幅度\n"
|
||
" (b) 消費行為熱門賽道：永續美妝、母嬰高端化、IP 聯名、敏弱肌、銀髮保健、男性保養\n"
|
||
" (c) 平台競爭：與蝦皮 / PChome / 酷澎的免運門檻、直播帶貨、會員訂閱策略對位\n\n"
|
||
"【策略矩陣分析】（4-5句）解讀加碼/機會/收割/觀察分佈，"
|
||
"點出哪類商品最值得加碼資源（廣告預算、首頁版位、滿額門檻），"
|
||
"成長動因（檔期 / 品牌力 / 廣告 / 搜尋詞排名）為何。\n\n"
|
||
"【行銷與銷售行動建議 — SMART 框架】\n"
|
||
"每條符合 Specific / Measurable / Achievable / Relevant / Time-bound：\n"
|
||
"■ 立即執行（2 條，✅ 開頭）：庫存補貨 / 廣告投放 / 定價或滿額門檻\n"
|
||
"■ 本期強化（2 條，✅ 開頭）：品類 mix / 客單拉升組合 / 新進榜扶植\n"
|
||
"每條須含「商品名/品類 + 量化目標（毛利+X% / 客單+NT$Y / 轉換率+Z%）+ 期限」。\n\n"
|
||
"【風險預警】（2-3句）指出 2~3 項潛在風險（集中度 / 毛利下滑 / 競品價格戰），"
|
||
"對應「立即啟動」防禦動作。\n\n"
|
||
"要求：每段引用至少 2 個具體數字，全文 600~800 字，禁用「可能/也許/建議考慮」。"
|
||
+ MARKET_TREND_2026
|
||
+ FORMAT_RULES
|
||
)
|
||
max_tokens = 1400
|
||
elif is_period:
|
||
sys_instruction = (
|
||
"你身兼三職：(1) 資深電商策略顧問（10 年 BCG / 麥肯錫零售諮詢經驗）"
|
||
"(2) momo BU 主管（決策季度/半年/年度資源分配）"
|
||
"(3) CFO 觀點（看 P&L 結構、毛利歸因、預算 vs 實際）。\n"
|
||
"你的客戶是 momo 高層管理層，會用本報告做下季度/半年度/年度的"
|
||
"戰略校正、資源重分配、OKR 設定。所有判斷必須有量化依據。\n\n"
|
||
f"請針對以下{report_type}資料，輸出戰略級期間回顧報告，結構嚴格如下：\n\n"
|
||
"【期間整體解讀】（4-5句）\n"
|
||
"引用期間業績、訂單、毛利率、客單價，評估等級（卓越/穩健/普通/警訊）；"
|
||
"與上期 / 去年同期分別作 △% 比較；指出最關鍵亮點與最大警訊；"
|
||
"與台灣電商市場同期表現比較定位（健康成長 5~15%、強勁 >20%、警訊 <0%）。\n\n"
|
||
"【市場趨勢與本期對位】（3-4句）\n"
|
||
"結合本期所跨檔期（依期間落點：母親節/520/618/雙11/雙12）回顧檔期效益，"
|
||
"對比歷史拉動幅度（如雙11 +50~80%、618 +30~50%）；"
|
||
"點出本期是否充分捕捉市場紅利或錯失機會。\n\n"
|
||
"【月度走勢分析】（3-4句）\n"
|
||
"解讀月度業績曲線：高點月成因（檔期/活動/季節）、低點月成因；"
|
||
"識別連續 2 個月以上的趨勢（持續上升/下降/震盪）；"
|
||
"QoQ / HoH / YoY 的成長動能差異。\n\n"
|
||
"【品類與商品結構洞察】（3-4句）\n"
|
||
"TOP3 主力品類佔比與健康度（前一品類 >60% 為集中度過高）；"
|
||
"新進榜商品 vs 跌出榜商品的比例與業績規模；"
|
||
"毛利結構（高毛利品類 vs 低毛利品類）的 mix 健康度。\n\n"
|
||
"【行動建議 — 戰略級 SMART】\n"
|
||
"■ 下期立即啟動（3 條，✅ 開頭，含期限）：\n"
|
||
" 針對庫存補貨、廣告投放、定價調整、品類 mix 調整等 30 天內可見效的決策。\n"
|
||
"■ 下期戰略重點（3 條，✅ 開頭，含 60-90 天目標）：\n"
|
||
" 針對品類 mix、商品組合、廠商議價、會員活動等 1 季可改善的結構性議題。\n"
|
||
"■ 下下期預備佈局（2 條，✅ 開頭，含 6-12 個月目標）：\n"
|
||
" 針對年度大檔（雙11/雙12/618）、新品類進入、自有品牌、平台戰略等長期議題。\n"
|
||
"每條必須含「具體商品/品類 + 量化目標（業績 +X% / 毛利 +Y pp / 客單 +NT$Z）+ 期限」。\n\n"
|
||
"【最大三大風險與防禦】（2-3句）\n"
|
||
"點出 3 項最大潛在風險（集中度 / 毛利下滑 / 競品價格戰 / 庫存積壓 / 廠商斷供 等），"
|
||
"對應「立即啟動」防禦動作（具體至：建立 N 天安全庫存 / 與 TOP3 簽 N 年協議）。\n\n"
|
||
"要求：每段引用至少 2 個具體數字，全文 1000~1300 字，"
|
||
"語氣為資深顧問遞交給 BU 主管/CEO 的戰略決策報告，禁用模糊用詞，要明確期限與量化。"
|
||
+ MARKET_TREND_2026
|
||
+ FORMAT_RULES
|
||
)
|
||
max_tokens = 2600
|
||
elif is_vendor:
|
||
sys_instruction = (
|
||
"你身兼 (1) 資深採購主管（10 年零售/電商採購實戰經驗，精通議價、選品、"
|
||
"獨家代理談判）(2) 供應鏈管理顧問（精通 Pareto 集中度風險、安全庫存、"
|
||
"雙源頭策略）。\n"
|
||
"你的客戶是 momo BU 採購主管，會用本報告做下季度的議價對象、新廠商扶植、"
|
||
"備援名單、毛利改善決策。\n\n"
|
||
f"請針對以下{report_type}資料，輸出採購策略視角的分析報告，結構嚴格如下：\n\n"
|
||
"【整體廠商結構解讀】（4-5 句）\n"
|
||
"引用廠商總數、合計業績、合計毛利、平均毛利率，評估廠商組合健康度（健康/警訊）；"
|
||
"點出最關鍵亮點（TOP1 廠商業績/毛利、新進榜潛力廠商）與最大警訊"
|
||
"（集中度過高、毛利持續下滑、長尾過多）；"
|
||
"與業界平均（健康電商前 20% 廠商佔 70~80% 業績為合理區間）作比較定位。\n\n"
|
||
"【集中度與供應風險評估】（4-5 句）\n"
|
||
"(a) Pareto 80/20 分析：前 N 家廠商佔 80% 業績的具體比例，是否健康分散\n"
|
||
"(b) TOP3 廠商斷供風險：若 TOP1 廠商斷供，影響業績幾 %？是否有備援？\n"
|
||
"(c) 長尾廠商價值：後 50% 廠商是新晉/補充/淘汰候選？毛利是否優於 TOP？\n"
|
||
"(d) 與上期比較：廠商總數變化、新進榜（🆕）vs 跌出榜的數量\n\n"
|
||
"【議價優先順序與毛利改善】（4-5 句）\n"
|
||
"(a) 第一線議價對象：TOP3 中毛利最低的，明確點名 + 議價方向（量價折扣 / "
|
||
"獨家代理 / 行銷費用協同）\n"
|
||
"(b) 高毛利廠商扶植：毛利率 >15% 但業績佔比偏低的，建議加碼資源（首頁版位、"
|
||
"廣告預算）\n"
|
||
"(c) 低毛利長尾汰換：毛利 <5% 且業績後段，建議下架或重新議價\n\n"
|
||
"【行動建議 — SMART 框架（必須 SMART）】\n"
|
||
"■ 立即執行（3 條，✅ 開頭）：\n"
|
||
" ✅ 議價：對 [具體廠商名] 啟動 Q+1 議價，目標毛利 +X pp，期限：YYYY/MM/DD\n"
|
||
" ✅ 備援：為 TOP3 廠商建立備援名單（同品類至少 1 家替代廠商）\n"
|
||
" ✅ 汰換：[具體廠商名] 毛利 X%、業績 Y 萬，建議下季度下架或重新議約\n"
|
||
"■ 中期強化（3 條，✅ 開頭）：\n"
|
||
" ✅ 新廠商開發：針對 [具體品類] 開發 N 家新廠商，下季度預期業績貢獻 NT$X 萬\n"
|
||
" ✅ 獨家代理談判：[具體廠商] 爭取台灣電商獨家權，預期市佔 +X%\n"
|
||
" ✅ 廠商分級制度：建立 A/B/C 分級（依業績+毛利+穩定度），每季調整資源分配\n"
|
||
"■ 長期結構（2 條，✅ 開頭）：\n"
|
||
" ✅ 集中度目標：12 個月內把前 5 家佔比從 X% 降至 Y%（風險分散）\n"
|
||
" ✅ 自有品牌（OEM/ODM）：規劃 [具體品類] 自有品牌，毛利目標 30%+\n\n"
|
||
"【最大風險與防禦動作】（2-3 句）\n"
|
||
"指出 2~3 項最大風險（單點供應斷鏈 / 競品挖角獨家廠商 / 進口匯率波動），"
|
||
"對應「立即啟動」防禦動作（具體至：建立 N 天安全庫存、與 TOP3 簽 N 年協議）。\n\n"
|
||
"要求：每段引用至少 2 個具體數字（廠商名 / 業績 / 毛利率 / 排名變化），"
|
||
"全文 800~1000 字，禁用「可能/也許/建議考慮」模糊用詞。"
|
||
+ MARKET_TREND_2026
|
||
+ FORMAT_RULES
|
||
)
|
||
max_tokens = 1800
|
||
elif is_competitor:
|
||
sys_instruction = (
|
||
"你是資深電商競品策略分析師，專精美妝（開架/專櫃）、保健食品、母嬰用品，"
|
||
"具備 10 年以上台灣電商市場實戰經驗，深度熟悉 PChome、momo 競爭生態、"
|
||
"台灣消費者行為與美妝/保健品類購買決策模式。\n\n"
|
||
"═══════════════════════════════\n"
|
||
"角色設定（絕對不可違反）\n"
|
||
" 我方 = PChome 競品 = momo\n"
|
||
" price_diff = momo售價 − PChome售價\n"
|
||
" 正值 → momo偏貴 → PChome定價優勢 → 加強曝光與轉換\n"
|
||
" 負值 → momo較便宜 → 競品威脅 → 研擬差異化因應策略\n"
|
||
"═══════════════════════════════\n\n"
|
||
f"請以 PChome 視角，針對以下{report_type}輸出一份專業競品分析報告：\n\n"
|
||
"【整體競爭態勢】（3-4句）\n"
|
||
"從 PChome 角度評估本期整體定價競爭力，引用平均價差百分比，"
|
||
"指出最關鍵亮點與警訊，並結合美妝/保健/母嬰市場脈動說明意涵。\n\n"
|
||
"【PChome 定價優勢商品深度解析】（4-5句）\n"
|
||
"點名具體優勢商品與品類（如施巴、ESTEE LAUDER 等），"
|
||
"推測 PChome 具優勢的可能原因（進貨量優勢、活動定價、品牌授權），"
|
||
"給出具體的曝光放大策略：首頁置頂、搜尋關鍵字投放、會員專案推廣。\n\n"
|
||
"【momo 威脅商品應對方案】（4-5句）\n"
|
||
"點名具體威脅商品，判斷 momo 低價策略來源（平台補貼/獨家代理/批量進貨），"
|
||
"提出 PChome 差異化應對：加值服務（快速到貨/禮盒包裝）、"
|
||
"組合促銷、PChome 幣回饋、VIP 會員專屬折扣，"
|
||
"避免純粹降價而犧牲毛利。\n\n"
|
||
"【美妝/保健/母嬰品類專項洞察】（3-4句）\n"
|
||
"針對本期出現的具體商品，結合台灣市場趨勢深度分析：\n"
|
||
"美妝：成分透明化趨勢、敏感肌/無添加需求、社群口碑行銷；\n"
|
||
"保健：機能性訴求、族群細分（銀髮/運動/女性）、定期訂購轉換；\n"
|
||
"母嬰：安全認證標章、日韓品牌偏好、媽媽社群影響力。\n\n"
|
||
"【本期 TOP3 業績導向行動建議】（3條，每條以 ✅ 開頭）\n"
|
||
"每條包含：具體商品或品類 + 行動方向 + 預期業績效益（轉換率↑/客單價↑/市佔↑）。\n\n"
|
||
"要求：每段必須引用至少一個具體數字或商品名，不超過 500 字，語氣如資深顧問報告。"
|
||
+ MARKET_TREND_2026
|
||
+ FORMAT_RULES
|
||
)
|
||
max_tokens = 1200
|
||
else:
|
||
# daily / weekly 走這條：精煉版顧問報告（時間範圍小，不需深度市場分析，但仍含趨勢脈絡）
|
||
sys_instruction = (
|
||
"你是資深電商營運顧問（10 年台灣電商實戰經驗），擅長從短期業績抓出可執行的"
|
||
"戰術建議。客戶是 momo BU 主管，會用本報告做今日/本週的庫存與廣告調整。\n\n"
|
||
f"請針對以下{report_type}資料，輸出簡潔但專業的分析與行動建議，結構如下：\n\n"
|
||
"【整體業績解讀】（2-3句）\n"
|
||
"引用業績、訂單、毛利率、客單價，評估等級（優/良/普/弱），點出最關鍵的亮點與警訊。\n\n"
|
||
"【市場機會與風險】（2-3句）\n"
|
||
"結合當前檔期（母親節/520/618/雙11/雙12 等），說明本期業績所受影響"
|
||
"與下一個應卡位的時機；指出潛在風險（庫存/競品/毛利）。\n\n"
|
||
"【TOP3 立即行動建議】（3 條，✅ 開頭）\n"
|
||
"每條符合 SMART：具體商品/品類 + 量化目標（補貨 N 組/廣告 +X%/客單 +NT$Y）+ 期限。\n\n"
|
||
"要求：每段引用具體數字，全文 350~450 字，禁用「可能/也許」模糊用詞。"
|
||
+ MARKET_TREND_2026
|
||
+ FORMAT_RULES
|
||
)
|
||
max_tokens = 900
|
||
|
||
def _call_gemini(prompt: str, tokens: int) -> str:
|
||
r = requests.post(
|
||
f"{GEMINI_BASE_URL}/{GEMINI_MODEL}:generateContent?key={GEMINI_API_KEY}",
|
||
headers={'Content-Type': 'application/json'},
|
||
json={
|
||
'contents': [{'parts': [{'text': prompt}]}],
|
||
'generationConfig': {'maxOutputTokens': tokens, 'temperature': 0.35},
|
||
},
|
||
timeout=40,
|
||
)
|
||
r.raise_for_status()
|
||
return (r.json().get('candidates', [{}])[0]
|
||
.get('content', {}).get('parts', [{}])[0]
|
||
.get('text', '').strip())
|
||
|
||
if not NVIDIA_API_KEY:
|
||
if GEMINI_API_KEY:
|
||
try:
|
||
raw = _call_gemini(f"{sys_instruction}\n\n--- 資料 ---\n{prompt_data}", max_tokens)
|
||
return _clean_ai_text(raw)
|
||
except Exception as e:
|
||
sys_log.warning(f"[PPT] Gemini error: {e}")
|
||
return '（AI 分析暫不可用，請確認 API Key 設定）'
|
||
|
||
# ── NIM (快速失敗 20s，失敗後 fallback Gemini) ────────────
|
||
try:
|
||
r = requests.post(
|
||
f'{NVIDIA_BASE_URL}/chat/completions',
|
||
headers={'Authorization': f'Bearer {NVIDIA_API_KEY}',
|
||
'Content-Type': 'application/json'},
|
||
json={
|
||
'model': CHAT_MODEL,
|
||
'messages': [
|
||
{'role': 'system', 'content': sys_instruction},
|
||
{'role': 'user', 'content': f'--- 資料 ---\n{prompt_data}'},
|
||
],
|
||
'max_tokens': max_tokens,
|
||
'temperature': 0.35,
|
||
},
|
||
timeout=25,
|
||
)
|
||
r.raise_for_status()
|
||
result_text = _clean_ai_text(r.json()['choices'][0]['message']['content'])
|
||
# ── PPT 分析自動入知識庫 ──────────────────────────────
|
||
if _LEARNING_ENABLED and result_text:
|
||
import threading as _thr
|
||
_thr.Thread(
|
||
target=store_insight,
|
||
kwargs={
|
||
'insight_type': report_type or 'analysis',
|
||
'content': result_text,
|
||
'period': datetime.now(TAIPEI_TZ).strftime('%Y-%m-%d'),
|
||
},
|
||
daemon=True
|
||
).start()
|
||
return result_text
|
||
except Exception as e:
|
||
sys_log.warning(f"[PPT] NIM unavailable ({type(e).__name__}), fallback Gemini")
|
||
|
||
# ── Gemini fallback ───────────────────────────────────────
|
||
if GEMINI_API_KEY:
|
||
try:
|
||
raw = _call_gemini(f"{sys_instruction}\n\n--- 資料 ---\n{prompt_data}", max_tokens)
|
||
result_text = _clean_ai_text(raw)
|
||
if _LEARNING_ENABLED and result_text:
|
||
import threading as _thr
|
||
_thr.Thread(
|
||
target=store_insight,
|
||
kwargs={
|
||
'insight_type': report_type or 'analysis',
|
||
'content': result_text,
|
||
'period': datetime.now(TAIPEI_TZ).strftime('%Y-%m-%d'),
|
||
},
|
||
daemon=True
|
||
).start()
|
||
return result_text
|
||
except Exception as e2:
|
||
sys_log.error(f"[PPT] Gemini fallback error: {e2}")
|
||
return '（AI 分析暫時無法使用，請稍後重試）'
|
||
|
||
|
||
def _ppt_needs_fallback(ai_text: str) -> bool:
|
||
if not ai_text:
|
||
return True
|
||
weak_markers = ('AI 分析暫', '暫無 AI 分析', '生成中', '請稍後重試')
|
||
return any(marker in ai_text for marker in weak_markers) or len(ai_text.strip()) < 40
|
||
|
||
|
||
def _ppt_fallback_insight(report_type: str, data_summary: str, mcp_text: str = '') -> str:
|
||
"""模型額度或外部 API 失敗時，仍用既有 DB 摘要補足簡報內容。"""
|
||
lines = [
|
||
f"【{report_type}重點摘要】",
|
||
data_summary[:1200],
|
||
]
|
||
if mcp_text:
|
||
lines.extend(["", "【外部情報補充】", mcp_text[:600]])
|
||
lines.extend([
|
||
"",
|
||
"【建議動作】",
|
||
"1. 先檢查高業績商品與高毛利商品是否重疊，優先補強可放大的品項。",
|
||
"2. 針對低毛利或異常波動商品，回看價格、促銷與庫存狀態。",
|
||
"3. 若本頁顯示資料不足，請先確認該日期/月份業績資料是否已完成匯入。",
|
||
])
|
||
return "\n".join(lines)
|
||
|
||
|
||
class PPTDataInsufficientError(Exception):
|
||
"""ADR-019 Phase 1：請求的 PPT 期間缺資料。raise 前已主動 inline-keyboard 詢問用戶。"""
|
||
pass
|
||
|
||
|
||
def _ppt_check_data_freshness(report_type: str, chat_id: int, reply_to: int,
|
||
requested_yr: int = None, requested_mo: int = None,
|
||
requested_date: str = None) -> None:
|
||
"""ADR-019 Phase 1：PPT 生成前 probe 資料新鮮度。資料缺口時主動 inline keyboard
|
||
詢問用戶（自訂日期 / 改看最新 / 取消），並 raise PPTDataInsufficientError 中止流程。
|
||
|
||
daily / strategy(單日)：傳 requested_date='YYYY/MM/DD'
|
||
monthly：傳 requested_yr, requested_mo
|
||
"""
|
||
latest = latest_date() # 'YYYY-MM-DD' 或 None
|
||
if not latest:
|
||
return # DB 連不到，原樣繼續，由 caller 處理
|
||
|
||
try:
|
||
latest_dt = datetime.strptime(latest.replace('/', '-'), '%Y-%m-%d')
|
||
except (ValueError, AttributeError):
|
||
return
|
||
|
||
if report_type in ('monthly', '月報') and requested_yr and requested_mo:
|
||
has_data = (latest_dt.year > requested_yr or
|
||
(latest_dt.year == requested_yr and latest_dt.month >= requested_mo))
|
||
if not has_data:
|
||
prev_yr, prev_mo = latest_dt.year, latest_dt.month
|
||
kb = [
|
||
_row((f'📊 改看 {prev_yr}/{prev_mo:02d} 月報',
|
||
f'cmd:ppt:monthly {prev_yr}/{prev_mo:02d}')),
|
||
_row(('📅 自訂月份', 'await:date_ppt_monthly')),
|
||
_row(('❌ 取消', 'menu:reports')),
|
||
]
|
||
send_message(
|
||
chat_id,
|
||
f"⚠️ *{requested_yr}/{requested_mo:02d}* 尚無業績資料\n"
|
||
f"目前最新資料截至：`{latest}`\n\n請選擇：",
|
||
reply_to,
|
||
keyboard=kb,
|
||
parse_mode='Markdown',
|
||
)
|
||
raise PPTDataInsufficientError(f'monthly {requested_yr}/{requested_mo:02d}')
|
||
|
||
elif report_type in ('daily', '日報', 'strategy', '策略', 'weekly', '週報') and requested_date:
|
||
sales = query_sales(requested_date)
|
||
if not sales.get('found'):
|
||
kb = [
|
||
_row((f'📊 改看 {latest} 日報', f'cmd:ppt:{report_type or "daily"} {latest}')),
|
||
_row(('📅 自訂日期', 'await:date_ppt_daily')),
|
||
_row(('❌ 取消', 'menu:reports')),
|
||
]
|
||
send_message(
|
||
chat_id,
|
||
f"⚠️ *{requested_date}* 尚無業績資料\n"
|
||
f"目前最新資料：`{latest}`\n\n請選擇：",
|
||
reply_to,
|
||
keyboard=kb,
|
||
parse_mode='Markdown',
|
||
)
|
||
raise PPTDataInsufficientError(f'{report_type} {requested_date}')
|
||
|
||
|
||
def _normalize_ppt_parameters(parameters: dict) -> str:
|
||
"""將快取參數轉成穩定字串。
|
||
自動把當前 ppt_generator 的模板版本字串 (tpl_ver) 併入 parameters，
|
||
任何 report_type 模板升級（bump TEMPLATE_VERSIONS）→ 舊快取全部 miss → 重新生成。
|
||
"""
|
||
try:
|
||
from services.ppt_generator import get_template_version
|
||
report_type = parameters.get('report_type') if isinstance(parameters, dict) else None
|
||
if report_type:
|
||
params_with_ver = dict(parameters)
|
||
params_with_ver['tpl_ver'] = get_template_version(report_type)
|
||
return json.dumps(params_with_ver, ensure_ascii=False, sort_keys=True, separators=(',', ':'))
|
||
return json.dumps(parameters, ensure_ascii=False, sort_keys=True, separators=(',', ':'))
|
||
except Exception:
|
||
try:
|
||
return json.dumps(parameters, ensure_ascii=False, sort_keys=True, separators=(',', ':'))
|
||
except Exception:
|
||
return json.dumps({}, ensure_ascii=False)
|
||
|
||
|
||
def _invalidate_ppt_cache(report_type: str = None) -> int:
|
||
"""強制失效指定 report_type（或全部）的 PPT 快取。
|
||
將 expires_at 設為 NOW() − 1 分鐘，下次查詢即 miss → 用最新模板重生。
|
||
回傳被影響的列數。
|
||
|
||
用法（管理員）:
|
||
_invalidate_ppt_cache('monthly') # 只清月報
|
||
_invalidate_ppt_cache() # 清全部
|
||
"""
|
||
from database.manager import DatabaseManager
|
||
from database.ppt_reports import PPTReport
|
||
|
||
now = datetime.now(TAIPEI_TZ).replace(tzinfo=None)
|
||
expired_at = now - timedelta(minutes=1)
|
||
session = DatabaseManager().get_session()
|
||
try:
|
||
q = session.query(PPTReport).filter(
|
||
or_(PPTReport.expires_at.is_(None), PPTReport.expires_at > now)
|
||
)
|
||
if report_type:
|
||
q = q.filter(PPTReport.report_type == report_type)
|
||
affected = q.update({PPTReport.expires_at: expired_at}, synchronize_session=False)
|
||
session.commit()
|
||
sys_log.info(f"[PPT] 強制失效快取 type={report_type or 'ALL'} affected={affected}")
|
||
return int(affected or 0)
|
||
except Exception as e:
|
||
session.rollback()
|
||
sys_log.error(f"[PPT] 失效快取失敗: {e}")
|
||
return 0
|
||
finally:
|
||
session.close()
|
||
|
||
|
||
def cleanup_expired_ppt_cache(days_old: int = 7, dry_run: bool = True) -> dict:
|
||
"""清理已過期且超過 days_old 天的 PPT 檔案 + DB 紀錄。
|
||
執行條件：expires_at < NOW() − days_old 天 → 刪檔 + 刪 row。
|
||
保留 days_old 天緩衝避免誤刪剛失效的紀錄。
|
||
|
||
安全預設：**dry_run=True**（critic 修正 HIGH-2，避免靜默實刪）。
|
||
呼叫方必須明確傳 dry_run=False 才會真正刪除。
|
||
launchd / cron 排程務必顯式傳：
|
||
cleanup_expired_ppt_cache(days_old=7, dry_run=False)
|
||
|
||
回傳統計欄位語意（critic Info-1）：
|
||
dry_run=True 時：deleted_files / deleted_rows / freed_bytes 為「將刪除」預估值
|
||
dry_run=False 時：上述為「已實刪」實際值
|
||
errors 為過程中發生例外的列表（id + 錯誤訊息）
|
||
"""
|
||
from database.manager import DatabaseManager
|
||
from database.ppt_reports import PPTReport
|
||
|
||
cutoff = datetime.now(TAIPEI_TZ).replace(tzinfo=None) - timedelta(days=days_old)
|
||
stat = {'deleted_files': 0, 'deleted_rows': 0,
|
||
'freed_bytes': 0, 'errors': [], 'dry_run': dry_run}
|
||
|
||
session = DatabaseManager().get_session()
|
||
try:
|
||
rows = (session.query(PPTReport)
|
||
.filter(PPTReport.expires_at.isnot(None),
|
||
PPTReport.expires_at < cutoff)
|
||
.all())
|
||
for r in rows:
|
||
try:
|
||
if r.file_path and os.path.exists(r.file_path):
|
||
size = os.path.getsize(r.file_path)
|
||
if not dry_run:
|
||
os.unlink(r.file_path)
|
||
stat['deleted_files'] += 1
|
||
stat['freed_bytes'] += size
|
||
if not dry_run:
|
||
session.delete(r)
|
||
stat['deleted_rows'] += 1
|
||
except Exception as e:
|
||
stat['errors'].append(f"id={r.id}: {e}")
|
||
if not dry_run:
|
||
session.commit()
|
||
sys_log.info(f"[PPT cleanup] {stat}")
|
||
return stat
|
||
except Exception as e:
|
||
session.rollback()
|
||
sys_log.error(f"[PPT cleanup] 失敗: {e}")
|
||
stat['errors'].append(str(e))
|
||
return stat
|
||
finally:
|
||
session.close()
|
||
|
||
|
||
def _load_cached_ppt_entry(report_type: str, parameters: dict):
|
||
"""回傳尚未過期的快取紀錄與解析後 payload，若無則回傳 (None, {})."""
|
||
from database.manager import DatabaseManager
|
||
from database.ppt_reports import PPTReport
|
||
|
||
now = datetime.now(TAIPEI_TZ).replace(tzinfo=None)
|
||
params = _normalize_ppt_parameters(parameters)
|
||
session = DatabaseManager().get_session()
|
||
try:
|
||
cached = (
|
||
session.query(PPTReport)
|
||
.filter(
|
||
PPTReport.report_type == report_type,
|
||
PPTReport.parameters == params,
|
||
or_(PPTReport.expires_at.is_(None), PPTReport.expires_at > now),
|
||
)
|
||
.order_by(PPTReport.generated_at.desc())
|
||
.first()
|
||
)
|
||
if not cached:
|
||
return None, {}
|
||
|
||
cached_payload = {}
|
||
if cached.cached_data:
|
||
try:
|
||
cached_payload = json.loads(cached.cached_data)
|
||
if not isinstance(cached_payload, dict):
|
||
cached_payload = {}
|
||
except Exception as e:
|
||
sys_log.warning(f"[PPT] cached_data 解析失敗: {e}")
|
||
cached_payload = {}
|
||
return cached, cached_payload
|
||
except Exception as e:
|
||
session.rollback()
|
||
sys_log.warning(f"[PPT] 讀取快取失敗：{e}")
|
||
finally:
|
||
session.close()
|
||
return None, {}
|
||
|
||
|
||
def _load_cached_ppt_path(report_type: str, parameters: dict) -> str | None:
|
||
"""嘗試回傳尚未過期且仍存在的快取檔案。"""
|
||
path, _ = _load_cached_ppt_path_and_analysis(report_type, parameters)
|
||
if path:
|
||
return path
|
||
return None
|
||
|
||
|
||
def _load_cached_ppt_analysis(report_type: str, parameters: dict) -> str | None:
|
||
"""回傳快取中的 AI 分析文字（可直接寫入簡報）。"""
|
||
_, cached_analysis = _load_cached_ppt_path_and_analysis(report_type, parameters)
|
||
if not cached_analysis or not isinstance(cached_analysis, str):
|
||
return None
|
||
return cached_analysis.strip() if cached_analysis else None
|
||
|
||
|
||
def _load_cached_ppt_path_and_analysis(report_type: str, parameters: dict):
|
||
"""回傳快取檔案路徑與 AI 分析文字。"""
|
||
cached, payload = _load_cached_ppt_entry(report_type, parameters)
|
||
cached_path = (
|
||
cached.file_path if cached and cached.file_path and os.path.exists(cached.file_path) else None
|
||
)
|
||
cached_analysis = payload.get('analysis') if isinstance(payload, dict) else None
|
||
return cached_path, cached_analysis
|
||
|
||
|
||
def _store_ppt_cache(report_type: str, parameters: dict, file_path: str, cached_payload: dict) -> str | None:
|
||
"""儲存 PPT 快取資料，回傳 file_path。"""
|
||
from database.manager import DatabaseManager
|
||
from database.ppt_reports import PPTReport
|
||
|
||
now = datetime.now(TAIPEI_TZ)
|
||
params = _normalize_ppt_parameters(parameters)
|
||
expire_at = now + timedelta(hours=PPT_CACHE_TTL_HOURS)
|
||
|
||
try:
|
||
cached_data = json.dumps(cached_payload, ensure_ascii=False)
|
||
except Exception:
|
||
cached_data = json.dumps({'error': 'cached_data serialization failed'}, ensure_ascii=False)
|
||
|
||
try:
|
||
file_size = os.path.getsize(file_path)
|
||
except OSError:
|
||
file_size = None
|
||
|
||
session = DatabaseManager().get_session()
|
||
try:
|
||
cached = (
|
||
session.query(PPTReport)
|
||
.filter(PPTReport.report_type == report_type, PPTReport.parameters == params)
|
||
.order_by(PPTReport.generated_at.desc())
|
||
.first()
|
||
)
|
||
if cached:
|
||
cached.file_path = file_path
|
||
cached.file_size = file_size
|
||
cached.generated_at = now.replace(tzinfo=None)
|
||
cached.expires_at = expire_at.replace(tzinfo=None)
|
||
cached.cached_data = cached_data
|
||
else:
|
||
session.add(PPTReport(
|
||
report_type=report_type,
|
||
parameters=params,
|
||
file_path=file_path,
|
||
file_size=file_size,
|
||
generated_at=now.replace(tzinfo=None),
|
||
expires_at=expire_at.replace(tzinfo=None),
|
||
cached_data=cached_data,
|
||
))
|
||
session.commit()
|
||
return file_path
|
||
except Exception as e:
|
||
session.rollback()
|
||
sys_log.warning(f"[PPT] 快取寫入失敗：{e}")
|
||
return None
|
||
finally:
|
||
session.close()
|
||
|
||
|
||
def _is_cached_ppt_file(file_path: str) -> bool:
|
||
"""判斷傳入檔案是否已被快取，避免傳送後刪除。"""
|
||
if not file_path:
|
||
return False
|
||
from database.manager import DatabaseManager
|
||
from database.ppt_reports import PPTReport
|
||
|
||
now = datetime.now(TAIPEI_TZ)
|
||
now = now.replace(tzinfo=None)
|
||
session = DatabaseManager().get_session()
|
||
try:
|
||
cached = (
|
||
session.query(PPTReport)
|
||
.filter(PPTReport.file_path == file_path)
|
||
.filter(or_(PPTReport.expires_at.is_(None), PPTReport.expires_at > now))
|
||
.first()
|
||
)
|
||
return cached is not None
|
||
except Exception:
|
||
return False
|
||
finally:
|
||
session.close()
|
||
|
||
|
||
def _fetch_mcp_context() -> str:
|
||
"""MCP 失敗時回空字串，避免阻塞。"""
|
||
try:
|
||
return build_mcp_context('', [])
|
||
except Exception:
|
||
return ''
|
||
|
||
|
||
def _generate_ppt_cmd(sub_type: str, sub_arg: str, _chat_id: int, target: str,
|
||
_reply_to: int = None) -> str:
|
||
"""依 sub_type 生成對應 pptx，回傳檔案路徑。
|
||
|
||
ADR-019 Phase 1：在生成前 probe 資料新鮮度，缺資料時 raise
|
||
PPTDataInsufficientError（已主動詢問用戶），由 _ppt_background 靜默吞掉。
|
||
"""
|
||
try:
|
||
from services.ppt_generator import (
|
||
generate_daily_ppt, generate_weekly_ppt,
|
||
generate_monthly_ppt, generate_strategy_ppt,
|
||
generate_competitor_ppt, generate_promo_ppt,
|
||
generate_vendor_ppt, generate_period_review_ppt,
|
||
check_pptx_available
|
||
)
|
||
except ImportError:
|
||
raise RuntimeError("ppt_generator 模組不可用，請確認 python-pptx 已安裝")
|
||
|
||
if not check_pptx_available():
|
||
raise RuntimeError("python-pptx 未安裝，請執行：pip install python-pptx")
|
||
|
||
now = datetime.now(TAIPEI_TZ)
|
||
|
||
if sub_type in ('daily', '日報'):
|
||
if sub_arg and re.fullmatch(r'\d{4}[/-]\d{1,2}[/-]\d{1,2}', sub_arg):
|
||
date_str = normalize_date(sub_arg)
|
||
else:
|
||
date_str = latest_date() or now.strftime('%Y/%m/%d')
|
||
|
||
_ppt_check_data_freshness('daily', _chat_id, _reply_to, requested_date=date_str)
|
||
|
||
params = {'report_type': 'daily', 'date': date_str}
|
||
cached, cached_ai = _load_cached_ppt_path_and_analysis('daily', params)
|
||
if cached:
|
||
return cached
|
||
|
||
mcp_text = ''
|
||
if not cached_ai:
|
||
mcp_text = _fetch_mcp_context()
|
||
sales = query_sales(date_str)
|
||
top_products = query_top_products(date_str, 10)
|
||
top_vendors = query_top_vendors(date_str, 5)
|
||
weekly = query_weekly_trend()
|
||
|
||
data_summary = (
|
||
f"日期：{date_str}\n"
|
||
f"業績：NT$ {float(sales.get('revenue', 0)):,.0f} | "
|
||
f"訂單：{sales.get('orders', '-')}筆 | 毛利率：{sales.get('gross_margin', 0):.1f}%\n"
|
||
f"熱銷商品：" + " / ".join(
|
||
f"{p['name']}(NT${p['revenue']:,.0f})" for p in top_products[:5]) + "\n"
|
||
f"外部情報：{mcp_text[:500]}"
|
||
)
|
||
ai_text = cached_ai or _ppt_ai_analysis(data_summary, '日報')
|
||
if not cached_ai and _ppt_needs_fallback(ai_text):
|
||
ai_text = _ppt_fallback_insight('日報', data_summary, mcp_text)
|
||
db_data = {
|
||
'sales': sales, 'top_products': top_products,
|
||
'top_vendors': top_vendors, 'weekly': weekly, 'mcp': mcp_text,
|
||
}
|
||
ppt_path = generate_daily_ppt(date_str, db_data, ai_text)
|
||
_store_ppt_cache('daily', params, ppt_path, {
|
||
'report_type': 'daily',
|
||
'parameters': params,
|
||
'data_summary': data_summary,
|
||
'analysis': ai_text,
|
||
'mcp': mcp_text,
|
||
})
|
||
return ppt_path
|
||
|
||
elif sub_type in ('weekly', '週報'):
|
||
end_str = latest_date() or now.strftime('%Y/%m/%d')
|
||
_ppt_check_data_freshness('weekly', _chat_id, _reply_to, requested_date=end_str)
|
||
|
||
params = {'report_type': 'weekly'}
|
||
cached, cached_ai = _load_cached_ppt_path_and_analysis('weekly', params)
|
||
if cached:
|
||
return cached
|
||
|
||
mcp_text = ''
|
||
if not cached_ai:
|
||
mcp_text = _fetch_mcp_context()
|
||
weekly = query_weekly_trend()
|
||
top_products = query_top_products(target, 10)
|
||
top_vendors = query_top_vendors(target, 10)
|
||
strat = analyze_product_strategy(target, 10)
|
||
|
||
data_summary = (
|
||
f"週期：{now.strftime('%Y/%m/%d')} 週報\n"
|
||
f"週業績：NT$ {sum(w['revenue'] for w in weekly):,.0f}\n"
|
||
f"逐日：" + " | ".join(f"{w['date']}(NT${w['revenue']:,.0f})" for w in weekly) + "\n"
|
||
f"熱銷：" + " / ".join(f"{p['name']}(NT${p['revenue']:,.0f})" for p in top_products[:5]) + "\n"
|
||
f"外部情報：{mcp_text[:500]}"
|
||
)
|
||
ai_text = cached_ai or _ppt_ai_analysis(data_summary, '週報')
|
||
if not cached_ai and _ppt_needs_fallback(ai_text):
|
||
ai_text = _ppt_fallback_insight('週報', data_summary, mcp_text)
|
||
db_data = {
|
||
'weekly': weekly, 'top_products': top_products,
|
||
'top_vendors': top_vendors, 'strategy': strat, 'mcp': mcp_text,
|
||
}
|
||
ppt_path = generate_weekly_ppt(db_data, ai_text)
|
||
_store_ppt_cache('weekly', params, ppt_path, {
|
||
'report_type': 'weekly',
|
||
'parameters': params,
|
||
'data_summary': data_summary,
|
||
'analysis': ai_text,
|
||
'mcp': mcp_text,
|
||
})
|
||
return ppt_path
|
||
|
||
elif sub_type in ('monthly', '月報'):
|
||
if sub_arg:
|
||
parts = sub_arg.replace('-', '/').split('/')
|
||
yr, mo = int(parts[0]), int(parts[1]) if len(parts) >= 2 else now.month
|
||
else:
|
||
yr, mo = now.year, now.month
|
||
|
||
_ppt_check_data_freshness('monthly', _chat_id, _reply_to,
|
||
requested_yr=yr, requested_mo=mo)
|
||
|
||
params = {'report_type': 'monthly', 'month': f'{yr}/{mo:02d}'}
|
||
cached, cached_ai = _load_cached_ppt_path_and_analysis('monthly', params)
|
||
if cached:
|
||
return cached
|
||
|
||
mcp_text = ''
|
||
if not cached_ai:
|
||
mcp_text = _fetch_mcp_context()
|
||
ms = query_monthly_summary(yr, mo)
|
||
top_cats = query_category_monthly(yr, mo, lim=8)
|
||
ms['top_categories'] = top_cats
|
||
|
||
# 月對月 / 年對年 比較資料（市場專業標準必備）
|
||
prev_yr_mo = (yr if mo > 1 else yr - 1)
|
||
prev_mo_no = (mo - 1 if mo > 1 else 12)
|
||
try:
|
||
prev_mo_data = query_monthly_summary(prev_yr_mo, prev_mo_no)
|
||
except Exception:
|
||
prev_mo_data = {'found': False}
|
||
try:
|
||
prev_yr_data = query_monthly_summary(yr - 1, mo)
|
||
except Exception:
|
||
prev_yr_data = {'found': False}
|
||
ms['prev_month'] = prev_mo_data if prev_mo_data.get('found') else None
|
||
ms['prev_year'] = prev_yr_data if prev_yr_data.get('found') else None
|
||
|
||
aov = ms.get('avg_order', ms.get('revenue', 0) / ms.get('orders', 1) if ms.get('orders') else 0)
|
||
top5_products = ms.get('top_products', [])[:5]
|
||
top5_cats = top_cats[:5]
|
||
cat_total = sum(float(c.get('revenue', 0)) for c in top5_cats)
|
||
cat_breakdown = '\n'.join(
|
||
f" - {c.get('cat','')}: NT${float(c.get('revenue',0)):,.0f}"
|
||
f"({float(c.get('revenue',0))/cat_total*100:.0f}%)" if cat_total else f" - {c.get('cat','')}:NT${float(c.get('revenue',0)):,.0f}"
|
||
for c in top5_cats
|
||
)
|
||
prod_breakdown = '\n'.join(
|
||
f" {i+1}. {p.get('name','')[:30]} — NT${float(p.get('revenue',0)):,.0f}"
|
||
for i, p in enumerate(top5_products)
|
||
)
|
||
data_summary = (
|
||
f"【月份】{yr}/{mo:02d}\n"
|
||
f"【月業績】NT${ms.get('revenue', 0):,.0f}\n"
|
||
f"【月訂單】{ms.get('orders', 0):,} 筆\n"
|
||
f"【毛利率】{ms.get('gross_margin', 0):.1f}%\n"
|
||
f"【平均客單價】NT${aov:,.0f}\n\n"
|
||
f"【品類業績分佈（TOP5）】\n{cat_breakdown}\n\n"
|
||
f"【熱銷商品 TOP5】\n{prod_breakdown}\n\n"
|
||
f"【MCP 外部市場情報】\n{mcp_text[:600] if mcp_text else '（無外部情報）'}"
|
||
)
|
||
ai_text = cached_ai or _ppt_ai_analysis(data_summary, '月報')
|
||
if not cached_ai and _ppt_needs_fallback(ai_text):
|
||
ai_text = _ppt_fallback_insight('月報', data_summary, mcp_text)
|
||
db_data = {'monthly': ms, 'mcp': mcp_text}
|
||
ppt_path = generate_monthly_ppt(yr, mo, db_data, ai_text)
|
||
_store_ppt_cache('monthly', params, ppt_path, {
|
||
'report_type': 'monthly',
|
||
'parameters': params,
|
||
'data_summary': data_summary,
|
||
'analysis': ai_text,
|
||
'mcp': mcp_text,
|
||
})
|
||
return ppt_path
|
||
|
||
elif sub_type in ('strategy', '策略'):
|
||
period_label = '日報'
|
||
if sub_arg and re.fullmatch(r'\d{4}[/-]\d{1,2}[/-]\d{1,2}', sub_arg):
|
||
date_str = normalize_date(sub_arg)
|
||
start_str, end_str = date_str, date_str
|
||
period_label = f'{date_str} 日策略'
|
||
elif sub_arg in ('weekly', 'week', '週', '週報'):
|
||
end_str = latest_date() or now.strftime('%Y/%m/%d')
|
||
start_str = (datetime.strptime(end_str.replace('/', '-'), '%Y-%m-%d')
|
||
- timedelta(days=6)).strftime('%Y/%m/%d')
|
||
date_str = f'{start_str}~{end_str}'
|
||
period_label = '週策略（近7日）'
|
||
elif sub_arg in ('monthly', 'month', '月', '月報') or (
|
||
sub_arg and re.match(r'\d{4}[/-]\d{1,2}$', sub_arg)):
|
||
m_parts = sub_arg.replace('-', '/').split('/') if sub_arg and '/' in sub_arg else []
|
||
if len(m_parts) == 2:
|
||
yr_s, mo_s = int(m_parts[0]), int(m_parts[1])
|
||
else:
|
||
yr_s, mo_s = now.year, now.month
|
||
import calendar
|
||
last_day = calendar.monthrange(yr_s, mo_s)[1]
|
||
start_str = f'{yr_s}/{mo_s:02d}/01'
|
||
end_str = f'{yr_s}/{mo_s:02d}/{last_day:02d}'
|
||
date_str = f'{yr_s}/{mo_s:02d}'
|
||
period_label = f'{yr_s}/{mo_s:02d} 月策略'
|
||
elif sub_arg in ('quarterly', 'quarter', 'q', '季', '季報'):
|
||
end_str = latest_date() or now.strftime('%Y/%m/%d')
|
||
start_str = (datetime.strptime(end_str.replace('/', '-'), '%Y-%m-%d')
|
||
- timedelta(days=89)).strftime('%Y/%m/%d')
|
||
date_str = f'{start_str}~{end_str}'
|
||
period_label = '季策略（近90日）'
|
||
elif sub_arg in ('half', 'h1', 'h2', '半年', '半年報'):
|
||
end_str = latest_date() or now.strftime('%Y/%m/%d')
|
||
start_str = (datetime.strptime(end_str.replace('/', '-'), '%Y-%m-%d')
|
||
- timedelta(days=179)).strftime('%Y/%m/%d')
|
||
date_str = f'{start_str}~{end_str}'
|
||
period_label = '半年策略（近180日）'
|
||
elif sub_arg in ('yearly', 'year', 'annual', '年', '年報'):
|
||
end_str = latest_date() or now.strftime('%Y/%m/%d')
|
||
start_str = (datetime.strptime(end_str.replace('/', '-'), '%Y-%m-%d')
|
||
- timedelta(days=364)).strftime('%Y/%m/%d')
|
||
date_str = f'{start_str}~{end_str}'
|
||
period_label = '年度策略（近365日）'
|
||
else:
|
||
date_str = latest_date() or now.strftime('%Y/%m/%d')
|
||
start_str, end_str = date_str, date_str
|
||
period_label = f'{date_str} 日策略'
|
||
|
||
_ppt_check_data_freshness('strategy', _chat_id, _reply_to, requested_date=end_str)
|
||
|
||
params = {'report_type': 'strategy', 'start': start_str, 'end': end_str, 'label': period_label}
|
||
cached, cached_ai = _load_cached_ppt_path_and_analysis('strategy', params)
|
||
if cached:
|
||
return cached
|
||
|
||
mcp_text = ''
|
||
if not cached_ai:
|
||
mcp_text = _fetch_mcp_context()
|
||
|
||
if start_str == end_str:
|
||
sales = query_sales(date_str)
|
||
top_products = query_top_products(date_str, 15)
|
||
strat = analyze_product_strategy(date_str, 20)
|
||
else:
|
||
rng = query_date_range(start_str, end_str)
|
||
sales = {'revenue': rng.get('revenue', 0),
|
||
'orders': rng.get('orders', 0),
|
||
'gross_margin': rng.get('gross_margin', 0),
|
||
'avg_order': rng.get('avg_order', 0)}
|
||
top_products = query_top_products_range(start_str, end_str, 15)
|
||
strat = _analyze_strategy_range(start_str, end_str, top_products)
|
||
|
||
from collections import Counter
|
||
strat_cnt = Counter(s['strategy'] for s in strat)
|
||
strat_detail = "\n".join(
|
||
f" [{k}×{v}件] " + " / ".join(
|
||
f"{s['name']}(NT${s['revenue']:,.0f})"
|
||
for s in strat if s['strategy'] == k)[:3]
|
||
for k, v in strat_cnt.most_common()
|
||
)
|
||
data_summary = (
|
||
f"分析週期：{period_label}\n"
|
||
f"業績：NT$ {float(sales.get('revenue', 0)):,.0f} | "
|
||
f"訂單：{sales.get('orders', 0)}筆 | "
|
||
f"毛利率：{sales.get('gross_margin', 0):.1f}% | "
|
||
f"客單價：NT$ {float(sales.get('avg_order', 0)):,.0f}\n\n"
|
||
f"策略矩陣分佈：\n{strat_detail}\n\n"
|
||
f"TOP5商品：" + " / ".join(
|
||
f"{p['name']}(NT${p['revenue']:,.0f})" for p in top_products[:5]) + "\n\n"
|
||
f"外部市場信號：{mcp_text[:600]}"
|
||
)
|
||
ai_text = cached_ai or _ppt_ai_analysis(data_summary, f'策略簡報（{period_label}）')
|
||
if not cached_ai and _ppt_needs_fallback(ai_text):
|
||
ai_text = _ppt_fallback_insight(f'策略簡報（{period_label}）', data_summary, mcp_text)
|
||
db_data = {
|
||
'sales': sales, 'top_products': top_products,
|
||
'strategy': strat, 'mcp': mcp_text,
|
||
'period_label': period_label,
|
||
}
|
||
ppt_path = generate_strategy_ppt(date_str, db_data, ai_text)
|
||
_store_ppt_cache('strategy', params, ppt_path, {
|
||
'report_type': 'strategy',
|
||
'parameters': params,
|
||
'data_summary': data_summary,
|
||
'analysis': ai_text,
|
||
'mcp': mcp_text,
|
||
})
|
||
return ppt_path
|
||
|
||
elif sub_type in ('competitor', '競品', 'compare'):
|
||
if not _PCHOME_AVAILABLE:
|
||
raise RuntimeError("PChome 比價模組不可用")
|
||
|
||
if sub_arg in ('weekly', 'week', '週'):
|
||
end_d = datetime.strptime(
|
||
(latest_date() or now.strftime('%Y/%m/%d')).replace('/', '-'), '%Y-%m-%d')
|
||
start_d = end_d - timedelta(days=6)
|
||
period_label = '週比較（近7日）'
|
||
date_str_for_query = start_d.strftime('%Y/%m/%d')
|
||
elif sub_arg in ('monthly', 'month', '月'):
|
||
end_d = datetime.strptime(
|
||
(latest_date() or now.strftime('%Y/%m/%d')).replace('/', '-'), '%Y-%m-%d')
|
||
start_d = end_d.replace(day=1)
|
||
period_label = f'{end_d.year}/{end_d.month:02d} 月比較'
|
||
date_str_for_query = start_d.strftime('%Y/%m/%d')
|
||
elif sub_arg in ('quarterly', 'quarter', 'q', '季'):
|
||
end_d = datetime.strptime(
|
||
(latest_date() or now.strftime('%Y/%m/%d')).replace('/', '-'), '%Y-%m-%d')
|
||
start_d = end_d - timedelta(days=89)
|
||
period_label = '季比較（近90日）'
|
||
date_str_for_query = start_d.strftime('%Y/%m/%d')
|
||
elif sub_arg in ('half', '半年'):
|
||
end_d = datetime.strptime(
|
||
(latest_date() or now.strftime('%Y/%m/%d')).replace('/', '-'), '%Y-%m-%d')
|
||
start_d = end_d - timedelta(days=179)
|
||
period_label = '半年比較（近180日）'
|
||
date_str_for_query = start_d.strftime('%Y/%m/%d')
|
||
elif sub_arg in ('yearly', 'year', '年'):
|
||
end_d = datetime.strptime(
|
||
(latest_date() or now.strftime('%Y/%m/%d')).replace('/', '-'), '%Y-%m-%d')
|
||
start_d = end_d - timedelta(days=364)
|
||
period_label = '年度比較（近365日）'
|
||
date_str_for_query = start_d.strftime('%Y/%m/%d')
|
||
elif sub_arg and re.match(r'\d{4}[/-]\d{1,2}[/-]\d{1,2}', sub_arg):
|
||
d_str = normalize_date(sub_arg)
|
||
start_d = end_d = datetime.strptime(d_str.replace('/', '-'), '%Y-%m-%d')
|
||
period_label = f'{d_str} 日比較'
|
||
date_str_for_query = d_str
|
||
else:
|
||
yd = (now - timedelta(days=1)).strftime('%Y/%m/%d')
|
||
start_d = end_d = datetime.strptime(yd.replace('/', '-'), '%Y-%m-%d')
|
||
period_label = f'{yd} 日比較'
|
||
date_str_for_query = yd
|
||
|
||
params = {
|
||
'report_type': 'competitor',
|
||
'start': start_d.strftime('%Y/%m/%d'),
|
||
'end': end_d.strftime('%Y/%m/%d'),
|
||
'label': period_label,
|
||
}
|
||
cached, cached_ai = _load_cached_ppt_path_and_analysis('competitor', params)
|
||
if cached:
|
||
return cached
|
||
|
||
mcp_text_c = ''
|
||
if not cached_ai:
|
||
mcp_text_c = _fetch_mcp_context()
|
||
|
||
results = pchome_batch(_db(), top_n=30, date_str=date_str_for_query)
|
||
if results:
|
||
pchome_save(_db(), results)
|
||
|
||
found_c = [r for r in results if r.get('found')]
|
||
pc_wins_c = [r for r in found_c if r.get('price_diff', 0) > 10]
|
||
mo_wins_c = [r for r in found_c if r.get('price_diff', 0) < -10]
|
||
avg_diff_c = (sum(r.get('price_diff_pct', 0) for r in found_c) / len(found_c)
|
||
if found_c else 0)
|
||
|
||
data_summary = (
|
||
f"【我方=PChome，競品=momo】\n"
|
||
f"分析週期：{period_label}\n"
|
||
f"掃描商品：{len(results)} 件 | 比對成功：{len(found_c)} 件\n"
|
||
f"PChome定價優勢（比momo便宜）：{len(pc_wins_c)} 件 | momo威脅（比PChome便宜）：{len(mo_wins_c)} 件\n"
|
||
f"平均價差：{avg_diff_c:+.1f}%（正值=momo偏貴=PChome具優勢）\n\n"
|
||
f"PChome優勢 TOP3（應加強曝光宣傳）：" + " / ".join(
|
||
f"{r['momo_name'][:15]}（PChome便宜NT${r['price_diff']:,.0f}）"
|
||
for r in pc_wins_c[:3]) + "\n"
|
||
f"momo威脅 TOP3（需研擬因應策略）：" + " / ".join(
|
||
f"{r['momo_name'][:15]}（momo便宜NT${abs(r['price_diff']):,.0f}）"
|
||
for r in mo_wins_c[:3]) + "\n\n"
|
||
f"外部情報：{mcp_text_c[:400]}"
|
||
)
|
||
ai_text = cached_ai or _ppt_ai_analysis(data_summary, f'競品比較簡報（{period_label}）')
|
||
if not cached_ai and _ppt_needs_fallback(ai_text):
|
||
ai_text = _ppt_fallback_insight(f'競品比較簡報（{period_label}）', data_summary, mcp_text_c)
|
||
|
||
db_data = {
|
||
'results': results,
|
||
'period_label': period_label,
|
||
'mcp': mcp_text_c,
|
||
}
|
||
ppt_path = generate_competitor_ppt(period_label, db_data, ai_text)
|
||
_store_ppt_cache('competitor', params, ppt_path, {
|
||
'report_type': 'competitor',
|
||
'parameters': params,
|
||
'data_summary': data_summary,
|
||
'analysis': ai_text,
|
||
'mcp': mcp_text_c,
|
||
})
|
||
return ppt_path
|
||
|
||
elif sub_type in ('promo', '促銷'):
|
||
m_p = re.findall(r'\d{4}[/\-]\d{1,2}[/\-]\d{1,2}', sub_arg)
|
||
if len(m_p) < 2:
|
||
raise ValueError(f"促銷簡報需要日期範圍，例如：promo 2026/04/01-2026/04/07")
|
||
start_s_p = normalize_date(m_p[0])
|
||
end_s_p = normalize_date(m_p[1])
|
||
promo_label_p = f'{start_s_p}~{end_s_p}'
|
||
|
||
params = {'report_type': 'promo', 'start': start_s_p, 'end': end_s_p, 'label': promo_label_p}
|
||
cached, cached_ai = _load_cached_ppt_path_and_analysis('promo', params)
|
||
if cached:
|
||
return cached
|
||
|
||
mcp_text = ''
|
||
if not cached_ai:
|
||
mcp_text = _fetch_mcp_context()
|
||
data_p = query_promo_comparison(start_s_p, end_s_p)
|
||
if not data_p:
|
||
raise ValueError("查無促銷期間資料")
|
||
pd = data_p.get('promo', {})
|
||
prev = data_p.get('pre', {})
|
||
tops_str = ' / '.join(
|
||
f"{p['name'][:12]}（NT${p['revenue']:,.0f}）"
|
||
for p in (pd.get('top_products') or [])[:3]
|
||
)
|
||
data_summary_p = (
|
||
f"促銷活動期間：{start_s_p} ~ {end_s_p}（{pd.get('days',0)}天）\n"
|
||
f"對比前期：{prev.get('start','')} ~ {prev.get('end','')}\n\n"
|
||
f"【活動期業績】NT$ {pd.get('revenue',0):,.0f} 訂單 {pd.get('orders',0):,}筆 "
|
||
f"毛利率 {pd.get('margin',0):.1f}%\n"
|
||
f"【對比期業績】NT$ {prev.get('revenue',0):,.0f} 訂單 {prev.get('orders',0):,}筆 "
|
||
f"毛利率 {prev.get('margin',0):.1f}%\n"
|
||
f"業績成長：{data_p.get('rev_lift',0):+.1f}% 訂單成長：{data_p.get('ord_lift',0):+.1f}%\n\n"
|
||
f"活動期熱銷 TOP3：{tops_str or '（無資料）'}\n"
|
||
f"外部市場情報：{mcp_text[:300]}"
|
||
)
|
||
ai_text_p = cached_ai or _ppt_ai_analysis(data_summary_p, f'促銷效益分析（{promo_label_p}）')
|
||
if not cached_ai and _ppt_needs_fallback(ai_text_p):
|
||
ai_text_p = _ppt_fallback_insight(f'促銷效益分析（{promo_label_p}）', data_summary_p, mcp_text)
|
||
ppt_path = generate_promo_ppt(promo_label_p, data_p, ai_text_p)
|
||
_store_ppt_cache('promo', params, ppt_path, {
|
||
'report_type': 'promo',
|
||
'parameters': params,
|
||
'data_summary': data_summary_p,
|
||
'analysis': ai_text_p,
|
||
'mcp': mcp_text,
|
||
})
|
||
return ppt_path
|
||
|
||
elif sub_type in ('vendor', '廠商'):
|
||
# /ppt vendor [YYYY/MM] 指定月份廠商報告
|
||
# /ppt vendor [YYYY/MM/DD-YYYY/MM/DD] 自訂期間
|
||
if sub_arg and re.match(r'\d{4}[/-]\d{1,2}$', sub_arg):
|
||
yr, mo = [int(x) for x in sub_arg.replace('-', '/').split('/')]
|
||
import calendar as _cal
|
||
last_day = _cal.monthrange(yr, mo)[1]
|
||
start_str = f"{yr}/{mo:02d}/01"
|
||
end_str = f"{yr}/{mo:02d}/{last_day:02d}"
|
||
period_lbl = f"{yr}/{mo:02d}"
|
||
# 上期
|
||
prev_yr = yr if mo > 1 else yr - 1
|
||
prev_mo = mo - 1 if mo > 1 else 12
|
||
prev_last = _cal.monthrange(prev_yr, prev_mo)[1]
|
||
prev_start = f"{prev_yr}/{prev_mo:02d}/01"
|
||
prev_end = f"{prev_yr}/{prev_mo:02d}/{prev_last:02d}"
|
||
elif sub_arg and '-' in sub_arg and len(sub_arg) > 15:
|
||
parts = sub_arg.split('-')
|
||
start_str = normalize_date(parts[0])
|
||
end_str = normalize_date(parts[1])
|
||
period_lbl = f"{start_str} ~ {end_str}"
|
||
yr, mo = (int(start_str.split('/')[0]), int(start_str.split('/')[1]))
|
||
# 上期 = 同等天數往前推
|
||
from datetime import datetime as _dt
|
||
from datetime import timedelta as _td
|
||
s = _dt.strptime(start_str.replace('/', '-'), '%Y-%m-%d').date()
|
||
e = _dt.strptime(end_str.replace('/', '-'), '%Y-%m-%d').date()
|
||
days = (e - s).days + 1
|
||
prev_end_d = s - _td(days=1)
|
||
prev_start_d = prev_end_d - _td(days=days - 1)
|
||
prev_start = prev_start_d.strftime('%Y/%m/%d')
|
||
prev_end = prev_end_d.strftime('%Y/%m/%d')
|
||
else:
|
||
# 預設：當月
|
||
yr = now.year
|
||
mo = now.month
|
||
import calendar as _cal
|
||
last_day = _cal.monthrange(yr, mo)[1]
|
||
start_str = f"{yr}/{mo:02d}/01"
|
||
end_str = f"{yr}/{mo:02d}/{last_day:02d}"
|
||
period_lbl = f"{yr}/{mo:02d}"
|
||
prev_yr = yr if mo > 1 else yr - 1
|
||
prev_mo = mo - 1 if mo > 1 else 12
|
||
prev_last = _cal.monthrange(prev_yr, prev_mo)[1]
|
||
prev_start = f"{prev_yr}/{prev_mo:02d}/01"
|
||
prev_end = f"{prev_yr}/{prev_mo:02d}/{prev_last:02d}"
|
||
|
||
params = {'report_type': 'vendor', 'period': period_lbl}
|
||
cached, cached_ai = _load_cached_ppt_path_and_analysis('vendor', params)
|
||
if cached:
|
||
return cached
|
||
|
||
mcp_text = ''
|
||
if not cached_ai:
|
||
mcp_text = _fetch_mcp_context()
|
||
|
||
curr = query_vendor_summary(start_str, end_str, lim=30)
|
||
prev = query_vendor_summary(prev_start, prev_end, lim=30)
|
||
curr['prev_period'] = prev.get('vendor_ranking', [])
|
||
curr['period_label'] = period_lbl
|
||
|
||
# data summary 給 AI
|
||
top5 = curr.get('vendor_ranking', [])[:5]
|
||
top5_str = '\n'.join(
|
||
f" {i+1}. {v.get('name','')[:30]} — NT${v.get('sales',0):,.0f}"
|
||
f" | 毛利 {v.get('margin',0):.1f}%"
|
||
for i, v in enumerate(top5)
|
||
)
|
||
kpis = curr.get('kpis', {})
|
||
data_summary = (
|
||
f"【期間】{period_lbl}\n"
|
||
f"【廠商總數】{kpis.get('vendor_count', 0)} 家\n"
|
||
f"【合計業績】NT${kpis.get('total_sales', 0):,.0f}\n"
|
||
f"【合計毛利】NT${kpis.get('total_profit', 0):,.0f}\n"
|
||
f"【平均毛利率】{kpis.get('avg_margin', 0):.1f}%\n\n"
|
||
f"【TOP 5 廠商】\n{top5_str}\n\n"
|
||
f"【上期同等期間業績】NT${prev.get('kpis', {}).get('total_sales', 0):,.0f}"
|
||
f"（{prev.get('kpis', {}).get('vendor_count', 0)} 家）\n\n"
|
||
f"【MCP 外部市場情報】\n{mcp_text[:500] if mcp_text else '（無外部情報）'}"
|
||
)
|
||
ai_text = cached_ai or _ppt_ai_analysis(data_summary, '廠商業績報告')
|
||
if not cached_ai and _ppt_needs_fallback(ai_text):
|
||
ai_text = _ppt_fallback_insight('廠商業績報告', data_summary, mcp_text)
|
||
|
||
ppt_path = generate_vendor_ppt(yr, mo, curr, ai_text)
|
||
_store_ppt_cache('vendor', params, ppt_path, {
|
||
'report_type': 'vendor',
|
||
'parameters': params,
|
||
'data_summary': data_summary,
|
||
'analysis': ai_text,
|
||
'mcp': mcp_text,
|
||
})
|
||
return ppt_path
|
||
|
||
elif sub_type in ('quarterly', '季報', 'half_yearly', '半年報', 'annual', '年報', 'ttm'):
|
||
# 期間回顧報告 — period_review 共用 generator
|
||
# /ppt quarterly [YYYY/Q1-4] 季報
|
||
# /ppt half_yearly [YYYY/H1-2] 半年報
|
||
# /ppt annual [YYYY] 年報
|
||
# /ppt ttm 最近 12 個月（滾動）
|
||
from datetime import datetime as _dt, timedelta as _td
|
||
import calendar as _cal
|
||
|
||
# ── 解析期間 ────────────────────────────────────────
|
||
if sub_type in ('quarterly', '季報'):
|
||
period_type = 'quarterly'
|
||
yr = int(sub_arg.split('/')[0]) if sub_arg else now.year
|
||
q = int(sub_arg.split('/Q')[1]) if sub_arg and 'Q' in sub_arg else ((now.month - 1) // 3 + 1)
|
||
start_mo, end_mo = (q - 1) * 3 + 1, q * 3
|
||
start_str = f"{yr}/{start_mo:02d}/01"
|
||
end_last = _cal.monthrange(yr, end_mo)[1]
|
||
end_str = f"{yr}/{end_mo:02d}/{end_last:02d}"
|
||
period_label = f"{yr} Q{q}"
|
||
# 上期 = 上一季
|
||
prev_q = q - 1 if q > 1 else 4
|
||
prev_yr = yr if q > 1 else yr - 1
|
||
prev_start_mo, prev_end_mo = (prev_q - 1) * 3 + 1, prev_q * 3
|
||
prev_start = f"{prev_yr}/{prev_start_mo:02d}/01"
|
||
prev_end_last = _cal.monthrange(prev_yr, prev_end_mo)[1]
|
||
prev_end = f"{prev_yr}/{prev_end_mo:02d}/{prev_end_last:02d}"
|
||
yoy_start = f"{yr-1}/{start_mo:02d}/01"
|
||
yoy_end_last = _cal.monthrange(yr-1, end_mo)[1]
|
||
yoy_end = f"{yr-1}/{end_mo:02d}/{yoy_end_last:02d}"
|
||
|
||
elif sub_type in ('half_yearly', '半年報'):
|
||
period_type = 'half_yearly'
|
||
yr = int(sub_arg.split('/')[0]) if sub_arg else now.year
|
||
h = int(sub_arg.split('/H')[1]) if sub_arg and 'H' in sub_arg else (1 if now.month <= 6 else 2)
|
||
start_mo, end_mo = (1, 6) if h == 1 else (7, 12)
|
||
start_str = f"{yr}/{start_mo:02d}/01"
|
||
end_last = _cal.monthrange(yr, end_mo)[1]
|
||
end_str = f"{yr}/{end_mo:02d}/{end_last:02d}"
|
||
period_label = f"{yr} H{h}"
|
||
prev_h = h - 1 if h > 1 else 2
|
||
prev_yr = yr if h > 1 else yr - 1
|
||
prev_start_mo, prev_end_mo = (1, 6) if prev_h == 1 else (7, 12)
|
||
prev_start = f"{prev_yr}/{prev_start_mo:02d}/01"
|
||
prev_end_last = _cal.monthrange(prev_yr, prev_end_mo)[1]
|
||
prev_end = f"{prev_yr}/{prev_end_mo:02d}/{prev_end_last:02d}"
|
||
yoy_start = f"{yr-1}/{start_mo:02d}/01"
|
||
yoy_end_last = _cal.monthrange(yr-1, end_mo)[1]
|
||
yoy_end = f"{yr-1}/{end_mo:02d}/{yoy_end_last:02d}"
|
||
|
||
elif sub_type in ('annual', '年報'):
|
||
period_type = 'annual'
|
||
yr = int(sub_arg) if sub_arg and sub_arg.isdigit() else now.year
|
||
start_str = f"{yr}/01/01"
|
||
end_str = f"{yr}/12/31"
|
||
period_label = f"{yr}"
|
||
prev_start = f"{yr-1}/01/01"
|
||
prev_end = f"{yr-1}/12/31"
|
||
yoy_start = f"{yr-2}/01/01"
|
||
yoy_end = f"{yr-2}/12/31"
|
||
|
||
else: # ttm 滾動 12 月
|
||
period_type = 'ttm'
|
||
today = now.date() if hasattr(now, 'date') else now
|
||
ttm_end = today
|
||
ttm_start = today.replace(day=1) - _td(days=365)
|
||
ttm_start = ttm_start.replace(day=1)
|
||
start_str = ttm_start.strftime('%Y/%m/%d')
|
||
end_str = ttm_end.strftime('%Y/%m/%d')
|
||
period_label = f"TTM {start_str[:7]}~{end_str[:7]}"
|
||
# 上期 TTM = 再往前 12 個月
|
||
prev_end_d = ttm_start - _td(days=1)
|
||
prev_start_d = prev_end_d.replace(day=1) - _td(days=365)
|
||
prev_start_d = prev_start_d.replace(day=1)
|
||
prev_start = prev_start_d.strftime('%Y/%m/%d')
|
||
prev_end = prev_end_d.strftime('%Y/%m/%d')
|
||
yoy_start = prev_start
|
||
yoy_end = prev_end
|
||
|
||
params = {'report_type': period_type, 'period': period_label}
|
||
cached, cached_ai = _load_cached_ppt_path_and_analysis(period_type, params)
|
||
if cached:
|
||
return cached
|
||
|
||
mcp_text = ''
|
||
if not cached_ai:
|
||
mcp_text = _fetch_mcp_context()
|
||
|
||
# 抓三段資料：本期、上期、去年同期
|
||
curr = query_period_summary(start_str, end_str)
|
||
if not curr.get('found'):
|
||
raise RuntimeError(f'{period_type} 期間 {period_label} 無資料，請確認 DB')
|
||
try:
|
||
prev = query_period_summary(prev_start, prev_end)
|
||
except Exception:
|
||
prev = {'found': False}
|
||
try:
|
||
yoy = query_period_summary(yoy_start, yoy_end)
|
||
if yoy.get('found'):
|
||
yoy['period_label'] = f"{yoy_start} ~ {yoy_end}"
|
||
except Exception:
|
||
yoy = {'found': False}
|
||
|
||
# 組 db_data
|
||
kpis = curr.get('kpis', {})
|
||
prod_breakdown = '\n'.join(
|
||
f" {i+1}. {p.get('name','')[:30]} — NT${p.get('revenue', 0):,.0f}"
|
||
for i, p in enumerate(curr.get('top_products', [])[:5])
|
||
)
|
||
cat_breakdown = '\n'.join(
|
||
f" - {c.get('cat','')}: NT${c.get('revenue', 0):,.0f}"
|
||
for c in curr.get('top_categories', [])[:5]
|
||
)
|
||
data_summary = (
|
||
f"【期間】{period_label}（{period_type}）\n"
|
||
f"【業績】NT${kpis.get('revenue', 0):,.0f}（{kpis.get('revenue', 0)/10000:.1f}萬）\n"
|
||
f"【訂單】{kpis.get('orders', 0):,} 筆\n"
|
||
f"【毛利率】{kpis.get('gross_margin', 0):.1f}%\n"
|
||
f"【平均客單】NT${kpis.get('avg_order', 0):,.0f}\n"
|
||
f"【商品數】{kpis.get('product_count', 0)}\n"
|
||
f"【廠商數】{kpis.get('vendor_count', 0)}\n\n"
|
||
f"【品類 TOP 5】\n{cat_breakdown}\n\n"
|
||
f"【熱銷商品 TOP 5】\n{prod_breakdown}\n\n"
|
||
f"【上期業績】NT${prev.get('kpis', {}).get('revenue', 0):,.0f}\n"
|
||
f"【去年同期業績】NT${yoy.get('kpis', {}).get('revenue', 0):,.0f}\n\n"
|
||
f"【MCP 外部市場情報】\n{mcp_text[:600] if mcp_text else '（無）'}"
|
||
)
|
||
ai_text = cached_ai or _ppt_ai_analysis(
|
||
data_summary,
|
||
f'{period_type}（{period_label}）'
|
||
)
|
||
if not cached_ai and _ppt_needs_fallback(ai_text):
|
||
ai_text = _ppt_fallback_insight(period_type, data_summary, mcp_text)
|
||
|
||
db_data_pr = dict(curr)
|
||
db_data_pr['prev_period'] = prev if prev.get('found') else None
|
||
db_data_pr['yoy_period'] = yoy if yoy.get('found') else None
|
||
db_data_pr['mcp'] = mcp_text
|
||
|
||
ppt_path = generate_period_review_ppt(period_type, period_label, db_data_pr, ai_text)
|
||
_store_ppt_cache(period_type, params, ppt_path, {
|
||
'report_type': period_type,
|
||
'parameters': params,
|
||
'data_summary': data_summary,
|
||
'analysis': ai_text,
|
||
'mcp': mcp_text,
|
||
})
|
||
return ppt_path
|
||
|
||
else:
|
||
raise RuntimeError(
|
||
f'不支援的簡報類型：{sub_type}'
|
||
f'（支援：daily / weekly / monthly / quarterly / half_yearly / annual / ttm / strategy / competitor / promo / vendor）'
|
||
)
|
||
|
||
|
||
# ── Telegram Excel 匯入 ──────────────────────────────────────────
|
||
|
||
# 必要欄位定義
|
||
_EXCEL_REQUIRED_COLS = ['日期', '商品ID', '商品名稱', '總業績', '數量']
|
||
_EXCEL_OPTIONAL_COLS = ['廠商名稱', '總成本', '商品分類L1', '毛%', '折扣活動名稱', '折價券活動名稱']
|
||
|
||
def _validate_excel_format(filepath: str, filename: str) -> dict:
|
||
"""
|
||
讀取 Excel，回傳格式驗證報告 dict：
|
||
ok, row_count, col_count, missing_required, present_optional,
|
||
dates, date_count, rev_total, report_type, warnings
|
||
"""
|
||
import pandas as pd
|
||
import os
|
||
result = {
|
||
'ok': False, 'row_count': 0, 'col_count': 0,
|
||
'missing_required': [], 'present_optional': [],
|
||
'dates': [], 'date_count': 0, 'rev_total': 0.0,
|
||
'report_type': '未知格式', 'warnings': [],
|
||
}
|
||
try:
|
||
df = pd.read_excel(filepath, engine='openpyxl', dtype=str)
|
||
if df.empty:
|
||
result['warnings'].append('Excel 檔案為空')
|
||
return result
|
||
|
||
result['row_count'] = len(df)
|
||
result['col_count'] = len(df.columns)
|
||
cols = df.columns.tolist()
|
||
|
||
# 判斷報表類型
|
||
if '即時業績' in filename and '當日' in filename:
|
||
result['report_type'] = '當日業績報表'
|
||
elif '即時業績' in filename and '全月' in filename:
|
||
result['report_type'] = '全月業績報表'
|
||
else:
|
||
result['report_type'] = '業績報表（自動偵測）'
|
||
result['warnings'].append('檔名不符合標準格式（建議：即時業績_當日_YYYYMMDD.xlsx）')
|
||
|
||
# 必要欄位檢查
|
||
result['missing_required'] = [c for c in _EXCEL_REQUIRED_COLS if c not in cols]
|
||
result['present_optional'] = [c for c in _EXCEL_OPTIONAL_COLS if c in cols]
|
||
|
||
# 日期分析（找日期欄）
|
||
date_col = next((c for c in ['日期', '訂單日期', '交易日期'] if c in cols), None)
|
||
if date_col:
|
||
import pandas as _pd2
|
||
dates_s = _pd2.to_datetime(df[date_col], errors='coerce').dt.strftime('%Y/%m/%d')
|
||
result['dates'] = sorted(dates_s.dropna().unique().tolist())
|
||
result['date_count'] = len(result['dates'])
|
||
|
||
# 業績預覽
|
||
if '總業績' in cols:
|
||
try:
|
||
result['rev_total'] = df['總業績'].apply(
|
||
lambda x: float(str(x).replace(',', '').strip() or '0')
|
||
).sum()
|
||
except Exception:
|
||
pass
|
||
|
||
result['ok'] = len(result['missing_required']) == 0
|
||
except Exception as _e:
|
||
result['warnings'].append(f'讀取失敗：{str(_e)[:80]}')
|
||
return result
|
||
|
||
|
||
def _fmt_excel_validation_report(filename: str, v: dict) -> str:
|
||
"""格式化 Excel 驗證報告為 Telegram 訊息"""
|
||
ok_mark = '✅' if v['ok'] else '❌'
|
||
lines = [
|
||
f"📊 *Excel 格式驗證報告*",
|
||
f"{'━' * 24}",
|
||
f"",
|
||
f"📄 *檔案*：`{filename}`",
|
||
f"📋 *類型*：{v['report_type']}",
|
||
f"📦 *資料量*：{v['row_count']:,} 筆 × {v['col_count']} 欄",
|
||
f"",
|
||
]
|
||
|
||
# 必要欄位
|
||
if v['missing_required']:
|
||
missing_str = ' '.join(f'❌ {c}' for c in v['missing_required'])
|
||
lines.append(f"❌ *必要欄位缺失*：{missing_str}")
|
||
lines.append(f" _缺少以上欄位，無法匯入_")
|
||
else:
|
||
present_str = ' '.join(f'✔ {c}' for c in _EXCEL_REQUIRED_COLS)
|
||
lines.append(f"✅ *必要欄位*（全部通過）")
|
||
lines.append(f" {present_str}")
|
||
|
||
if v['present_optional']:
|
||
opt_str = ' '.join(f'✔ {c}' for c in v['present_optional'])
|
||
lines.append(f"📌 *附加欄位*：{opt_str}")
|
||
|
||
lines.append("")
|
||
|
||
# 日期範圍
|
||
if v['dates']:
|
||
if v['date_count'] == 1:
|
||
lines.append(f"📅 *涵蓋日期*：`{v['dates'][0]}`（1 天）")
|
||
elif v['date_count'] <= 5:
|
||
date_str = ' / '.join(v['dates'])
|
||
lines.append(f"📅 *涵蓋日期*：{v['date_count']} 天（{date_str}）")
|
||
else:
|
||
lines.append(f"📅 *涵蓋日期*：{v['dates'][0]} ~ {v['dates'][-1]}（共 {v['date_count']} 天）")
|
||
|
||
if v['rev_total']:
|
||
lines.append(f"💰 *業績預覽*：`NT$ {v['rev_total']:,.0f}`")
|
||
|
||
if v['warnings']:
|
||
lines.append("")
|
||
for w in v['warnings']:
|
||
lines.append(f"⚠️ {w}")
|
||
|
||
lines.append("")
|
||
lines.append(f"{'━' * 24}")
|
||
if v['ok']:
|
||
lines.append(f"✅ *格式驗證通過，可以匯入！*")
|
||
lines.append(f"_寫入後將覆蓋相同日期的現有資料_")
|
||
else:
|
||
lines.append(f"❌ *格式驗證失敗，無法匯入*")
|
||
lines.append(f"_請修正上述缺失欄位後重新上傳_")
|
||
return "\n".join(lines)
|
||
|
||
|
||
def _download_telegram_file(file_id: str, suffix: str = '.xlsx') -> str:
|
||
"""從 Telegram 下載檔案到 /tmp，回傳本地路徑；失敗回傳 ''"""
|
||
try:
|
||
r = requests.get(f"{BOT_API_URL}/getFile", params={'file_id': file_id}, timeout=10)
|
||
file_path_tg = r.json().get('result', {}).get('file_path', '')
|
||
if not file_path_tg:
|
||
return ''
|
||
file_url = f"https://api.telegram.org/file/bot{BOT_TOKEN}/{file_path_tg}"
|
||
file_data = requests.get(file_url, timeout=60).content
|
||
import tempfile as _tf
|
||
tmp = _tf.NamedTemporaryFile(suffix=suffix, prefix='ocbot_xl_', delete=False, dir='/tmp')
|
||
tmp.write(file_data)
|
||
tmp.close()
|
||
return tmp.name
|
||
except Exception as _e:
|
||
sys_log.error(f"[ExcelImport] download failed: {_e}")
|
||
return ''
|
||
|
||
|
||
def _handle_excel_import(doc: dict, chat_id: int, reply_to: int):
|
||
"""背景執行緒：下載 Excel → 驗證 → 發送驗證報告 + 確認按鈕"""
|
||
filename = doc.get('file_name', 'upload.xlsx')
|
||
file_id = doc.get('file_id', '')
|
||
file_size = doc.get('file_size', 0)
|
||
|
||
# 大小限制 50MB
|
||
if file_size > 50 * 1024 * 1024:
|
||
send_message(chat_id, "⚠️ 檔案太大（上限 50MB），請壓縮後重新上傳", reply_to)
|
||
return
|
||
|
||
send_message(chat_id,
|
||
f"⏳ 正在下載 Excel 檔案...\n`{filename}`",
|
||
reply_to, parse_mode='Markdown')
|
||
|
||
suffix = '.xlsx' if filename.lower().endswith('.xlsx') else '.xls'
|
||
local_path = _download_telegram_file(file_id, suffix)
|
||
if not local_path:
|
||
send_message(chat_id, "❌ 檔案下載失敗，請重試", reply_to)
|
||
return
|
||
|
||
# 驗證格式
|
||
v = _validate_excel_format(local_path, filename)
|
||
report_text = _fmt_excel_validation_report(filename, v)
|
||
|
||
if v['ok']:
|
||
# 儲存待確認資訊
|
||
_excel_pending[chat_id] = {
|
||
'file_path': local_path,
|
||
'filename': filename,
|
||
}
|
||
kb = [
|
||
_row(('✅ 確認匯入資料庫', 'cmd:import_confirm'), ('❌ 取消', 'cmd:import_cancel')),
|
||
]
|
||
send_message(chat_id, report_text, reply_to, kb)
|
||
else:
|
||
# 驗證失敗 → 直接顯示報告，不提供匯入選項
|
||
try:
|
||
import os
|
||
os.unlink(local_path)
|
||
except Exception:
|
||
pass
|
||
send_message(chat_id, report_text, reply_to)
|
||
|
||
|
||
# ── 排程自動報告 ───────────────────────────────────────────────
|
||
|
||
def send_morning_report():
|
||
"""每日 08:30 早報 — P10 升級版：TOP15 + PPT導引按鈕"""
|
||
try:
|
||
now = datetime.now(TAIPEI_TZ)
|
||
td = now.strftime('%Y/%m/%d')
|
||
yd = (now - timedelta(days=1)).strftime('%Y/%m/%d')
|
||
wdays = ['週一', '週二', '週三', '週四', '週五', '週六', '週日']
|
||
sales = query_sales(yd)
|
||
top15 = query_top_products(yd, 15)
|
||
weekly = query_weekly_trend()
|
||
|
||
# 計算7日均值做基準
|
||
avg7 = 0
|
||
if weekly and len(weekly) >= 3:
|
||
avg7 = sum(w['revenue'] for w in weekly[-7:]) / min(7, len(weekly))
|
||
|
||
lines = [
|
||
f"☀️ *早安！今天是 {now.strftime('%m/%d')}（{wdays[now.weekday()]}）*",
|
||
f"{'─' * 30}",
|
||
"",
|
||
]
|
||
|
||
if sales.get('found'):
|
||
rev = float(sales['revenue'])
|
||
orders = sales.get('orders', 0) or 0
|
||
margin = float(sales.get('gross_margin', 0))
|
||
lines.append(f"📅 *昨日業績回顧* _({yd})_")
|
||
lines.append(f" 💰 業績：`NT$ {rev:,.0f}` 📦 訂單：`{orders:,}` 筆")
|
||
lines.append(f" 🛒 客單：`NT$ {float(sales.get('avg_order',0)):,.0f}` 📈 毛利率：`{margin:.1f}%`")
|
||
if avg7 > 0:
|
||
diff = rev - avg7
|
||
pct = diff / avg7 * 100
|
||
arrow = '▲' if diff >= 0 else '▼'
|
||
vs_avg = f"{arrow}{abs(pct):.1f}% vs 7日均 (`NT$ {diff:+,.0f}`)"
|
||
lines.append(f" 📊 {vs_avg}")
|
||
else:
|
||
lines.append(f"📅 _昨日資料尚未匯入（{yd}）_")
|
||
lines.append("")
|
||
|
||
if top15:
|
||
lines.append(f"🏆 *昨日熱銷 TOP15*")
|
||
lines.append(f"{'─' * 26}")
|
||
for i, p in enumerate(top15):
|
||
pid = p.get('id', '') or ''
|
||
sid = _short_id(pid)
|
||
link = _pchome_link(pid, p['name'], 20)
|
||
rank = MEDALS[i] if i < len(MEDALS) else f"`{i+1}.`"
|
||
lines.append(f" {rank} {link}")
|
||
lines.append(f" 🆔 `{sid}` `NT$ {p['revenue']:,.0f}` 📦 {p.get('qty','-')}件")
|
||
lines.append("")
|
||
|
||
gs = get_goal_status(td)
|
||
if gs.get('daily_goal'):
|
||
dg = gs['daily_goal']
|
||
lines.append(f"🎯 *今日目標* `NT$ {dg:,.0f}`")
|
||
lines.append(f" 💪 加油衝刺！祝業績長紅！")
|
||
elif gs.get('monthly_goal'):
|
||
mg = gs['monthly_goal']
|
||
lines.append(f"🎯 *月目標* `NT$ {mg:,.0f}`")
|
||
lines.append(f" 月累計：`NT$ {gs.get('month_rev',0):,.0f}` (`{gs.get('monthly_pct',0) or 0:.1f}%`)")
|
||
else:
|
||
lines.append("💪 *今日加油！* 祝業績長紅！")
|
||
lines.append("")
|
||
|
||
try:
|
||
from services.mcp_context_service import get_taiwan_weather
|
||
taipei = get_taiwan_weather().get('weather', {}).get('臺北市', {})
|
||
if taipei:
|
||
wx = taipei.get('Wx', '')
|
||
pop = taipei.get('PoP', '')
|
||
temp = taipei.get('temp', '') or f"{taipei.get('MinT','')}-{taipei.get('MaxT','')}°C"
|
||
weather_line = f"🌤 *台北天氣* {wx}"
|
||
if temp.strip('-'):
|
||
weather_line += f" {temp}"
|
||
if pop:
|
||
weather_line += f" 💧降雨 {pop}%"
|
||
lines.append(weather_line)
|
||
if int(pop or 0) >= 60:
|
||
lines.append(" ☂️ _建議做室內促銷活動_")
|
||
except Exception:
|
||
pass
|
||
|
||
# P10 — 導引按鈕：產出日報PPT + 查看業績數據
|
||
morning_kb = [
|
||
_row((f'📊 產出 {yd} 日報PPT', f'cmd:ppt:daily {yd}'),
|
||
('📈 業績數據', f'cmd:sales:{yd}')),
|
||
_row(('🏆 完整熱銷排行', f'cmd:top:{yd}'),
|
||
('📋 下載 Excel', f'cmd:report:{yd}')),
|
||
]
|
||
send_message(ALLOWED_GROUP, "\n".join(lines), keyboard=morning_kb, parse_mode='Markdown')
|
||
sys_log.info("[OpenClawBot] 早報已發送")
|
||
except Exception as e:
|
||
sys_log.error(f"[OpenClawBot] 早報失敗: {e}")
|
||
|
||
|
||
def send_evening_report():
|
||
"""每日 21:00 晚報"""
|
||
try:
|
||
now = datetime.now(TAIPEI_TZ)
|
||
td = now.strftime('%Y/%m/%d')
|
||
sales = query_sales(td)
|
||
weekly = query_weekly_trend()
|
||
|
||
lines = [
|
||
f"🌙 *今日業績收盤 {now.strftime('%m/%d')} 21:00*",
|
||
f"{'─' * 30}",
|
||
"",
|
||
]
|
||
|
||
if sales.get('found'):
|
||
rev = float(sales['revenue'])
|
||
orders = sales.get('orders', 0) or 0
|
||
prods = sales.get('products', 0) or 0
|
||
avg_o = float(sales.get('avg_order', 0))
|
||
margin = float(sales.get('gross_margin', 0))
|
||
|
||
lines.append(f"💰 *今日業績* `NT$ {rev:,.0f}`")
|
||
lines.append(f"📦 訂單 `{orders:,}` 筆 🛍 商品 `{prods:,}` 件 🛒 客單 `NT$ {avg_o:,.0f}`")
|
||
lines.append(f"📈 毛利率 `{margin:.1f}%`")
|
||
lines.append("")
|
||
|
||
gs = get_goal_status(td)
|
||
if gs.get('daily_goal'):
|
||
pct = gs.get('daily_pct', 0) or 0
|
||
dg = gs['daily_goal']
|
||
gap = dg - rev
|
||
bar_len = min(10, max(0, int(pct // 10)))
|
||
bar = '█' * bar_len + '░' * (10 - bar_len)
|
||
result = "🏆 超標達成！" if gap <= 0 else f"差 `NT$ {gap:,.0f}` 達標"
|
||
lines.append(f"🎯 *日目標達成* `[{bar}]` *{pct:.1f}%* {result}")
|
||
lines.append("")
|
||
|
||
# vs 昨日比較
|
||
if weekly and len(weekly) >= 2:
|
||
yd_rev = weekly[-2]['revenue'] if len(weekly) >= 2 else 0
|
||
if yd_rev:
|
||
diff = rev - yd_rev
|
||
pct_d = diff / yd_rev * 100
|
||
arrow = '▲' if diff >= 0 else '▼'
|
||
emoji = '📈' if diff >= 0 else '📉'
|
||
lines.append(f"{emoji} *vs 昨日* {arrow}`{abs(pct_d):.1f}%` (`NT$ {diff:+,.0f}`)")
|
||
lines.append("")
|
||
else:
|
||
lines.append("⚠️ *今日業績資料尚未匯入*")
|
||
lines.append("")
|
||
|
||
top15_ev = query_top_products(td, 15)
|
||
if top15_ev:
|
||
lines.append(f"🏆 *今日熱銷 TOP15*")
|
||
lines.append(f"{'─' * 26}")
|
||
for i, p in enumerate(top15_ev):
|
||
pid = p.get('id', '') or ''
|
||
sid = _short_id(pid)
|
||
link = _pchome_link(pid, p['name'], 20)
|
||
rank = MEDALS[i] if i < len(MEDALS) else f"`{i+1}.`"
|
||
lines.append(f" {rank} {link}")
|
||
lines.append(f" 🆔 `{sid}` `NT$ {p['revenue']:,.0f}` 📦 {p.get('qty','-')}件")
|
||
lines.append("")
|
||
|
||
try:
|
||
strat = analyze_product_strategy(td, 8)
|
||
hot_list = [s for s in strat if s['strategy'] == '加碼'][:2]
|
||
opp_list = [s for s in strat if s['strategy'] == '機會'][:2]
|
||
if hot_list or opp_list:
|
||
lines.append(f"🧬 *明日行動建議*")
|
||
for s in hot_list:
|
||
pid_s = s.get('id', '') or ''
|
||
link_s = _pchome_link(pid_s, s['name'], 18)
|
||
lines.append(f" 🔥 加碼：{link_s} 週▲{abs(s.get('growth',0))*100:.0f}%")
|
||
for s in opp_list:
|
||
pid_s = s.get('id', '') or ''
|
||
link_s = _pchome_link(pid_s, s['name'], 18)
|
||
lines.append(f" 💡 機會：{link_s}")
|
||
lines.append("")
|
||
except Exception:
|
||
pass
|
||
|
||
# P10 — 晚報導引按鈕
|
||
evening_kb = [
|
||
_row((f'📊 產出 {td} 日報PPT', f'cmd:ppt:daily {td}'),
|
||
('📈 完整業績數據', f'cmd:sales:{td}')),
|
||
_row(('📋 下載 Excel 報表', f'cmd:report:{td}'),
|
||
('🧬 策略矩陣分析', f'cmd:strategy:{td}')),
|
||
]
|
||
send_message(ALLOWED_GROUP, "\n".join(lines), keyboard=evening_kb, parse_mode='Markdown')
|
||
sys_log.info("[OpenClawBot] 晚報已發送")
|
||
except Exception as e:
|
||
sys_log.error(f"[OpenClawBot] 晚報失敗: {e}")
|
||
|
||
|
||
def send_weekly_report():
|
||
"""每週一 09:00 週報"""
|
||
try:
|
||
now = datetime.now(TAIPEI_TZ)
|
||
td = now.strftime('%Y/%m/%d')
|
||
weekly = query_weekly_trend()
|
||
WEEKDAYS_ZH = ['週一', '週二', '週三', '週四', '週五', '週六', '週日']
|
||
|
||
lines = [
|
||
f"📊 *週報 {now.strftime('%m/%d')}（週一）*",
|
||
f"{'─' * 30}",
|
||
"",
|
||
]
|
||
|
||
if weekly:
|
||
total = sum(d.get('revenue', 0) for d in weekly)
|
||
avg = total / len(weekly)
|
||
max_r = max(d['revenue'] for d in weekly)
|
||
min_r = min(d['revenue'] for d in weekly)
|
||
max_rev = max_r or 1
|
||
lines.append(f"📅 *上週業績總覽*")
|
||
lines.append(f" 合計：`NT$ {total:,.0f}` 日均：`NT$ {avg:,.0f}`")
|
||
lines.append(f" 最高：`NT$ {max_r:,.0f}` 最低：`NT$ {min_r:,.0f}`")
|
||
lines.append("")
|
||
lines.append("*逐日明細*")
|
||
for i, d in enumerate(weekly[-7:]):
|
||
rev = d.get('revenue', 0)
|
||
bar_len = max(1, int(rev / max_rev * 8))
|
||
bar = '█' * bar_len + '·' * (8 - bar_len)
|
||
try:
|
||
from datetime import datetime as dt
|
||
d_obj = dt.strptime(d['date'].replace('/', '-'), '%Y-%m-%d')
|
||
wday = WEEKDAYS_ZH[d_obj.weekday()]
|
||
except Exception:
|
||
wday = ''
|
||
# vs 前日漲跌
|
||
if i > 0:
|
||
prev = weekly[i - 1]['revenue']
|
||
pct_chg = (rev - prev) / prev * 100 if prev else 0
|
||
chg_str = f" {'▲' if pct_chg >= 0 else '▼'}{abs(pct_chg):.1f}%"
|
||
else:
|
||
chg_str = ''
|
||
lines.append(f" `{d['date']}` {wday} `{bar}` `NT$ {rev:>10,.0f}`{chg_str}")
|
||
lines.append("")
|
||
|
||
top5 = query_top_products(td, 5)
|
||
if top5:
|
||
lines.append(f"🏆 *本週熱銷 TOP5*")
|
||
for i, p in enumerate(top5[:5]):
|
||
sid = _short_id(p.get('id', ''))
|
||
lines.append(f" {MEDALS[i]} {p['name'][:18]} `NT$ {p['revenue']:,.0f}`")
|
||
lines.append(f" 🆔 `{sid}`")
|
||
lines.append("")
|
||
|
||
lines.append(f"📋 [報表中心]({MOMO_BASE_URL}/reports)")
|
||
|
||
# 附趨勢圖
|
||
chart = gen_trend_chart(14)
|
||
if chart:
|
||
send_photo(ALLOWED_GROUP, chart, caption="\n".join(lines))
|
||
os.unlink(chart)
|
||
else:
|
||
send_message(ALLOWED_GROUP, "\n".join(lines), parse_mode='Markdown')
|
||
|
||
sys_log.info("[OpenClawBot] 週報已發送")
|
||
except Exception as e:
|
||
sys_log.error(f"[OpenClawBot] 週報失敗: {e}")
|
||
|
||
|
||
def check_anomalies():
|
||
"""每日 9/12/15/18 點異常偵測（整體業績 + 商品級）"""
|
||
try:
|
||
now = datetime.now(TAIPEI_TZ)
|
||
td = now.strftime('%Y/%m/%d')
|
||
ts = now.strftime('%H:%M')
|
||
alerts = []
|
||
|
||
# ── ① 整體業績異常（今日 vs 7日均） ──────────────────
|
||
try:
|
||
weekly = query_weekly_trend()
|
||
today_data = next((w for w in weekly if w['date'] == td), None)
|
||
past_data = [w for w in weekly if w['date'] != td]
|
||
if today_data and len(past_data) >= 3:
|
||
avg7 = sum(w['revenue'] for w in past_data[-7:]) / len(past_data[-7:])
|
||
if avg7 > 0:
|
||
pct = (today_data['revenue'] - avg7) / avg7 * 100
|
||
if abs(pct) >= 25:
|
||
direction = '📈 急升' if pct > 0 else '📉 急降'
|
||
tip = '建議加強曝光推廣' if pct > 0 else '⚠️ 請立即確認原因（活動下架？資料延遲？）'
|
||
alerts.insert(0,
|
||
f"🔔 *整體業績異常* {direction} {abs(pct):.0f}%\n"
|
||
f" 今日：`NT$ {today_data['revenue']:,.0f}` "
|
||
f"7日均：`NT$ {avg7:,.0f}`\n"
|
||
f" 💡 {tip}"
|
||
)
|
||
except Exception as _e:
|
||
sys_log.warning(f"[anomaly] 整體業績檢查失敗: {_e}")
|
||
|
||
# ── ② 商品級異常（偏差>30%） ─────────────────────────
|
||
anomalies = query_anomalies(td)
|
||
for a in anomalies:
|
||
pct = a.get('pct', 0) or 0
|
||
if abs(pct) < 30:
|
||
continue
|
||
direction = '📈 *急升*' if pct > 0 else '📉 *急降*'
|
||
today_rev = a.get('today', 0)
|
||
avg7_rev = a.get('avg7', 0)
|
||
sid = _short_id(a.get('id', ''))
|
||
if avg7_rev:
|
||
ratio = today_rev / avg7_rev
|
||
bar_len = min(10, max(0, int(ratio * 5)))
|
||
ratio_bar = '█' * bar_len + '░' * (10 - bar_len)
|
||
alerts.append(
|
||
f"{direction} {abs(pct):.0f}%\n"
|
||
f" 商品：{a['name'][:20]}\n"
|
||
f" 🆔 `{sid}`\n"
|
||
f" 今日：`NT$ {today_rev:,.0f}` 7日均：`NT$ {avg7_rev:,.0f}`\n"
|
||
f" 比率：`{ratio_bar}` {ratio:.1f}x"
|
||
)
|
||
else:
|
||
alerts.append(
|
||
f"{direction} {abs(pct):.0f}%\n"
|
||
f" 商品：{a['name'][:20]}\n"
|
||
f" 🆔 `{sid}`\n"
|
||
f" 今日：`NT$ {today_rev:,.0f}` 7日均：`NT$ {avg7_rev:,.0f}`"
|
||
)
|
||
|
||
if alerts:
|
||
header = (
|
||
f"🚨 *業績異常偵測 {td} {ts}*\n"
|
||
f"{'─' * 28}\n"
|
||
f"共發現 {len(alerts)} 件異常\n\n"
|
||
)
|
||
msg = header + "\n\n".join(alerts)
|
||
ack_kb = [
|
||
_row(('✅ 已知悉', 'cmd:ack:anomaly'),
|
||
('🔄 追蹤中', 'cmd:ack:tracking')),
|
||
_row(('📊 查看今日業績', f'cmd:sales:{td}'),
|
||
('🏆 熱銷商品', f'cmd:top:{td}')),
|
||
]
|
||
send_message(ALLOWED_GROUP, msg, keyboard=ack_kb, parse_mode='Markdown')
|
||
sys_log.info(f"[OpenClawBot] 異常告警 {len(alerts)} 筆")
|
||
else:
|
||
sys_log.info(f"[OpenClawBot] 異常偵測完成，無告警 ({ts})")
|
||
except Exception as e:
|
||
sys_log.error(f"[OpenClawBot] check_anomalies: {e}")
|
||
|
||
|
||
def send_competitor_report():
|
||
"""每日 08:00 推播競品比價日報 + 降價警報"""
|
||
if not _PCHOME_AVAILABLE:
|
||
return
|
||
try:
|
||
yesterday = (datetime.now(TAIPEI_TZ).date() - timedelta(days=1)).strftime('%Y/%m/%d')
|
||
sys_log.info(f'[PChome] 每日競品日報開始 {yesterday}')
|
||
results = pchome_batch(_db(), top_n=30, date_str=yesterday)
|
||
pchome_save(_db(), results)
|
||
msg = pchome_fmt_report(results, yesterday)
|
||
kb = [_row(('🔍 搜尋比價', 'await:search_compare'),
|
||
('📄 比價簡報', 'menu:competitor_ppt'))]
|
||
send_message(ALLOWED_GROUP, msg, None, kb)
|
||
sys_log.info(f'[PChome] 競品日報已推播 {len(results)} 件商品')
|
||
|
||
# ── 追蹤價格變動，有降價則額外發送警報 ──────────────
|
||
try:
|
||
changes = track_competitor_price_changes(results)
|
||
if changes:
|
||
alert_lines = [
|
||
f"⚡ *momo 降價警報！* 共 {len(changes)} 件",
|
||
f"{'─' * 26}",
|
||
f"_momo 降價 ≥5% → PChome 需評估因應策略_",
|
||
"",
|
||
]
|
||
for c in changes[:8]:
|
||
alert_lines.append(
|
||
f"📉 *{c['name']}*\n"
|
||
f" `NT$ {c['prev_price']:,.0f}` → `NT$ {c['curr_price']:,.0f}` "
|
||
f"*{c['pct']:+.1f}%*"
|
||
)
|
||
send_message(ALLOWED_GROUP, "\n".join(alert_lines), parse_mode='Markdown')
|
||
sys_log.info(f"[price_track] 降價警報 {len(changes)} 件")
|
||
except Exception as _pe:
|
||
sys_log.warning(f"[price_track] {_pe}")
|
||
except Exception as e:
|
||
sys_log.error(f'[PChome] send_competitor_report: {e}', exc_info=True)
|
||
|
||
|
||
def send_daily_excel():
|
||
"""每日 08:45 自動發送昨日 Excel 業績報表"""
|
||
try:
|
||
yesterday = (datetime.now(TAIPEI_TZ).date() - timedelta(days=1)).strftime('%Y/%m/%d')
|
||
sys_log.info(f"[AutoExcel] 開始產生 {yesterday} Excel 報表")
|
||
path = generate_daily_pdf(yesterday)
|
||
if not path:
|
||
sys_log.warning("[AutoExcel] 報表產生失敗")
|
||
return
|
||
ext = 'Excel' if path.endswith('.xlsx') else 'CSV'
|
||
caption = (
|
||
f"📊 *{yesterday} 完整業績報表（{ext}）*\n"
|
||
f"_自動定時發送 — 每日 08:45_"
|
||
)
|
||
send_document(ALLOWED_GROUP, path, caption=caption)
|
||
try:
|
||
import os as _os; _os.unlink(path)
|
||
except Exception:
|
||
pass
|
||
sys_log.info(f"[AutoExcel] {yesterday} 報表已發送")
|
||
except Exception as e:
|
||
sys_log.error(f"[AutoExcel] {e}")
|
||
|
||
|
||
def start_scheduler():
|
||
"""啟動排程（Flask app 啟動後呼叫）"""
|
||
global _scheduler
|
||
try:
|
||
if _scheduler is not None and _scheduler.running:
|
||
sys_log.info("[OpenClawBot] Scheduler 已在執行中，跳過重複啟動")
|
||
return
|
||
|
||
from apscheduler.schedulers.background import BackgroundScheduler
|
||
from apscheduler.triggers.cron import CronTrigger
|
||
|
||
_scheduler = BackgroundScheduler(timezone='Asia/Taipei')
|
||
_scheduler.add_job(
|
||
send_morning_report,
|
||
CronTrigger(hour=8, minute=30),
|
||
id="openclaw_send_morning_report",
|
||
replace_existing=True,
|
||
)
|
||
_scheduler.add_job(
|
||
send_competitor_report,
|
||
CronTrigger(hour=8, minute=0),
|
||
id="openclaw_send_competitor_report",
|
||
replace_existing=True,
|
||
)
|
||
_scheduler.add_job(
|
||
send_daily_excel,
|
||
CronTrigger(hour=8, minute=45),
|
||
id="openclaw_send_daily_excel",
|
||
replace_existing=True,
|
||
)
|
||
_scheduler.add_job(
|
||
send_evening_report,
|
||
CronTrigger(hour=21, minute=0),
|
||
id="openclaw_send_evening_report",
|
||
replace_existing=True,
|
||
)
|
||
_scheduler.add_job(
|
||
send_weekly_report,
|
||
CronTrigger(day_of_week='mon', hour=9, minute=0),
|
||
id="openclaw_send_weekly_report",
|
||
replace_existing=True,
|
||
)
|
||
_scheduler.add_job(
|
||
check_anomalies,
|
||
CronTrigger(hour='9,12,15,18', minute=0),
|
||
id="openclaw_check_anomalies",
|
||
replace_existing=True,
|
||
)
|
||
# ADR-019 Phase 6: 每日 09:00 主動巡檢資料新鮮度，缺資料時透過 EventRouter 發警告
|
||
try:
|
||
from services.data_freshness_probe import run_data_freshness_probe
|
||
_scheduler.add_job(
|
||
run_data_freshness_probe,
|
||
CronTrigger(hour=9, minute=5),
|
||
id="openclaw_data_freshness_probe",
|
||
replace_existing=True,
|
||
)
|
||
except ImportError as _e:
|
||
sys_log.warning(f"[OpenClawBot] data_freshness_probe 未安裝，跳過：{_e}")
|
||
_scheduler.start()
|
||
sys_log.info("[OpenClawBot] Scheduler started ✓ (competitor/morning/excel/evening/weekly/anomaly/freshness)")
|
||
except ImportError:
|
||
sys_log.warning("[OpenClawBot] APScheduler 未安裝 — 排程功能停用")
|
||
except Exception as e:
|
||
sys_log.error(f"[OpenClawBot] start_scheduler: {e}")
|
||
|
||
|
||
def register_commands():
|
||
"""Telegram 只支援英文小寫指令"""
|
||
cmds = [
|
||
{'command': 'sales', 'description': '今日業績總覽'},
|
||
{'command': 'top', 'description': '熱銷商品 TOP10（含商品ID）'},
|
||
{'command': 'vendor', 'description': '熱銷廠商排行'},
|
||
{'command': 'trend', 'description': '近7日業績趨勢'},
|
||
{'command': 'compare', 'description': '同期比較（vs上週/上月）'},
|
||
{'command': 'category', 'description': '分類業績鑽取'},
|
||
{'command': 'restock', 'description': '📦 補貨預測（銷售速度分析）'},
|
||
{'command': 'promo', 'description': '🎉 促銷效益追蹤 /promo 2026/04/01-2026/04/07'},
|
||
{'command': 'goal', 'description': '目標達成率（/goal 200000 設定）'},
|
||
{'command': 'chart', 'description': '業績趨勢圖'},
|
||
{'command': 'health', 'description': '商品健康分析'},
|
||
{'command': 'strategy', 'description': '商品策略矩陣'},
|
||
{'command': 'ppt', 'description': '生成業績簡報 /ppt daily|weekly|monthly|strategy'},
|
||
{'command': 'report', 'description': '下載完整業績報表 (Excel)'},
|
||
{'command': 'history', 'description': '月份業績總覽（/history 2026/03）'},
|
||
{'command': 'news', 'description': '即時電商新聞'},
|
||
{'command': 'weather', 'description': '今日天氣（行銷參考）'},
|
||
{'command': 'menu', 'description': '顯示主選單'},
|
||
{'command': 'help', 'description': '使用說明'},
|
||
]
|
||
return _tg('setMyCommands', {'commands': cmds})
|
||
|
||
|
||
_AWAIT_PROMPTS = {
|
||
'date_sales': ('📅 請輸入查詢日期\n格式：`2026/04/15`', '業績日期'),
|
||
'date_range_sales': (
|
||
'📅 請輸入日期或日期區間\n\n'
|
||
'📌 單日：`2026/04/15`\n'
|
||
'📌 區間：`2026/04/01-2026/04/15`\n'
|
||
'📌 月份：`2026/04`',
|
||
'日期/區間',
|
||
),
|
||
'date_top': ('📅 請輸入查詢日期\n格式：`2026/04/15`', '商品日期'),
|
||
'date_analysis': ('📅 請輸入分析日期\n格式：`2026/04/15`', '分析日期'),
|
||
'date_ppt_daily': ('📅 請輸入日報日期\n格式：`2026/04/15`', 'PPT日期'),
|
||
'date_ppt_monthly':('📅 請輸入月報月份\n格式：`2026/03`', 'PPT月份'),
|
||
'goal_daily': ('🎯 請輸入每日業績目標金額（NT$）\n例如：`150000`', '日目標'),
|
||
'goal_monthly': ('🎯 請輸入每月業績目標金額（NT$）\n例如：`3000000`', '月目標'),
|
||
'goal_quarterly': ('🎯 請輸入每季業績目標金額（NT$）\n例如：`9000000`', '季目標'),
|
||
'goal_half': ('🎯 請輸入半年業績目標金額（NT$）\n例如：`18000000`', '半年目標'),
|
||
'goal_yearly': ('🎯 請輸入全年業績目標金額（NT$）\n例如：`36000000`', '年目標'),
|
||
'search_compare': ('🔍 請輸入商品關鍵字\n例如：`休足時間貼片`', '比價關鍵字'),
|
||
'date_trend_month': ('📅 請輸入查詢月份\n格式：`2026/03`', '趨勢月份'),
|
||
'date_trend_year': ('📅 請輸入查詢年份\n格式：`2026`', '趨勢年份'),
|
||
'date_trend_quarter':('📅 請輸入查詢季度\n格式：`2026/Q1`（Q1~Q4）', '趨勢季度'),
|
||
'date_competitor': ('📅 請輸入競品分析日期\n格式：`2026/04/15`', '競品日期'),
|
||
'promo_range': ('🎉 請輸入促銷日期範圍\n格式：`2026/04/01-2026/04/07`\n（活動開始日-活動結束日）', '促銷範圍'),
|
||
}
|
||
|
||
|
||
def sales_quick_kb(date_str):
|
||
try:
|
||
from datetime import datetime as dt
|
||
d = dt.strptime(date_str.replace('/', '-'), '%Y-%m-%d').date()
|
||
yesterday = (d - timedelta(days=1)).strftime('%Y/%m/%d')
|
||
return [
|
||
_row(('⬅️ 昨日業績', f'cmd:sales:{yesterday}'), ('🏆 熱銷商品', f'cmd:top:{date_str}')),
|
||
_row(('📋 完整報表', f'cmd:report:{date_str}')),
|
||
]
|
||
except Exception:
|
||
return None
|
||
|
||
|
||
# ── 資料查詢 ──────────────────────────────────────────────────
|
||
def _db():
|
||
return DatabaseManager().engine
|
||
|
||
|
||
def normalize_date(s: str) -> str:
|
||
return s.replace('-', '/') if s else s
|
||
|
||
|
||
def latest_date() -> str:
|
||
try:
|
||
with _db().connect() as c:
|
||
row = c.execute(
|
||
text('SELECT MAX("日期") FROM realtime_sales_monthly')
|
||
).fetchone()
|
||
return str(row[0]) if row and row[0] else None
|
||
except Exception:
|
||
return None
|
||
|
||
|
||
configure_menu_keyboards(latest_date_provider=latest_date, goals=_GOALS, taipei_tz=TAIPEI_TZ)
|
||
|
||
|
||
def query_sales(d: str) -> dict:
|
||
try:
|
||
with _db().connect() as c:
|
||
row = c.execute(text("""
|
||
SELECT COUNT(DISTINCT "訂單編號"),
|
||
COALESCE(SUM(CAST("總業績" AS FLOAT)),0),
|
||
COALESCE(SUM(CAST("總成本" AS FLOAT)),0),
|
||
COUNT(DISTINCT "商品ID")
|
||
FROM realtime_sales_monthly WHERE "日期"=:d
|
||
"""), {'d': d}).fetchone()
|
||
if row and row[0] > 0:
|
||
rev, cost, orders = row[1], row[2], row[0]
|
||
return {'found': True, 'date': d, 'orders': orders, 'revenue': rev,
|
||
'avg_order': rev/orders if orders else 0,
|
||
'gross_margin': (rev-cost)/rev*100 if rev>0 else 0,
|
||
'products': row[3]}
|
||
return {'found': False, 'date': d}
|
||
except Exception as e:
|
||
sys_log.error(f"[OpenClawBot] query_sales: {e}")
|
||
return {'found': False, 'date': d}
|
||
|
||
|
||
def query_top_products(d, lim=10):
|
||
try:
|
||
with _db().connect() as c:
|
||
rows = c.execute(text("""
|
||
SELECT "商品ID", "商品名稱", SUM(CAST("總業績" AS FLOAT)),
|
||
SUM(CAST("數量" AS INTEGER))
|
||
FROM realtime_sales_monthly WHERE "日期"=:d
|
||
GROUP BY "商品ID", "商品名稱" ORDER BY 3 DESC LIMIT :lim
|
||
"""), {'d': d, 'lim': lim}).fetchall()
|
||
return [{'id': r[0], 'name': r[1], 'revenue': r[2], 'qty': r[3]} for r in rows]
|
||
except Exception:
|
||
return []
|
||
|
||
|
||
def query_vendor_summary(start_date: str, end_date: str, lim: int = 30) -> dict:
|
||
"""查詢期間廠商業績摘要（vendor PPT 用）
|
||
|
||
回傳：{
|
||
vendor_ranking: [{name, sales, profit, margin, qty, orders}, ...] (TOP lim),
|
||
kpis: {total_sales, total_profit, avg_margin, vendor_count},
|
||
period_label: 'YYYY/MM/DD ~ YYYY/MM/DD'
|
||
}
|
||
"""
|
||
try:
|
||
with _db().connect() as c:
|
||
row = c.execute(text("""
|
||
SELECT COUNT(DISTINCT "廠商名稱"),
|
||
COALESCE(SUM(CAST("總業績" AS FLOAT)), 0),
|
||
COALESCE(SUM(CAST("總成本" AS FLOAT)), 0)
|
||
FROM realtime_sales_monthly
|
||
WHERE CAST("日期" AS DATE) BETWEEN CAST(:s AS DATE) AND CAST(:e AS DATE)
|
||
AND "廠商名稱" IS NOT NULL AND "廠商名稱" != ''
|
||
"""), {'s': start_date.replace('/', '-'), 'e': end_date.replace('/', '-')}).fetchone()
|
||
|
||
vendor_rows = c.execute(text("""
|
||
SELECT "廠商名稱",
|
||
SUM(CAST("總業績" AS FLOAT)) AS sales,
|
||
SUM(CAST("總成本" AS FLOAT)) AS cost,
|
||
SUM(CAST("數量" AS INTEGER)) AS qty,
|
||
COUNT(DISTINCT "訂單編號") AS orders
|
||
FROM realtime_sales_monthly
|
||
WHERE CAST("日期" AS DATE) BETWEEN CAST(:s AS DATE) AND CAST(:e AS DATE)
|
||
AND "廠商名稱" IS NOT NULL AND "廠商名稱" != ''
|
||
GROUP BY "廠商名稱"
|
||
ORDER BY sales DESC
|
||
LIMIT :lim
|
||
"""), {'s': start_date.replace('/', '-'),
|
||
'e': end_date.replace('/', '-'),
|
||
'lim': lim}).fetchall()
|
||
|
||
vcount, total_sales, total_cost = (row[0] or 0), float(row[1] or 0), float(row[2] or 0)
|
||
total_profit = total_sales - total_cost
|
||
avg_margin = total_profit / total_sales * 100 if total_sales else 0
|
||
|
||
vendor_ranking = []
|
||
for r in vendor_rows:
|
||
sales = float(r[1] or 0)
|
||
cost = float(r[2] or 0)
|
||
profit = sales - cost
|
||
margin = profit / sales * 100 if sales else 0
|
||
vendor_ranking.append({
|
||
'name': r[0],
|
||
'sales': sales,
|
||
'profit': profit,
|
||
'margin': margin,
|
||
'qty': int(r[3] or 0),
|
||
'orders': int(r[4] or 0),
|
||
})
|
||
|
||
return {
|
||
'vendor_ranking': vendor_ranking,
|
||
'kpis': {
|
||
'total_sales': total_sales,
|
||
'total_profit': total_profit,
|
||
'avg_margin': avg_margin,
|
||
'vendor_count': vcount,
|
||
},
|
||
'period_label': f"{start_date} ~ {end_date}",
|
||
}
|
||
except Exception as e:
|
||
sys_log.error(f"[query_vendor_summary] {e}")
|
||
return {'vendor_ranking': [], 'kpis': {}, 'period_label': ''}
|
||
|
||
|
||
def query_top_vendors(d, lim=10):
|
||
try:
|
||
with _db().connect() as c:
|
||
rows = c.execute(text("""
|
||
SELECT "廠商名稱", SUM(CAST("總業績" AS FLOAT))
|
||
FROM realtime_sales_monthly
|
||
WHERE "日期"=:d AND "廠商名稱" IS NOT NULL AND "廠商名稱"!=''
|
||
GROUP BY "廠商名稱" ORDER BY 2 DESC LIMIT :lim
|
||
"""), {'d': d, 'lim': lim}).fetchall()
|
||
return [{'name': r[0], 'revenue': r[1]} for r in rows]
|
||
except Exception:
|
||
return []
|
||
|
||
|
||
def query_weekly_trend():
|
||
try:
|
||
with _db().connect() as c:
|
||
rows = c.execute(text("""
|
||
SELECT "日期",
|
||
COALESCE(SUM(CAST("總業績" AS FLOAT)),0),
|
||
COUNT(DISTINCT "訂單編號")
|
||
FROM realtime_sales_monthly
|
||
WHERE CAST("日期" AS DATE) >= CURRENT_DATE - INTERVAL '7 days'
|
||
GROUP BY "日期" ORDER BY "日期" DESC LIMIT 7
|
||
""")).fetchall()
|
||
return [{'date': str(r[0]), 'revenue': r[1], 'orders': int(r[2] or 0)} for r in rows]
|
||
except Exception as e:
|
||
sys_log.error(f"[OpenClawBot] weekly_trend: {e}")
|
||
return []
|
||
|
||
|
||
def query_trend_range(start_str: str, end_str: str) -> list:
|
||
"""指定區間每日業績（用於趨勢圖，回傳 [{'date', 'revenue', 'orders'}] 按日期升序）"""
|
||
try:
|
||
with _db().connect() as c:
|
||
rows = c.execute(text("""
|
||
SELECT "日期",
|
||
COALESCE(SUM(CAST("總業績" AS FLOAT)), 0),
|
||
COUNT(DISTINCT "訂單編號")
|
||
FROM realtime_sales_monthly
|
||
WHERE CAST("日期" AS DATE) BETWEEN CAST(:s AS DATE) AND CAST(:e AS DATE)
|
||
GROUP BY "日期" ORDER BY "日期" ASC
|
||
"""), {'s': start_str.replace('/', '-'), 'e': end_str.replace('/', '-')}).fetchall()
|
||
return [{'date': str(r[0]), 'revenue': float(r[1]), 'orders': int(r[2] or 0)} for r in rows]
|
||
except Exception as e:
|
||
sys_log.error(f"[OpenClawBot] query_trend_range: {e}")
|
||
return []
|
||
|
||
|
||
def query_monthly_summary(year: int, month: int) -> dict:
|
||
"""取得指定年月業績摘要（逐日 + 合計 + TOP10商品/廠商）
|
||
支援 YYYY/MM/DD 與 YYYY-MM-DD 兩種日期格式
|
||
"""
|
||
month_str = f"{year}/{month:02d}"
|
||
# BETWEEN 比 LIKE 更可靠，且同時支援 DATE 與 TEXT 格式
|
||
import calendar as _cal2
|
||
last_day = _cal2.monthrange(year, month)[1]
|
||
start_date = f"{year}-{month:02d}-01"
|
||
end_date = f"{year}-{month:02d}-{last_day:02d}"
|
||
try:
|
||
with _db().connect() as c:
|
||
# 月合計 — 用 BETWEEN 避免 LIKE 格式相依
|
||
row = c.execute(text("""
|
||
SELECT COUNT(DISTINCT "訂單編號"),
|
||
COALESCE(SUM(CAST("總業績" AS FLOAT)),0),
|
||
COALESCE(SUM(CAST("總成本" AS FLOAT)),0),
|
||
COUNT(DISTINCT "商品ID"),
|
||
COUNT(DISTINCT "日期")
|
||
FROM realtime_sales_monthly
|
||
WHERE CAST("日期" AS DATE)
|
||
BETWEEN CAST(:s AS DATE) AND CAST(:e AS DATE)
|
||
"""), {'s': start_date, 'e': end_date}).fetchone()
|
||
|
||
daily_rows = c.execute(text("""
|
||
SELECT "日期", SUM(CAST("總業績" AS FLOAT)) as rev,
|
||
COUNT(DISTINCT "訂單編號") as orders
|
||
FROM realtime_sales_monthly
|
||
WHERE CAST("日期" AS DATE)
|
||
BETWEEN CAST(:s AS DATE) AND CAST(:e AS DATE)
|
||
GROUP BY "日期" ORDER BY "日期" ASC
|
||
"""), {'s': start_date, 'e': end_date}).fetchall()
|
||
|
||
prod_rows = c.execute(text("""
|
||
SELECT "商品ID", "商品名稱",
|
||
SUM(CAST("總業績" AS FLOAT)) as rev,
|
||
SUM(CAST("數量" AS INTEGER)) as qty,
|
||
COUNT(DISTINCT "訂單編號") as orders
|
||
FROM realtime_sales_monthly
|
||
WHERE CAST("日期" AS DATE)
|
||
BETWEEN CAST(:s AS DATE) AND CAST(:e AS DATE)
|
||
GROUP BY "商品ID", "商品名稱" ORDER BY 3 DESC LIMIT 50
|
||
"""), {'s': start_date, 'e': end_date}).fetchall()
|
||
|
||
vendor_rows = c.execute(text("""
|
||
SELECT "廠商名稱", SUM(CAST("總業績" AS FLOAT)) as rev
|
||
FROM realtime_sales_monthly
|
||
WHERE CAST("日期" AS DATE)
|
||
BETWEEN CAST(:s AS DATE) AND CAST(:e AS DATE)
|
||
AND "廠商名稱" IS NOT NULL AND "廠商名稱" != ''
|
||
GROUP BY "廠商名稱" ORDER BY 2 DESC LIMIT 10
|
||
"""), {'s': start_date, 'e': end_date}).fetchall()
|
||
|
||
if not row or row[0] == 0:
|
||
return {'found': False, 'month': month_str}
|
||
|
||
orders, revenue, cost = row[0], row[1], row[2]
|
||
return {
|
||
'found': True, 'month': month_str,
|
||
'orders': orders, 'revenue': revenue,
|
||
'cost': cost,
|
||
'gross_margin': (revenue - cost) / revenue * 100 if revenue > 0 else 0,
|
||
'avg_order': revenue / orders if orders else 0,
|
||
'products': row[3], 'days_with_data': row[4],
|
||
'daily': [{'date': str(r[0]), 'revenue': float(r[1]), 'orders': int(r[2])}
|
||
for r in daily_rows],
|
||
'top_products': [{'id': r[0], 'name': r[1], 'revenue': float(r[2]),
|
||
'qty': int(r[3] or 0), 'orders': int(r[4] or 0)}
|
||
for r in prod_rows],
|
||
'top_vendors': [{'name': r[0], 'revenue': float(r[1])} for r in vendor_rows],
|
||
}
|
||
except Exception as e:
|
||
sys_log.error(f"[OpenClawBot] query_monthly_summary {month_str}: {e}")
|
||
return {'found': False, 'month': month_str}
|
||
|
||
|
||
def query_date_range(start_str: str, end_str: str) -> dict:
|
||
"""查詢指定日期區間業績（start/end 格式 YYYY/MM/DD）"""
|
||
try:
|
||
with _db().connect() as c:
|
||
row = c.execute(text("""
|
||
SELECT COUNT(DISTINCT "訂單編號"),
|
||
COALESCE(SUM(CAST("總業績" AS FLOAT)),0),
|
||
COALESCE(SUM(CAST("總成本" AS FLOAT)),0),
|
||
COUNT(DISTINCT "商品ID"),
|
||
COUNT(DISTINCT "日期")
|
||
FROM realtime_sales_monthly
|
||
WHERE CAST("日期" AS DATE) BETWEEN CAST(:s AS DATE) AND CAST(:e AS DATE)
|
||
"""), {'s': start_str.replace('/', '-'), 'e': end_str.replace('/', '-')}).fetchone()
|
||
|
||
daily = c.execute(text("""
|
||
SELECT "日期", SUM(CAST("總業績" AS FLOAT))
|
||
FROM realtime_sales_monthly
|
||
WHERE CAST("日期" AS DATE) BETWEEN CAST(:s AS DATE) AND CAST(:e AS DATE)
|
||
GROUP BY "日期" ORDER BY "日期" ASC
|
||
"""), {'s': start_str.replace('/', '-'), 'e': end_str.replace('/', '-')}).fetchall()
|
||
|
||
if not row or row[0] == 0:
|
||
return {'found': False, 'range': f'{start_str}~{end_str}'}
|
||
|
||
orders, revenue, cost = row[0], row[1], row[2]
|
||
return {
|
||
'found': True, 'range': f'{start_str}~{end_str}',
|
||
'orders': orders, 'revenue': revenue,
|
||
'gross_margin': (revenue - cost) / revenue * 100 if revenue > 0 else 0,
|
||
'avg_order': revenue / orders if orders else 0,
|
||
'products': row[3], 'days_with_data': row[4],
|
||
'daily': [{'date': str(r[0]), 'revenue': float(r[1])} for r in daily],
|
||
}
|
||
except Exception as e:
|
||
sys_log.error(f"[OpenClawBot] query_date_range: {e}")
|
||
return {'found': False, 'range': f'{start_str}~{end_str}'}
|
||
|
||
|
||
def query_period_summary(start_date: str, end_date: str) -> dict:
|
||
"""期間業績完整摘要（quarterly / half_yearly / annual / ttm 共用）
|
||
|
||
回傳：{
|
||
kpis: {revenue, orders, gross_margin, avg_order, product_count, vendor_count, days},
|
||
monthly_breakdown: [{month, revenue, orders, gross_margin}],
|
||
top_products: [...],
|
||
top_categories: [...],
|
||
top_vendors: [...],
|
||
found: bool
|
||
}
|
||
"""
|
||
try:
|
||
s = start_date.replace('/', '-')
|
||
e = end_date.replace('/', '-')
|
||
with _db().connect() as c:
|
||
row = c.execute(text("""
|
||
SELECT COUNT(DISTINCT "訂單編號"),
|
||
COALESCE(SUM(CAST("總業績" AS FLOAT)), 0),
|
||
COALESCE(SUM(CAST("總成本" AS FLOAT)), 0),
|
||
COUNT(DISTINCT "商品ID"),
|
||
COUNT(DISTINCT "廠商名稱"),
|
||
COUNT(DISTINCT "日期")
|
||
FROM realtime_sales_monthly
|
||
WHERE CAST("日期" AS DATE) BETWEEN CAST(:s AS DATE) AND CAST(:e AS DATE)
|
||
"""), {'s': s, 'e': e}).fetchone()
|
||
|
||
# 月度聚合（YYYY-MM）
|
||
monthly_rows = c.execute(text("""
|
||
SELECT TO_CHAR(CAST("日期" AS DATE), 'YYYY-MM') AS ym,
|
||
SUM(CAST("總業績" AS FLOAT)) AS rev,
|
||
SUM(CAST("總成本" AS FLOAT)) AS cost,
|
||
COUNT(DISTINCT "訂單編號") AS orders
|
||
FROM realtime_sales_monthly
|
||
WHERE CAST("日期" AS DATE) BETWEEN CAST(:s AS DATE) AND CAST(:e AS DATE)
|
||
GROUP BY ym ORDER BY ym ASC
|
||
"""), {'s': s, 'e': e}).fetchall()
|
||
|
||
# TOP 50 商品
|
||
prod_rows = c.execute(text("""
|
||
SELECT "商品ID", "商品名稱",
|
||
SUM(CAST("總業績" AS FLOAT)) AS rev,
|
||
SUM(CAST("數量" AS INTEGER)) AS qty,
|
||
COUNT(DISTINCT "訂單編號") AS orders
|
||
FROM realtime_sales_monthly
|
||
WHERE CAST("日期" AS DATE) BETWEEN CAST(:s AS DATE) AND CAST(:e AS DATE)
|
||
GROUP BY "商品ID", "商品名稱"
|
||
ORDER BY 3 DESC LIMIT 50
|
||
"""), {'s': s, 'e': e}).fetchall()
|
||
|
||
# TOP 8 品類
|
||
cat_rows = c.execute(text("""
|
||
SELECT "商品分類L1", SUM(CAST("總業績" AS FLOAT)) AS rev
|
||
FROM realtime_sales_monthly
|
||
WHERE CAST("日期" AS DATE) BETWEEN CAST(:s AS DATE) AND CAST(:e AS DATE)
|
||
AND "商品分類L1" IS NOT NULL AND "商品分類L1" != ''
|
||
GROUP BY "商品分類L1" ORDER BY 2 DESC LIMIT 8
|
||
"""), {'s': s, 'e': e}).fetchall()
|
||
|
||
# TOP 30 廠商
|
||
vendor_rows = c.execute(text("""
|
||
SELECT "廠商名稱",
|
||
SUM(CAST("總業績" AS FLOAT)) AS rev,
|
||
SUM(CAST("總成本" AS FLOAT)) AS cost
|
||
FROM realtime_sales_monthly
|
||
WHERE CAST("日期" AS DATE) BETWEEN CAST(:s AS DATE) AND CAST(:e AS DATE)
|
||
AND "廠商名稱" IS NOT NULL AND "廠商名稱" != ''
|
||
GROUP BY "廠商名稱" ORDER BY 2 DESC LIMIT 30
|
||
"""), {'s': s, 'e': e}).fetchall()
|
||
|
||
if not row or row[0] == 0:
|
||
return {'found': False}
|
||
|
||
orders, revenue, cost = int(row[0]), float(row[1]), float(row[2])
|
||
gm = (revenue - cost) / revenue * 100 if revenue > 0 else 0
|
||
|
||
return {
|
||
'found': True,
|
||
'kpis': {
|
||
'revenue': revenue,
|
||
'orders': orders,
|
||
'gross_margin': gm,
|
||
'avg_order': revenue / orders if orders else 0,
|
||
'product_count': int(row[3] or 0),
|
||
'vendor_count': int(row[4] or 0),
|
||
'days': int(row[5] or 0),
|
||
},
|
||
'monthly_breakdown': [
|
||
{'month': r[0],
|
||
'revenue': float(r[1] or 0),
|
||
'gross_margin': ((float(r[1] or 0) - float(r[2] or 0)) / float(r[1] or 1) * 100)
|
||
if float(r[1] or 0) else 0,
|
||
'orders': int(r[3] or 0)}
|
||
for r in monthly_rows
|
||
],
|
||
'top_products': [
|
||
{'id': r[0], 'name': r[1], 'revenue': float(r[2]),
|
||
'qty': int(r[3] or 0), 'orders': int(r[4] or 0)}
|
||
for r in prod_rows
|
||
],
|
||
'top_categories': [
|
||
{'cat': r[0], 'revenue': float(r[1])} for r in cat_rows
|
||
],
|
||
'top_vendors': [
|
||
{'name': r[0],
|
||
'sales': float(r[1] or 0),
|
||
'profit': float(r[1] or 0) - float(r[2] or 0),
|
||
'margin': ((float(r[1] or 0) - float(r[2] or 0)) / float(r[1] or 1) * 100)
|
||
if float(r[1] or 0) else 0}
|
||
for r in vendor_rows
|
||
],
|
||
}
|
||
except Exception as e:
|
||
sys_log.error(f"[query_period_summary] {e}")
|
||
return {'found': False}
|
||
|
||
|
||
def query_available_months() -> list:
|
||
"""取得 DB 中有資料的月份清單（支援 YYYY/MM/DD 和 YYYY-MM-DD 兩種日期格式）"""
|
||
try:
|
||
with _db().connect() as c:
|
||
rows = c.execute(text("""
|
||
SELECT REPLACE(SUBSTRING("日期"::TEXT, 1, 7), '-', '/') AS ym,
|
||
COUNT(DISTINCT "日期") AS days
|
||
FROM realtime_sales_monthly
|
||
GROUP BY ym ORDER BY ym DESC LIMIT 24
|
||
""")).fetchall()
|
||
return [{'month': r[0], 'days': r[1]} for r in rows]
|
||
except Exception:
|
||
return []
|
||
|
||
|
||
def query_top_products_range(start_str: str, end_str: str, lim: int = 10) -> list:
|
||
"""指定區間熱銷商品"""
|
||
try:
|
||
with _db().connect() as c:
|
||
rows = c.execute(text("""
|
||
SELECT "商品ID", "商品名稱",
|
||
SUM(CAST("總業績" AS FLOAT)) as rev,
|
||
SUM(CAST("數量" AS INTEGER)) as qty
|
||
FROM realtime_sales_monthly
|
||
WHERE CAST("日期" AS DATE) BETWEEN CAST(:s AS DATE) AND CAST(:e AS DATE)
|
||
GROUP BY "商品ID", "商品名稱" ORDER BY 3 DESC LIMIT :lim
|
||
"""), {'s': start_str.replace('/', '-'),
|
||
'e': end_str.replace('/', '-'), 'lim': lim}).fetchall()
|
||
return [{'id': r[0], 'name': r[1], 'revenue': float(r[2]), 'qty': int(r[3])}
|
||
for r in rows]
|
||
except Exception:
|
||
return []
|
||
|
||
|
||
def resolve_date(q: str) -> str:
|
||
"""從問題文字解析目標日期，回傳 YYYY/MM/DD"""
|
||
import re
|
||
today = datetime.now(TAIPEI_TZ).date()
|
||
|
||
if '昨天' in q or '昨日' in q:
|
||
return (today - timedelta(days=1)).strftime('%Y/%m/%d')
|
||
if '前天' in q:
|
||
return (today - timedelta(days=2)).strftime('%Y/%m/%d')
|
||
if '大前天' in q:
|
||
return (today - timedelta(days=3)).strftime('%Y/%m/%d')
|
||
|
||
# 明確日期格式：2026/04/15 or 2026-04-15 or 04/15
|
||
m = re.search(r'(\d{4})[/-](\d{1,2})[/-](\d{1,2})', q)
|
||
if m:
|
||
return f"{m.group(1)}/{int(m.group(2)):02d}/{int(m.group(3)):02d}"
|
||
m = re.search(r'(\d{1,2})[/-](\d{1,2})(?![/-]\d)', q)
|
||
if m:
|
||
return f"{today.year}/{int(m.group(1)):02d}/{int(m.group(2)):02d}"
|
||
|
||
return latest_date() or today.strftime('%Y/%m/%d')
|
||
|
||
|
||
def resolve_query_intent(q: str) -> dict:
|
||
"""
|
||
NIM fallback 用：解析問題中的時間參數
|
||
types: 'day' / 'month' / 'range' / 'all'
|
||
（Function Calling 架構不需要此函數，僅 NIM 備援使用）
|
||
"""
|
||
import re
|
||
today = datetime.now(TAIPEI_TZ).date()
|
||
|
||
# ── 月份查詢 ─────────────────────────────────────────────
|
||
# 「上個月」「上月」
|
||
if any(kw in q for kw in ['上個月', '上月', '上月份']):
|
||
first = today.replace(day=1)
|
||
last_m = (first - timedelta(days=1))
|
||
return {'type': 'month', 'year': last_m.year, 'month': last_m.month}
|
||
|
||
# 「這個月」「本月」
|
||
if any(kw in q for kw in ['這個月', '本月', '這月', '當月']):
|
||
return {'type': 'month', 'year': today.year, 'month': today.month}
|
||
|
||
# 「N月」or「N月份」
|
||
m = re.search(r'(\d{1,2})月(?:份)?', q)
|
||
if m:
|
||
month_num = int(m.group(1))
|
||
if 1 <= month_num <= 12:
|
||
year = today.year if month_num <= today.month else today.year - 1
|
||
# 也可能帶年份
|
||
ym = re.search(r'(\d{4})年.*?(\d{1,2})月', q)
|
||
if ym:
|
||
year = int(ym.group(1))
|
||
month_num = int(ym.group(2))
|
||
return {'type': 'month', 'year': year, 'month': month_num}
|
||
|
||
# ── 週查詢 ───────────────────────────────────────────────
|
||
if any(kw in q for kw in ['上週', '上个星期', '上星期', '上週']):
|
||
mon = today - timedelta(days=today.weekday() + 7)
|
||
sun = mon + timedelta(days=6)
|
||
return {'type': 'range', 'start': mon.strftime('%Y/%m/%d'),
|
||
'end': sun.strftime('%Y/%m/%d'), 'label': '上週'}
|
||
if any(kw in q for kw in ['這週', '本週', '這周', '本周']):
|
||
mon = today - timedelta(days=today.weekday())
|
||
return {'type': 'range', 'start': mon.strftime('%Y/%m/%d'),
|
||
'end': today.strftime('%Y/%m/%d'), 'label': '本週'}
|
||
|
||
# ── 近N天 ─────────────────────────────────────────────────
|
||
m = re.search(r'近(\d+)[天日]', q)
|
||
if m:
|
||
n = int(m.group(1))
|
||
start = (today - timedelta(days=n - 1)).strftime('%Y/%m/%d')
|
||
return {'type': 'range', 'start': start,
|
||
'end': today.strftime('%Y/%m/%d'), 'label': f'近{n}天'}
|
||
|
||
# ── 多月查詢（全部資料/歷史）──────────────────────────────
|
||
if any(kw in q for kw in ['全部', '所有', '歷史', '整年', '今年', '全年', '各月']):
|
||
return {'type': 'all'}
|
||
|
||
# ── 預設：單日 ────────────────────────────────────────────
|
||
return {'type': 'day', 'date': resolve_date(q)}
|
||
|
||
|
||
# ── 格式化 ────────────────────────────────────────────────────
|
||
MEDALS = ['🥇','🥈','🥉','4️⃣','5️⃣','6️⃣','7️⃣','8️⃣','9️⃣','🔟']
|
||
|
||
|
||
def fmt_sales(sales, top, report_url):
|
||
if not sales.get('found'):
|
||
return (f"⚠️ *查無資料*\n\n"
|
||
f"`{sales.get('date','?')}` 尚無業績資料\n"
|
||
f"請確認當日資料是否已匯入系統。")
|
||
rev = float(sales.get('revenue', 0))
|
||
orders = sales.get('orders', 0) or 0
|
||
avg_o = float(sales.get('avg_order', 0))
|
||
margin = float(sales.get('gross_margin', 0))
|
||
prods = sales.get('products', 0) or 0
|
||
|
||
rev_wan = rev / 10000
|
||
|
||
lines = [
|
||
f"📊 *{sales['date']} 業績總覽*",
|
||
f"{'─' * 26}",
|
||
f"",
|
||
f"💰 總業績 `NT$ {rev:,.0f}`",
|
||
f"📦 訂單數量 `{orders:,}` 筆",
|
||
f"🛒 平均客單 `NT$ {avg_o:,.0f}`",
|
||
f"📈 毛利率 `{margin:.1f}%`",
|
||
f"🛍 商品件數 `{prods:,}` 件",
|
||
]
|
||
if top:
|
||
lines.append(f"")
|
||
lines.append(f"{'─' * 26}")
|
||
lines.append(f"🏆 *今日熱銷 TOP3*")
|
||
for i, p in enumerate(top[:3]):
|
||
pid = p.get('id', '') or ''
|
||
sid = _short_id(pid)
|
||
link = _pchome_link(pid, p['name'], 22)
|
||
rev_p = p['revenue']
|
||
qty = p.get('qty', '-')
|
||
lines.append(f" {MEDALS[i]} {link}")
|
||
lines.append(f" 🆔 `{sid}` 💰 `NT$ {rev_p:,.0f}` 📦 {qty}件")
|
||
lines.append(f"")
|
||
lines.append(f"📥 點選下方「完整報表」按鈕下載 Excel")
|
||
return "\n".join(lines)
|
||
|
||
|
||
def _esc(s: str) -> str:
|
||
"""Escape Telegram Markdown v1 special chars in plain text"""
|
||
for ch in ('_', '*', '`', '['):
|
||
s = s.replace(ch, f'\\{ch}')
|
||
return s
|
||
|
||
|
||
PCHOME_URL = 'https://24h.pchome.com.tw/prod/'
|
||
|
||
def _pchome_link(pid: str, name: str, max_len: int = 22) -> str:
|
||
"""生成可點選的 PChome 商品頁 Markdown 連結（Telegram Markdown v1）"""
|
||
safe = name[:max_len].replace('[', '').replace(']', '').replace('(', '').replace(')', '')
|
||
if pid and pid.strip():
|
||
return f"[{safe}]({PCHOME_URL}{pid.strip()})"
|
||
return _esc(name[:max_len])
|
||
|
||
|
||
def fmt_products(products, date_str):
|
||
if not products:
|
||
return f"⚠️ *查無資料*\n\n`{date_str}` 尚無商品業績資料"
|
||
# 計算合計
|
||
total_rev = sum(p['revenue'] for p in products)
|
||
total_qty = sum(p.get('qty', 0) or 0 for p in products)
|
||
lines = [
|
||
f"🏆 *{date_str} 熱銷商品排行*",
|
||
f"共 {len(products)} 件商品 | 合計 `NT$ {total_rev:,.0f}` | {total_qty:,} 件",
|
||
f"{'─' * 30}",
|
||
"",
|
||
]
|
||
for i, p in enumerate(products):
|
||
pid = p.get('id', '') or ''
|
||
sid = _short_id(pid)
|
||
link = _pchome_link(pid, p['name'], 22)
|
||
rev = p['revenue']
|
||
qty = p.get('qty', 0) or 0
|
||
pct = rev / total_rev * 100 if total_rev else 0
|
||
medal = MEDALS[i] if i < len(MEDALS) else f"`{i+1}.`"
|
||
bar_len = max(1, int(pct / 100 * 8))
|
||
mini_bar = '▪' * bar_len + '·' * (8 - bar_len)
|
||
lines.append(f"{medal} {link}")
|
||
lines.append(f" 🆔 `{sid}` 💰 `NT$ {rev:,.0f}` 📦 {qty}件 `{mini_bar}` {pct:.1f}%")
|
||
lines.append("")
|
||
lines.append(f"_💡 點商品名稱可開啟 PChome 商品頁_")
|
||
return "\n".join(lines)
|
||
|
||
|
||
def fmt_vendors(vendors, date_str):
|
||
if not vendors:
|
||
return f"⚠️ *查無資料*\n\n`{date_str}` 尚無廠商業績資料"
|
||
total_rev = sum(v['revenue'] for v in vendors)
|
||
lines = [
|
||
f"🏭 *{date_str} 熱銷廠商排行*",
|
||
f"共 {len(vendors)} 家廠商 | 合計 `NT$ {total_rev:,.0f}`",
|
||
f"{'─' * 30}",
|
||
"",
|
||
]
|
||
for i, v in enumerate(vendors):
|
||
medal = MEDALS[i] if i < len(MEDALS) else f"`{i+1}.`"
|
||
name = _esc(v['name'][:24])
|
||
rev = v['revenue']
|
||
pct = rev / total_rev * 100 if total_rev else 0
|
||
bar_len = max(1, int(pct / 100 * 8))
|
||
mini_bar = '▪' * bar_len + '·' * (8 - bar_len)
|
||
lines.append(f"{medal} *{name}*")
|
||
lines.append(f" 💰 `NT$ {rev:,.0f}` `{mini_bar}` 佔比 {pct:.1f}%")
|
||
lines.append("")
|
||
return "\n".join(lines)
|
||
|
||
|
||
def fmt_trend(weekly, period_label: str = ''):
|
||
"""格式化趨勢資料 — 彩色進度條 + emoji + 統計摘要（週/月用）"""
|
||
if not weekly:
|
||
return "⚠️ *尚無趨勢資料*\n\n請確認資料是否已匯入。"
|
||
|
||
# 按日期升序排列
|
||
data = sorted(weekly, key=lambda x: x['date'])
|
||
revs = [w['revenue'] for w in data]
|
||
max_rev = max(revs) if revs else 1
|
||
min_rev = min(revs) if revs else 0
|
||
avg_rev = sum(revs) / len(revs) if revs else 0
|
||
total = sum(revs)
|
||
BAR_LEN = 6
|
||
WEEKDAYS_ZH = ['週一', '週二', '週三', '週四', '週五', '週六', '週日']
|
||
|
||
label = period_label or f'近 {len(data)} 日'
|
||
lines = [f"📈 *業績趨勢 — {label}*", ""]
|
||
|
||
for i, w in enumerate(data):
|
||
rev = w['revenue']
|
||
filled = max(0, min(BAR_LEN, int(rev / max_rev * BAR_LEN) if max_rev else 0))
|
||
empty = BAR_LEN - filled
|
||
|
||
# 台灣慣例：上升=🟥紅, 下降=🟩綠, 初始=🟦藍
|
||
if i == 0:
|
||
bar_str = '🟦' * filled + '▪️' * empty
|
||
dir_icon = '🔵'
|
||
pct_str = ''
|
||
else:
|
||
diff = rev - data[i - 1]['revenue']
|
||
prev = data[i - 1]['revenue'] or 1
|
||
pct_chg = diff / prev * 100
|
||
if diff > 0:
|
||
bar_str = '🟥' * filled + '▪️' * empty
|
||
dir_icon = '📈'
|
||
pct_str = f" ▲`{pct_chg:.1f}%`"
|
||
elif diff < 0:
|
||
bar_str = '🟩' * filled + '▪️' * empty
|
||
dir_icon = '📉'
|
||
pct_str = f" ▼`{abs(pct_chg):.1f}%`"
|
||
else:
|
||
bar_str = '🟦' * filled + '▪️' * empty
|
||
dir_icon = '→'
|
||
pct_str = ' —'
|
||
|
||
# 日期 + 星期
|
||
try:
|
||
from datetime import datetime as _dt2
|
||
d_obj = _dt2.strptime(w['date'].replace('/', '-'), '%Y-%m-%d')
|
||
date_disp = d_obj.strftime('%m/%d')
|
||
wday = WEEKDAYS_ZH[d_obj.weekday()]
|
||
except Exception:
|
||
date_disp = w['date'][-5:]
|
||
wday = ''
|
||
|
||
rev_wan = rev / 10000
|
||
lines.append(f"{dir_icon} `{date_disp}` {wday} {bar_str}{pct_str}")
|
||
lines.append(f" 💰 `NT$ {rev:>10,.0f}` ({rev_wan:.1f}萬)")
|
||
|
||
# 統計摘要
|
||
lines.append("")
|
||
lines.append("─────────────────────")
|
||
lines.append("📊 *統計摘要*")
|
||
lines.append(f" 📦 合計：`NT$ {total:,.0f}` ({total/10000:.1f}萬)")
|
||
lines.append(f" 📐 日均：`NT$ {avg_rev:,.0f}` ({avg_rev/10000:.1f}萬)")
|
||
if len(revs) >= 2:
|
||
up_days = sum(1 for j in range(1, len(revs)) if revs[j] > revs[j - 1])
|
||
down_days = sum(1 for j in range(1, len(revs)) if revs[j] < revs[j - 1])
|
||
flat_days = len(revs) - 1 - up_days - down_days
|
||
lines.append(f" 📈 上升 {up_days}天 📉 下降 {down_days}天 → 持平 {flat_days}天")
|
||
lines.append(f" 🏆 最高：`NT$ {max_rev:,.0f}` ({max_rev/10000:.1f}萬)")
|
||
lines.append(f" 🔻 最低：`NT$ {min_rev:,.0f}` ({min_rev/10000:.1f}萬)")
|
||
last_diff = revs[-1] - revs[-2]
|
||
last_pct = last_diff / (revs[-2] or 1) * 100
|
||
icon = '🔴' if last_diff >= 0 else '🟢'
|
||
arrow = '▲' if last_diff >= 0 else '▼'
|
||
lines.append(f" {icon} 最新日：{arrow}`{abs(last_pct):.1f}%` (`NT$ {last_diff:+,.0f}`)")
|
||
|
||
return "\n".join(lines)
|
||
|
||
|
||
def fmt_trend_summary(data: list, period_label: str) -> str:
|
||
"""長週期趨勢摘要（不顯示逐日列表，只顯示統計，搭配圖表使用）"""
|
||
if not data:
|
||
return "⚠️ *尚無趨勢資料*\n\n請確認資料是否已匯入。"
|
||
data = sorted(data, key=lambda x: x['date'])
|
||
revs = [w['revenue'] for w in data]
|
||
total = sum(revs)
|
||
avg_rev = total / len(revs) if revs else 0
|
||
max_rev = max(revs)
|
||
min_rev = min(revs)
|
||
max_date = data[revs.index(max_rev)]['date'][-5:]
|
||
min_date = data[revs.index(min_rev)]['date'][-5:]
|
||
up_days = sum(1 for j in range(1, len(revs)) if revs[j] > revs[j-1])
|
||
dn_days = sum(1 for j in range(1, len(revs)) if revs[j] < revs[j-1])
|
||
ft_days = len(revs) - 1 - up_days - dn_days
|
||
def _fmt_date(d):
|
||
# 將 YYYY-MM-DD 或 YYYY/MM/DD 轉成 YYYY/MM/DD
|
||
return str(d).replace('-', '/')[:10]
|
||
start_d = _fmt_date(data[0]['date'])
|
||
end_d = _fmt_date(data[-1]['date'])
|
||
|
||
# 前半段 vs 後半段比較
|
||
mid = len(revs) // 2
|
||
first = sum(revs[:mid]) / mid if mid else 0
|
||
last = sum(revs[mid:]) / (len(revs) - mid) if len(revs) > mid else 0
|
||
trend_icon = '📈' if last >= first else '📉'
|
||
trend_pct = (last - first) / first * 100 if first else 0
|
||
|
||
lines = [
|
||
f"📊 *業績趨勢摘要 — {period_label}*",
|
||
f" {start_d} ~ {end_d} 共 {len(revs)} 天有資料",
|
||
"",
|
||
f" 💰 *合計*：`NT$ {total:,.0f}` ({total/10000:.1f}萬)",
|
||
f" 📐 *日均*：`NT$ {avg_rev:,.0f}` ({avg_rev/10000:.1f}萬)",
|
||
"",
|
||
f" 🏆 *最高*：`NT$ {max_rev:,.0f}` ({max_rev/10000:.1f}萬) [{max_date}]",
|
||
f" 🔻 *最低*：`NT$ {min_rev:,.0f}` ({min_rev/10000:.1f}萬) [{min_date}]",
|
||
"",
|
||
f" 📈 上升 {up_days}天 📉 下降 {dn_days}天 → 持平 {ft_days}天",
|
||
f" {trend_icon} 整體趨勢：後半段日均 vs 前半段 {'+' if trend_pct >= 0 else ''}{trend_pct:.1f}%",
|
||
"",
|
||
"_📊 走勢圖如下_",
|
||
]
|
||
return "\n".join(lines)
|
||
|
||
|
||
def gen_aggregated_chart(data_points: list, title: str, granularity: str = 'weekly') -> str:
|
||
"""產生聚合柱狀圖 PNG — 週/月粒度，上升紅/下降綠
|
||
granularity: 'weekly' → 按週分組, 'monthly' → 按月分組
|
||
"""
|
||
try:
|
||
import matplotlib
|
||
matplotlib.use('Agg')
|
||
_setup_mpl_chinese()
|
||
import matplotlib.pyplot as plt
|
||
import matplotlib.patches as mpatches
|
||
from datetime import datetime as _dt3
|
||
import tempfile
|
||
from collections import defaultdict
|
||
|
||
data = sorted(data_points, key=lambda x: x['date'])
|
||
if not data:
|
||
return ''
|
||
|
||
buckets = defaultdict(float)
|
||
for w in data:
|
||
try:
|
||
d_obj = _dt3.strptime(w['date'].replace('/', '-'), '%Y-%m-%d')
|
||
if granularity == 'monthly':
|
||
key = d_obj.strftime('%Y/%m')
|
||
else:
|
||
# ISO week key: YYYY-Www
|
||
iso = d_obj.isocalendar()
|
||
key = f"{iso[0]}/W{iso[1]:02d}"
|
||
except Exception:
|
||
continue
|
||
buckets[key] += w['revenue']
|
||
|
||
if not buckets:
|
||
return ''
|
||
|
||
labels = sorted(buckets.keys())
|
||
revenues = [buckets[k] / 10000 for k in labels] # 萬元
|
||
|
||
UP_COLOR = '#E53935'
|
||
DOWN_COLOR = '#43A047'
|
||
NEUTRAL = '#1565C0'
|
||
|
||
colors = []
|
||
for i, r in enumerate(revenues):
|
||
if i == 0:
|
||
colors.append(NEUTRAL)
|
||
elif r >= revenues[i - 1]:
|
||
colors.append(UP_COLOR)
|
||
else:
|
||
colors.append(DOWN_COLOR)
|
||
|
||
fig, ax = plt.subplots(figsize=(max(10, len(labels) * 0.9), 6))
|
||
fig.patch.set_facecolor('#FAFAFA')
|
||
ax.set_facecolor('#FAFAFA')
|
||
|
||
bars = ax.bar(range(len(labels)), revenues, color=colors,
|
||
edgecolor='white', linewidth=0.8, width=0.65)
|
||
|
||
# 金額標籤
|
||
for i, (bar, r) in enumerate(zip(bars, revenues)):
|
||
ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + max(revenues) * 0.02,
|
||
f'{r:.1f}萬', ha='center', va='bottom', fontsize=8.5,
|
||
color=colors[i], fontweight='bold')
|
||
|
||
# 漲跌百分比標籤
|
||
for i in range(1, len(revenues)):
|
||
pct = (revenues[i] - revenues[i-1]) / revenues[i-1] * 100 if revenues[i-1] else 0
|
||
arrow = '▲' if pct >= 0 else '▼'
|
||
ax.text(i, revenues[i] / 2,
|
||
f'{arrow}{abs(pct):.1f}%', ha='center', va='center',
|
||
fontsize=7.5, color='white', fontweight='bold')
|
||
|
||
# 平均線
|
||
avg = sum(revenues) / len(revenues)
|
||
ax.axhline(avg, color='#FF6F00', linestyle='--', linewidth=1.5, alpha=0.8,
|
||
label=f'平均 {avg:.1f}萬')
|
||
|
||
ax.set_xticks(range(len(labels)))
|
||
ax.set_xticklabels(labels, fontsize=9, rotation=30 if len(labels) > 8 else 0)
|
||
ax.set_ylabel('業績（萬元）', fontsize=11)
|
||
ax.set_title(title, fontsize=13, fontweight='bold', pad=12)
|
||
ax.grid(True, axis='y', alpha=0.25, linestyle='--', color='#BDBDBD')
|
||
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'{x:,.0f}'))
|
||
ax.legend(fontsize=9, framealpha=0.85)
|
||
|
||
up_p = mpatches.Patch(color=UP_COLOR, label='▲ 上升')
|
||
dn_p = mpatches.Patch(color=DOWN_COLOR, label='▼ 下降')
|
||
ax.legend(handles=[up_p, dn_p] + ax.get_legend_handles_labels()[0],
|
||
loc='upper left', fontsize=9, framealpha=0.85)
|
||
|
||
plt.tight_layout()
|
||
tmp = tempfile.NamedTemporaryFile(
|
||
suffix='.png', prefix='ocbot_agg_', delete=False, dir='/tmp')
|
||
plt.savefig(tmp.name, dpi=130, bbox_inches='tight')
|
||
plt.close()
|
||
return tmp.name
|
||
except ImportError:
|
||
sys_log.warning('[OpenClawBot] matplotlib not installed')
|
||
return ''
|
||
except Exception as e:
|
||
sys_log.error(f'[OpenClawBot] gen_aggregated_chart: {e}')
|
||
return ''
|
||
|
||
|
||
# ── AI 自然語言 ───────────────────────────────────────────────
|
||
|
||
# 功能說明關鍵詞（P8：讓使用者問功能時能得到正確答案）
|
||
_HELP_KEYWORDS = (
|
||
'怎麼用', '如何用', '怎麼操作', '教我', '說明', '功能', '按鈕',
|
||
'怎麼查', '哪個按鈕', '在哪裡', '怎麼產出', '怎麼看', '如何查',
|
||
'圖片比價', '照片比價', '如何比價', '怎麼比價',
|
||
'簡報怎麼', '怎麼出簡報', '如何產報告', 'PPT怎麼', 'ppt怎麼',
|
||
'目標怎麼設', '如何設定目標', '怎麼設目標',
|
||
'告警', '異常通知', '怎麼知悉',
|
||
'早報', '晚報', '週報', '自動推播',
|
||
'使用說明', '操作說明', 'help', '幫助',
|
||
)
|
||
|
||
_WAKEUP_KEYWORDS = (
|
||
'小o',
|
||
'小o小龍蝦',
|
||
'小o_小龍蝦',
|
||
'小龍蝦',
|
||
'openclaw',
|
||
)
|
||
|
||
_BUSINESS_KEYWORDS = (
|
||
'業績', '營收', '銷售', '銷量', '熱銷', '商品', '廠商', '目標',
|
||
'報表', '趨勢', '分析', '簡報', '比價', '競品', '補貨', '促銷',
|
||
'異常', '健康', '分類', '策略', '天氣', '新聞', '匯率', '節慶',
|
||
'價格', '今日', '昨日', '今天', '本週', '上週', '本月', '上月', '今年',
|
||
)
|
||
|
||
_GREETING_KEYWORDS = (
|
||
'你好', '嗨', '哈囉', '早安', '午安', '晚安', '在嗎', '你有空', '在不在',
|
||
'hello', 'hi', 'hey',
|
||
)
|
||
|
||
|
||
def _normalize_nl_query(q: str) -> str:
|
||
"""V-Fix：去除 NL 句尾符號與空白，降低誤判率。"""
|
||
return re.sub(r'[\s_\-,:;,.，。!?！？"“”\(\)\[\]<>]+', '', (q or '').lower())
|
||
|
||
|
||
def _contains_business_signal(q: str) -> bool:
|
||
"""V-Fix：是否包含可直接進 AI 查詢邏輯的業務關鍵字。"""
|
||
ql = (q or '').lower()
|
||
return any(kw in ql for kw in _BUSINESS_KEYWORDS)
|
||
|
||
|
||
def _looks_like_wakeup_prompt(q: str) -> bool:
|
||
"""V-Fix：防止只叫名字/打招呼時走自由生成。"""
|
||
ql = (q or '').lower().strip()
|
||
if not ql:
|
||
return False
|
||
|
||
compact = _normalize_nl_query(ql)
|
||
if not compact:
|
||
return False
|
||
|
||
if compact in _WAKEUP_KEYWORDS:
|
||
return True
|
||
|
||
if (
|
||
any(name in compact for name in _WAKEUP_KEYWORDS)
|
||
and not _contains_business_signal(ql)
|
||
and len(compact) <= 18
|
||
):
|
||
return True
|
||
|
||
if compact in _GREETING_KEYWORDS and len(compact) <= 10:
|
||
return True
|
||
|
||
return False
|
||
|
||
def _is_help_question(q: str) -> bool:
|
||
ql = q.lower()
|
||
return any(kw in ql for kw in _HELP_KEYWORDS)
|
||
|
||
|
||
# ══════════════════════════════════════════════════════════════
|
||
# Gemini Function Calling 工具定義
|
||
# AI 自主決定要呼叫哪些工具，不再靠 if/else 規則
|
||
# ══════════════════════════════════════════════════════════════
|
||
_FC_TOOLS = [{
|
||
"function_declarations": [
|
||
{
|
||
"name": "query_sales",
|
||
"description": (
|
||
"查詢公司內部業績資料庫。"
|
||
"當用戶詢問自家業績、銷售數字、訂單量、毛利率、熱銷商品、廠商排行等內部數據時使用。"
|
||
),
|
||
"parameters": {
|
||
"type": "OBJECT",
|
||
"properties": {
|
||
"period": {
|
||
"type": "STRING",
|
||
"enum": ["today","yesterday","this_week","last_week",
|
||
"this_month","last_month","date","month","range","all"],
|
||
"description": "查詢時間範圍"
|
||
},
|
||
"date": {"type": "STRING", "description": "具體日期 YYYY/MM/DD，period=date 時填"},
|
||
"year": {"type": "INTEGER", "description": "年份，period=month 時填"},
|
||
"month": {"type": "INTEGER", "description": "月份1-12，period=month 時填"},
|
||
"start_date": {"type": "STRING", "description": "開始日期 YYYY/MM/DD，period=range 時填"},
|
||
"end_date": {"type": "STRING", "description": "結束日期 YYYY/MM/DD，period=range 時填"},
|
||
},
|
||
"required": ["period"]
|
||
}
|
||
},
|
||
{
|
||
"name": "get_market_intel",
|
||
"description": (
|
||
"取得外部市場情報。"
|
||
"當用戶詢問市場趨勢、電商新聞、消費者討論（PTT/Dcard）、"
|
||
"熱搜關鍵字、YouTube爆紅商品、匯率、天氣、即將到來的節慶促銷檔期等外部資訊時使用。"
|
||
),
|
||
"parameters": {
|
||
"type": "OBJECT",
|
||
"properties": {
|
||
"sources": {
|
||
"type": "ARRAY",
|
||
"items": {
|
||
"type": "STRING",
|
||
"enum": ["news","trends","social","youtube","weather","exchange","calendar"]
|
||
},
|
||
"description": "需要哪些情報來源，可多選。全部不確定時選 ['news','trends','calendar']"
|
||
}
|
||
},
|
||
"required": ["sources"]
|
||
}
|
||
},
|
||
{
|
||
"name": "get_knowledge",
|
||
"description": (
|
||
"從歷史知識庫和過去 AI 分析記錄中語義檢索相關資訊。"
|
||
"當需要參考過去的分析結論、歷史業績模式、或相似問題的回答時使用。"
|
||
),
|
||
"parameters": {
|
||
"type": "OBJECT",
|
||
"properties": {
|
||
"query": {"type": "STRING", "description": "檢索關鍵字或問題描述"}
|
||
},
|
||
"required": ["query"]
|
||
}
|
||
},
|
||
{
|
||
"name": "check_data_freshness",
|
||
"description": (
|
||
"查詢內部業績資料的最新可用日期與可用月份清單。"
|
||
"ADR-019 Phase 2：在回答任何特定時段（本月/本週/某月某日）業績問題前，"
|
||
"先呼叫此工具確認資料是否已涵蓋該期間，避免回覆「業績為零」這類因 ETL "
|
||
"尚未匯入造成的虛假結論。月初/季初、用戶用『本月/本週』等相對時間詞時務必先呼叫。"
|
||
),
|
||
"parameters": {"type": "OBJECT", "properties": {}, "required": []}
|
||
}
|
||
]
|
||
}]
|
||
|
||
|
||
def _execute_tool(name: str, args: dict) -> dict:
|
||
"""執行 Gemini 指定的工具，回傳結構化結果"""
|
||
now = datetime.now(TAIPEI_TZ)
|
||
today = now.strftime("%Y/%m/%d")
|
||
yd = (now - timedelta(days=1)).strftime("%Y/%m/%d")
|
||
|
||
# ── query_sales ───────────────────────────────────────────
|
||
if name == "query_sales":
|
||
period = args.get("period", "today")
|
||
result = {}
|
||
|
||
if period in ("today", "date"):
|
||
d = args.get("date", today)
|
||
sales = query_sales(d)
|
||
tops = query_top_products(d, 10)
|
||
vens = query_top_vendors(d, 5)
|
||
result = {"date": d, "sales": sales, "top_products": tops, "top_vendors": vens}
|
||
|
||
elif period == "yesterday":
|
||
sales = query_sales(yd)
|
||
tops = query_top_products(yd, 10)
|
||
result = {"date": yd, "sales": sales, "top_products": tops}
|
||
|
||
elif period in ("this_week", "last_week"):
|
||
trend = query_weekly_trend()
|
||
result = {"weekly_trend": trend}
|
||
|
||
elif period == "this_month":
|
||
ms = query_monthly_summary(now.year, now.month)
|
||
result = {"monthly": ms, "year": now.year, "month": now.month}
|
||
|
||
elif period == "last_month":
|
||
first = now.replace(day=1)
|
||
lm = (first - timedelta(days=1))
|
||
ms = query_monthly_summary(lm.year, lm.month)
|
||
result = {"monthly": ms, "year": lm.year, "month": lm.month}
|
||
|
||
elif period == "month":
|
||
yr = args.get("year", now.year)
|
||
mo = args.get("month", now.month)
|
||
ms = query_monthly_summary(yr, mo)
|
||
result = {"monthly": ms, "year": yr, "month": mo}
|
||
|
||
elif period == "range":
|
||
s = args.get("start_date", yd)
|
||
e = args.get("end_date", today)
|
||
rng = query_date_range(s, e)
|
||
tops = query_top_products_range(s, e, 10)
|
||
result = {"range": rng, "start": s, "end": e, "top_products": tops}
|
||
|
||
elif period == "all":
|
||
history = query_daily_history(30)
|
||
avail = query_available_months()
|
||
result = {"history_30d": history, "available_months": avail}
|
||
|
||
# 附上可查詢月份清單
|
||
avail = query_available_months()
|
||
result["available_months"] = [m["month"] for m in avail] if avail else []
|
||
result["report_url"] = f"{MOMO_BASE_URL}/reports/daily/{args.get('date', today)}"
|
||
return result
|
||
|
||
# ── get_market_intel ──────────────────────────────────────
|
||
elif name == "get_market_intel":
|
||
sources = args.get("sources", ["news", "trends", "calendar"])
|
||
data = {}
|
||
source_map = {
|
||
"news": lambda: {**get_tw_media_news(), **{"google": get_ecommerce_news()}},
|
||
"trends": get_taiwan_trends,
|
||
"social": get_dcard_trends,
|
||
"youtube": get_youtube_trending,
|
||
"weather": get_taiwan_weather,
|
||
"exchange": get_twbank_exchange_rates,
|
||
"calendar": get_upcoming_events,
|
||
}
|
||
for src in sources:
|
||
if src in source_map:
|
||
try:
|
||
data[src] = source_map[src]()
|
||
except Exception as e:
|
||
data[src] = {"error": str(e)}
|
||
return data
|
||
|
||
# ── get_knowledge ─────────────────────────────────────────
|
||
elif name == "get_knowledge":
|
||
if not _LEARNING_ENABLED:
|
||
return {"results": [], "note": "知識庫未啟用"}
|
||
try:
|
||
items = retrieve_knowledge(args.get("query", ""), top_k=4, min_quality=0.4)
|
||
return {"results": [
|
||
{"category": i["category"], "title": i["title"],
|
||
"content": i["content"][:400], "score": i["similarity"]}
|
||
for i in items
|
||
]}
|
||
except Exception as e:
|
||
return {"results": [], "error": str(e)}
|
||
|
||
# ── check_data_freshness ──────────────────────────────────
|
||
elif name == "check_data_freshness":
|
||
# ADR-019 Phase 2：讓 agent 在回答業績問題前 probe 資料缺口
|
||
latest = latest_date() # 'YYYY-MM-DD' 或 None
|
||
avail_months = query_available_months() or []
|
||
result = {
|
||
"latest_data_date": latest,
|
||
"today": today,
|
||
"available_months": [m["month"] for m in avail_months],
|
||
"month_count": len(avail_months),
|
||
}
|
||
if latest:
|
||
try:
|
||
latest_dt = datetime.strptime(latest.replace('/', '-'), '%Y-%m-%d')
|
||
gap_days = (now.date() - latest_dt.date()).days
|
||
result["gap_days"] = gap_days
|
||
result["current_month_has_data"] = (
|
||
latest_dt.year == now.year and latest_dt.month == now.month
|
||
)
|
||
result["data_freshness_warning"] = (
|
||
"⚠️ 當月尚無資料，請改詢問上月" if not result["current_month_has_data"]
|
||
else ("⚠️ 資料落後 " + str(gap_days) + " 天" if gap_days > 2 else None)
|
||
)
|
||
except (ValueError, AttributeError):
|
||
pass
|
||
return result
|
||
|
||
return {"error": f"unknown tool: {name}"}
|
||
|
||
|
||
def openclaw_answer(question: str, chat_id: int = None):
|
||
"""
|
||
Function Calling 架構 — AI 自主決定查什麼、怎麼回答
|
||
不再靠 if/else 規則判斷意圖
|
||
|
||
ADR-019 Phase 4：可選 chat_id 啟用對話 state。傳入後 agent 會看到該 chat 的
|
||
最近對話歷史，並把本輪 (question, answer) 寫回 session 供下輪使用。
|
||
chat_id 為 None 時行為與舊版完全相同（無 multi-turn 記憶）。
|
||
"""
|
||
from services import openclaw_session
|
||
|
||
now = datetime.now(TAIPEI_TZ)
|
||
today_str = now.strftime("%Y/%m/%d")
|
||
history_ctx = openclaw_session.history_as_prompt(chat_id) if chat_id else ""
|
||
|
||
# ── 只叫名 / 問候：先導回主選單，避免題外市場回覆 ───────────────
|
||
if _looks_like_wakeup_prompt(question):
|
||
wakeup_text = (
|
||
"👋 *OpenClaw（小O）* 在！\n\n"
|
||
"你可以直接點下面按鈕，或直接問我：\n"
|
||
" 「今天業績如何？」\n"
|
||
" 「怎麼查看熱銷商品？」\n"
|
||
" 「有什麼市場情報？」"
|
||
)
|
||
return wakeup_text, main_menu_keyboard()
|
||
|
||
# ── 功能說明直接導 help ───────────────────────────────────
|
||
if _is_help_question(question):
|
||
help_text = (
|
||
"📖 *OpenClaw 功能說明*\n\n"
|
||
"你可以直接點下方按鈕進入對應功能，或問我：\n\n"
|
||
"🔍 *常見問題*\n"
|
||
" 「今天業績」→ 點 📊業績查詢 → 今日\n"
|
||
" 「熱銷商品」→ 點 🏆商品廠商 → 熱銷商品\n"
|
||
" 「出日報PPT」→ 點 📄簡報報表 → 日報\n"
|
||
" 「促銷分析」→ 點 📈智能分析 → 促銷追蹤\n"
|
||
" 「設定月目標」→ 點 🎯目標管理 → 月目標\n"
|
||
" 「圖片比價」→ 直接傳圖片給我\n"
|
||
" 「競品比較」→ 點 🔍競品日報\n\n"
|
||
"📌 *完整說明*：點下方「❓使用說明」按鈕\n\n"
|
||
"_或直接問我任何問題，我會自動找資料回答！_"
|
||
)
|
||
return help_text, quick_menu_keyboard()
|
||
|
||
from services.ollama_service import ollama_service
|
||
|
||
# ── Ollama 首選（使用傳統 prompt 注入上下文）─────────────────
|
||
try:
|
||
if ollama_service.check_connection():
|
||
intent = resolve_query_intent(question)
|
||
today = today_str
|
||
yd = (now - timedelta(days=1)).strftime("%Y/%m/%d")
|
||
d = intent.get("date", yd) if intent["type"] == "day" else yd
|
||
|
||
db_ctx = ""
|
||
if intent["type"] == "day":
|
||
s = query_sales(d)
|
||
t = query_top_products(d, 8)
|
||
if s.get("found"):
|
||
db_ctx = (
|
||
f"日期{d} 業績NT${s.get('revenue',0):,.0f} "
|
||
f"訂單{s.get('orders',0)}筆 毛利{s.get('gross_margin',0):.1f}% "
|
||
f"TOP5: " + " / ".join(
|
||
f"[{p.get('id','')}]{p['name']}(NT${p['revenue']:,.0f})"
|
||
for p in t[:5])
|
||
)
|
||
elif intent["type"] == "month":
|
||
ms = query_monthly_summary(intent["year"], intent["month"])
|
||
if ms.get("found"):
|
||
db_ctx = (f"{intent['year']}年{intent['month']:02d}月 "
|
||
f"業績NT${ms.get('revenue',0):,.0f} "
|
||
f"訂單{ms.get('orders',0)}筆 "
|
||
f"毛利{ms.get('gross_margin',0):.1f}%")
|
||
|
||
mcp_ctx = build_mcp_context(question)
|
||
|
||
sys_prompt = (
|
||
f"你是 OpenClaw（小O），電商智能助理。今天{today}。\n"
|
||
+ (f"【最近對話】\n{history_ctx}\n\n" if history_ctx else "")
|
||
+ (f"【業績資料】{db_ctx}\n" if db_ctx else "")
|
||
+ (f"【市場情報】{mcp_ctx[:400]}\n" if mcp_ctx else "")
|
||
+ "請用繁體中文直接回答，不要開場白，300字以內。"
|
||
)
|
||
|
||
resp = ollama_service.generate(question, system_prompt=sys_prompt, timeout=180)
|
||
if resp.success and resp.content:
|
||
if chat_id:
|
||
openclaw_session.append_turn(chat_id, question, resp.content)
|
||
if _LEARNING_ENABLED:
|
||
import threading as _thr
|
||
_thr.Thread(target=store_conversation,
|
||
args=(0, 0, question, resp.content, "ollama", []),
|
||
daemon=True).start()
|
||
return resp.content, None
|
||
else:
|
||
sys_log.warning(f"[Ollama] 生成失敗: {resp.error}，fallback 到 Gemini")
|
||
except Exception as e:
|
||
sys_log.warning(f"[Ollama] 例外發生: {e}，fallback 到 Gemini")
|
||
|
||
if not GEMINI_API_KEY and not NVIDIA_API_KEY:
|
||
return "（AI 引擎未設定，請確認 API Key 或啟動 Ollama 服務）", None
|
||
|
||
# ── Gemini Function Calling (備援 1) ─────────────────────────
|
||
if GEMINI_API_KEY:
|
||
try:
|
||
sys_msg = (
|
||
f"你是 OpenClaw（小O），服務「小龍蝦」電商業務團隊的 AI 助理。\n"
|
||
f"今天是 {today_str}。\n"
|
||
+ (f"\n【最近對話歷史】\n{history_ctx}\n" if history_ctx else "")
|
||
+ "你有四個工具可以使用：\n"
|
||
"1. query_sales — 查自家業績資料庫\n"
|
||
"2. get_market_intel — 取得外部市場情報（新聞/熱搜/PTT口碑/匯率/天氣/節慶）\n"
|
||
"3. get_knowledge — 查歷史分析知識庫\n"
|
||
"4. check_data_freshness — 確認業績資料最新可用日期，回答任何特定時段問題前必先呼叫\n\n"
|
||
"決策規則（ADR-019 Phase 2）：\n"
|
||
"- 用戶用『本月/本週/今天』等相對時間 → 先 check_data_freshness\n"
|
||
"- 若 data_freshness_warning 顯示當月無資料，禁止編造『業績為零』，"
|
||
"改主動問用戶是否要改看上月（並附 latest_data_date）\n"
|
||
"- 工具結果回來後用繁體中文自然回答，不要開場白，不要多餘客套話\n"
|
||
"- 同時呼叫多個工具時請平行送出\n"
|
||
"- 若【最近對話歷史】顯示用戶剛被詢問某參數（如月份），優先用該答案接續執行"
|
||
)
|
||
|
||
# 第一輪：讓 Gemini 判斷需要呼叫哪些工具
|
||
conversation = [
|
||
{"role": "user", "parts": [{"text": sys_msg + "\n\n用戶問：" + question}]}
|
||
]
|
||
payload = {
|
||
"contents": conversation,
|
||
"tools": _FC_TOOLS,
|
||
"tool_config": {"function_calling_config": {"mode": "AUTO"}},
|
||
"generationConfig": {"temperature": 0.3, "maxOutputTokens": 600},
|
||
}
|
||
r1 = requests.post(
|
||
f"{GEMINI_BASE_URL}/{GEMINI_MODEL}:generateContent?key={GEMINI_API_KEY}",
|
||
headers={"Content-Type": "application/json"},
|
||
json=payload, timeout=30,
|
||
)
|
||
r1.raise_for_status()
|
||
resp1 = r1.json()
|
||
candidate = resp1.get("candidates", [{}])[0]
|
||
parts = candidate.get("content", {}).get("parts", [])
|
||
|
||
# 如果沒有 function call，直接回傳文字
|
||
tool_calls = [p["functionCall"] for p in parts if "functionCall" in p]
|
||
if not tool_calls:
|
||
text = "".join(p.get("text", "") for p in parts if "text" in p).strip()
|
||
if text:
|
||
if chat_id:
|
||
openclaw_session.append_turn(chat_id, question, text)
|
||
if _LEARNING_ENABLED:
|
||
import threading as _thr
|
||
_thr.Thread(target=store_conversation,
|
||
args=(0, 0, question, text, "direct", []),
|
||
daemon=True).start()
|
||
return text, None
|
||
|
||
# 第二輪：執行所有工具，把結果送回 Gemini
|
||
tool_results = []
|
||
used_sources = []
|
||
for tc in tool_calls:
|
||
fn_name = tc["name"]
|
||
fn_args = tc.get("args", {})
|
||
sys_log.info(f"[FC] calling {fn_name}({fn_args})")
|
||
fn_result = _execute_tool(fn_name, fn_args)
|
||
used_sources.append(fn_name)
|
||
tool_results.append({
|
||
"functionResponse": {
|
||
"name": fn_name,
|
||
"response": fn_result
|
||
}
|
||
})
|
||
|
||
# 組建多輪對話
|
||
conversation.append({"role": "model", "parts": parts})
|
||
conversation.append({"role": "user", "parts": tool_results})
|
||
|
||
payload2 = {
|
||
"contents": conversation,
|
||
"tools": _FC_TOOLS,
|
||
"tool_config": {"function_calling_config": {"mode": "NONE"}}, # 第二輪強制回文字
|
||
"generationConfig": {
|
||
"temperature": 0.3,
|
||
"maxOutputTokens": 600,
|
||
},
|
||
}
|
||
r2 = requests.post(
|
||
f"{GEMINI_BASE_URL}/{GEMINI_MODEL}:generateContent?key={GEMINI_API_KEY}",
|
||
headers={"Content-Type": "application/json"},
|
||
json=payload2, timeout=35,
|
||
)
|
||
r2.raise_for_status()
|
||
resp2 = r2.json()
|
||
parts2 = resp2.get("candidates", [{}])[0].get("content", {}).get("parts", [])
|
||
final = "".join(p.get("text", "") for p in parts2 if "text" in p).strip()
|
||
|
||
if final:
|
||
sys_log.info(f"[FC] done tools={used_sources} reply={len(final)}chars")
|
||
if chat_id:
|
||
openclaw_session.append_turn(chat_id, question, final)
|
||
if _LEARNING_ENABLED:
|
||
import threading as _thr
|
||
_thr.Thread(target=store_conversation,
|
||
args=(0, 0, question, final, ",".join(used_sources), used_sources),
|
||
daemon=True).start()
|
||
return final, None
|
||
|
||
except Exception as e:
|
||
sys_log.warning(f"[FC] Gemini Function Calling failed: {e}, fallback NIM")
|
||
|
||
# ── NIM 備援（不支援 FC，用傳統 prompt）─────────────────
|
||
if NVIDIA_API_KEY:
|
||
try:
|
||
intent = resolve_query_intent(question)
|
||
today = today_str
|
||
yd = (now - timedelta(days=1)).strftime("%Y/%m/%d")
|
||
d = intent.get("date", yd) if intent["type"] == "day" else yd
|
||
|
||
db_ctx = ""
|
||
if intent["type"] == "day":
|
||
s = query_sales(d)
|
||
t = query_top_products(d, 8)
|
||
if s.get("found"):
|
||
db_ctx = (
|
||
f"日期{d} 業績NT${s.get('revenue',0):,.0f} "
|
||
f"訂單{s.get('orders',0)}筆 毛利{s.get('gross_margin',0):.1f}% "
|
||
f"TOP5: " + " / ".join(
|
||
f"[{p.get('id','')}]{p['name']}(NT${p['revenue']:,.0f})"
|
||
for p in t[:5])
|
||
)
|
||
elif intent["type"] == "month":
|
||
ms = query_monthly_summary(intent["year"], intent["month"])
|
||
if ms.get("found"):
|
||
db_ctx = (f"{intent['year']}年{intent['month']:02d}月 "
|
||
f"業績NT${ms.get('revenue',0):,.0f} "
|
||
f"訂單{ms.get('orders',0)}筆 "
|
||
f"毛利{ms.get('gross_margin',0):.1f}%")
|
||
|
||
mcp_ctx = build_mcp_context(question)
|
||
|
||
nim_prompt = (
|
||
f"你是 OpenClaw（小O），電商智能助理。今天{today}。\n"
|
||
+ (f"【業績資料】{db_ctx}\n" if db_ctx else "")
|
||
+ (f"【市場情報】{mcp_ctx[:400]}\n" if mcp_ctx else "")
|
||
+ f"\n用戶問：{question}\n"
|
||
"請用繁體中文直接回答，不要開場白，300字以內。"
|
||
)
|
||
r = requests.post(
|
||
f"{NVIDIA_BASE_URL}/chat/completions",
|
||
headers={"Authorization": f"Bearer {NVIDIA_API_KEY}",
|
||
"Content-Type": "application/json"},
|
||
json={
|
||
"model": CHAT_MODEL,
|
||
"messages": [{"role": "user", "content": nim_prompt}],
|
||
"max_tokens": 500, "temperature": 0.3,
|
||
},
|
||
timeout=20,
|
||
)
|
||
r.raise_for_status()
|
||
return r.json()["choices"][0]["message"]["content"].strip(), None
|
||
except Exception as e:
|
||
sys_log.error(f"[FC] NIM fallback error: {e}")
|
||
|
||
return "（AI 引擎全部異常：Ollama 超時，Gemini/NIM 備援亦無回應或達速率限制，請稍後再試）", None
|
||
|
||
|
||
|
||
# ── 指令處理 ──────────────────────────────────────────────────
|
||
_CMD_TO_NL = {
|
||
'sales': lambda a: f"請查 {a or '今日'} 的業績數字（包含營收、訂單數、毛利率）",
|
||
'top': lambda a: f"請列出 {a or '今日'} 的 TOP10 熱銷商品",
|
||
'vendor': lambda a: f"請列出 {a or '今日'} 的 TOP10 熱銷廠商",
|
||
}
|
||
|
||
|
||
def _agent_dispatch_cmd(cmd, arg, chat_id, reply_to) -> bool:
|
||
"""ADR-019 Phase 3: Feature-flagged. 將白名單 cmd 翻成 NL question 交 agent 處理。
|
||
|
||
Agent 自動 probe 資料新鮮度（透過 Phase 2 的 check_data_freshness tool），缺資料時
|
||
主動詢問用戶。回 True 表示已交 agent 處理，handle_cmd 不再走原 dispatch。
|
||
回 False 表示維持原行為（含 feature flag 關閉、cmd 不在白名單、agent 失敗等）。
|
||
"""
|
||
if not _OPENCLAW_AGENT_DISPATCH_ENABLED:
|
||
return False
|
||
if cmd not in _AGENT_DISPATCH_CMDS:
|
||
return False
|
||
if cmd not in _CMD_TO_NL:
|
||
return False # 翻譯規則尚未建立 → 安全降級
|
||
|
||
nl_question = _CMD_TO_NL[cmd](arg)
|
||
try:
|
||
sys_log.info(f"[AgentDispatch] cmd:{cmd}:{arg or ''} → NL: {nl_question}")
|
||
txt, kb = openclaw_answer(nl_question, chat_id=chat_id)
|
||
send_message(chat_id, txt, reply_to, keyboard=kb)
|
||
return True
|
||
except Exception as e:
|
||
sys_log.error(f"[AgentDispatch] cmd:{cmd} agent failed, fallback to direct handler: {e}")
|
||
return False
|
||
|
||
|
||
def handle_cmd(cmd, arg, chat_id, reply_to):
|
||
ld = latest_date() or datetime.now(TAIPEI_TZ).strftime('%Y/%m/%d')
|
||
target = normalize_date(arg) if arg else ld
|
||
|
||
# ADR-019 Phase 3: agent dispatch hook（feature flag 預設 OFF）
|
||
if (not _CMD_FROM_CALLBACK_CTX.get()) and _agent_dispatch_cmd(cmd, arg, chat_id, reply_to):
|
||
return
|
||
|
||
def _send_mcp_text_result(title: str, data, empty_message: str) -> bool:
|
||
"""相容新版 MCP 文字回傳；已處理則回 True，舊 dict 格式則回 False。"""
|
||
if isinstance(data, str):
|
||
text = data.strip()
|
||
if text:
|
||
send_message(chat_id, f"{title}\n\n{text[:3600]}", reply_to, parse_mode=None)
|
||
else:
|
||
send_message(chat_id, empty_message, reply_to, parse_mode=None)
|
||
return True
|
||
return False
|
||
|
||
if cmd in ('sales', '業績'):
|
||
s = query_sales(target)
|
||
t = query_top_products(target, 3)
|
||
send_message(chat_id, fmt_sales(s, t, f"{MOMO_BASE_URL}/reports/daily/{target}"),
|
||
reply_to, sales_quick_kb(target) if s.get('found') else None)
|
||
|
||
elif cmd in ('top', '熱銷'):
|
||
p = query_top_products(target, 10)
|
||
kb = [_row(('📊 查業績', f'cmd:sales:{target}'),
|
||
('📋 完整報表', f'cmd:report:{target}'))]
|
||
send_message(chat_id, fmt_products(p, target), reply_to, kb)
|
||
|
||
elif cmd in ('vendor', '廠商'):
|
||
v = query_top_vendors(target, 10)
|
||
kb = [_row(('🏆 熱銷商品', f'cmd:top:{target}'),
|
||
('📊 查業績', f'cmd:sales:{target}'))]
|
||
send_message(chat_id, fmt_vendors(v, target), reply_to, kb)
|
||
|
||
elif cmd in ('trend', '趨勢'):
|
||
import calendar as _cal
|
||
from datetime import date as _date
|
||
now_d = datetime.now(TAIPEI_TZ).date()
|
||
sub = arg.lower().strip() if arg else '7'
|
||
|
||
# 決定查詢區間
|
||
if sub in ('', '7', 'week', 'weekly', '週', '近7日', '七天'):
|
||
ld_str = latest_date() or now_d.strftime('%Y/%m/%d')
|
||
end_d = datetime.strptime(ld_str.replace('/', '-'), '%Y-%m-%d').date()
|
||
start_d = end_d - timedelta(days=6)
|
||
period_label = '近7日'
|
||
elif sub in ('month', '30', '月', '本月', '近30日', '近一月'):
|
||
end_d = now_d
|
||
start_d = end_d - timedelta(days=29)
|
||
period_label = '近30日'
|
||
elif sub in ('quarter', 'q', '季', '近季', '近3月', '近三月'):
|
||
end_d = now_d
|
||
start_d = end_d - timedelta(days=89)
|
||
period_label = '近3個月'
|
||
elif sub in ('half', '半年', '近半年', '6月', '六個月'):
|
||
end_d = now_d
|
||
start_d = end_d - timedelta(days=179)
|
||
period_label = '近半年'
|
||
elif sub in ('year', 'yearly', '年', '本年', '近年', '近一年'):
|
||
end_d = now_d
|
||
start_d = end_d - timedelta(days=364)
|
||
period_label = '近一年'
|
||
elif re.fullmatch(r'\d{4}/\d{1,2}', sub):
|
||
yr_s, mo_s = sub.split('/')
|
||
yr, mo = int(yr_s), int(mo_s)
|
||
start_d = _date(yr, mo, 1)
|
||
end_d = _date(yr, mo, _cal.monthrange(yr, mo)[1])
|
||
period_label = f'{yr}年{mo:02d}月'
|
||
elif re.fullmatch(r'\d{4}', sub):
|
||
yr = int(sub)
|
||
start_d = _date(yr, 1, 1)
|
||
end_d = _date(yr, 12, 31)
|
||
period_label = f'{yr}年度'
|
||
elif re.fullmatch(r'\d{4}/[qQ]([1-4])', sub):
|
||
m2 = re.fullmatch(r'(\d{4})/[qQ]([1-4])', sub)
|
||
yr, qn = int(m2.group(1)), int(m2.group(2))
|
||
q_months = [(1, 3), (4, 6), (7, 9), (10, 12)]
|
||
sm, em = q_months[qn - 1]
|
||
start_d = _date(yr, sm, 1)
|
||
end_d = _date(yr, em, _cal.monthrange(yr, em)[1])
|
||
period_label = f'{yr}年Q{qn}'
|
||
else:
|
||
ld_str = latest_date() or now_d.strftime('%Y/%m/%d')
|
||
end_d = datetime.strptime(ld_str.replace('/', '-'), '%Y-%m-%d').date()
|
||
start_d = end_d - timedelta(days=6)
|
||
period_label = '近7日'
|
||
|
||
start_str = start_d.strftime('%Y/%m/%d')
|
||
end_str = end_d.strftime('%Y/%m/%d')
|
||
days_count = (end_d - start_d).days + 1
|
||
data = query_trend_range(start_str, end_str)
|
||
if not data:
|
||
data = query_weekly_trend()
|
||
|
||
chart_title = f'業績趨勢走勢圖 — {period_label}（{start_str}~{end_str}）'
|
||
|
||
if days_count <= 35:
|
||
# 週/月：逐日文字 + 折線圖
|
||
send_message(chat_id, fmt_trend(data, period_label), reply_to, _submenu_trend())
|
||
try:
|
||
png = gen_trend_chart(data_points=data, title=chart_title)
|
||
if png:
|
||
send_photo(chat_id, png, caption=f'📈 {period_label} 趨勢走勢圖')
|
||
try: os.unlink(png)
|
||
except Exception: pass
|
||
except Exception as _te:
|
||
sys_log.warning(f'[OpenClawBot] trend chart error: {_te}')
|
||
else:
|
||
# 季/半年/年：摘要 + 聚合柱狀圖（不洗版面）
|
||
granularity = 'monthly' if days_count > 100 else 'weekly'
|
||
send_message(chat_id, fmt_trend_summary(data, period_label), reply_to, _submenu_trend())
|
||
try:
|
||
png = gen_aggregated_chart(data, chart_title, granularity=granularity)
|
||
if png:
|
||
unit = '月' if granularity == 'monthly' else '週'
|
||
send_photo(chat_id, png, caption=f'📊 {period_label} 業績走勢（按{unit}）')
|
||
try: os.unlink(png)
|
||
except Exception: pass
|
||
except Exception as _te:
|
||
sys_log.warning(f'[OpenClawBot] trend agg chart error: {_te}')
|
||
|
||
elif cmd in ('report', '報表'):
|
||
send_message(chat_id, f"⏳ 正在產生 {target} 完整報表...", reply_to, parse_mode=None)
|
||
try:
|
||
import os as _os
|
||
pdf_path = generate_daily_pdf(target)
|
||
if pdf_path:
|
||
if pdf_path.endswith('.xlsx'):
|
||
ext = 'Excel'
|
||
elif pdf_path.endswith('.csv'):
|
||
ext = 'CSV'
|
||
else:
|
||
ext = 'PDF'
|
||
send_document(
|
||
chat_id, pdf_path,
|
||
caption=f"📊 {target} 業績報表（{ext}）",
|
||
reply_to=reply_to
|
||
)
|
||
_os.unlink(pdf_path)
|
||
else:
|
||
send_message(chat_id,
|
||
f"⚠️ 報表產生失敗，請稍後再試",
|
||
reply_to)
|
||
except Exception as e:
|
||
sys_log.error(f"[OpenClawBot] report cmd error: {e}")
|
||
send_message(chat_id,
|
||
f"⚠️ 報表產生發生錯誤，請稍後再試",
|
||
reply_to)
|
||
|
||
elif cmd == 'news':
|
||
try:
|
||
from services.mcp_context_service import get_ecommerce_news
|
||
data = get_ecommerce_news()
|
||
if _send_mcp_text_result("📰 即時電商新聞", data, "⚠️ 新聞資料暫時無法取得"):
|
||
return
|
||
news = data.get('news', [])
|
||
sys_log.info(f"[OpenClawBot] news: {len(news)} items")
|
||
if news:
|
||
lines = ["📰 即時電商新聞"]
|
||
for n in news[:6]:
|
||
title = n['title'][:60]
|
||
url = n.get('url', '')
|
||
src = n.get('source', '')
|
||
if url:
|
||
lines.append(f"• <a href='{url}'>{title}</a>（{src}）")
|
||
else:
|
||
lines.append(f"• {title}（{src}）")
|
||
send_message(chat_id, "\n".join(lines), reply_to, parse_mode='HTML')
|
||
else:
|
||
send_message(chat_id, "⚠️ 新聞資料暫時無法取得", reply_to, parse_mode=None)
|
||
except Exception as e:
|
||
sys_log.error(f"[OpenClawBot] news error: {e}", exc_info=True)
|
||
send_message(chat_id, "⚠️ 新聞功能暫時異常，請稍後再試", reply_to, parse_mode=None)
|
||
|
||
elif cmd == 'weather':
|
||
try:
|
||
from services.mcp_context_service import get_taiwan_weather
|
||
data = get_taiwan_weather()
|
||
if _send_mcp_text_result("🌤 台灣天氣預報", data, "⚠️ 天氣資料暫時無法取得"):
|
||
return
|
||
weather = data.get('weather', {})
|
||
sys_log.info(f"[OpenClawBot] weather: {list(weather.keys())}")
|
||
if weather:
|
||
lines = ["🌤 台灣天氣預報"]
|
||
for city, w in list(weather.items())[:6]:
|
||
wx = w.get('Wx', w.get('weatherDesc', ''))
|
||
pop = w.get('PoP', '')
|
||
tmin = w.get('MinT', '')
|
||
tmax = w.get('MaxT', '')
|
||
tmp = w.get('temp', f"{tmin}~{tmax}°C" if tmin else '')
|
||
lines.append(f"• {city}：{wx} {tmp}" +
|
||
(f" 降雨{pop}%" if pop else ""))
|
||
send_message(chat_id, "\n".join(lines), reply_to, parse_mode=None)
|
||
else:
|
||
send_message(chat_id, "⚠️ 天氣資料暫時無法取得", reply_to, parse_mode=None)
|
||
except Exception as e:
|
||
sys_log.error(f"[OpenClawBot] weather error: {e}", exc_info=True)
|
||
send_message(chat_id, "⚠️ 天氣功能暫時異常，請稍後再試", reply_to, parse_mode=None)
|
||
|
||
elif cmd == 'trends':
|
||
try:
|
||
from services.mcp_context_service import get_taiwan_trends
|
||
data = get_taiwan_trends()
|
||
if _send_mcp_text_result("🔥 台灣 Google 熱搜（即時）", data, "⚠️ 熱搜資料暫時無法取得"):
|
||
return
|
||
trends = data.get('trends', [])
|
||
if trends:
|
||
lines = ["🔥 *台灣 Google 熱搜（即時）*\n"]
|
||
for i, t in enumerate(trends[:12], 1):
|
||
lines.append(f"{i}. {t['keyword']}")
|
||
send_message(chat_id, "\n".join(lines), reply_to, parse_mode='Markdown')
|
||
else:
|
||
send_message(chat_id, "⚠️ 熱搜資料暫時無法取得", reply_to)
|
||
except Exception as e:
|
||
send_message(chat_id, f"⚠️ 取得失敗: {e}", reply_to)
|
||
|
||
elif cmd == 'dcard':
|
||
try:
|
||
from services.mcp_context_service import get_dcard_trends
|
||
data = get_dcard_trends()
|
||
if _send_mcp_text_result("💬 Dcard 消費者討論熱點", data, "⚠️ Dcard 資料暫時無法取得"):
|
||
return
|
||
posts = data.get('posts', [])
|
||
if posts:
|
||
lines = ["💬 *Dcard 消費者討論熱點*\n"]
|
||
for p in posts[:8]:
|
||
lines.append(f"• [{p['board']}] {p['title'][:50]}")
|
||
send_message(chat_id, "\n".join(lines), reply_to, parse_mode='Markdown')
|
||
else:
|
||
send_message(chat_id, "⚠️ Dcard 資料暫時無法取得", reply_to)
|
||
except Exception as e:
|
||
send_message(chat_id, f"⚠️ 取得失敗: {e}", reply_to)
|
||
|
||
elif cmd == 'exchange':
|
||
try:
|
||
from services.mcp_context_service import get_twbank_exchange_rates
|
||
data = get_twbank_exchange_rates()
|
||
if _send_mcp_text_result("💱 台灣銀行即時匯率", data, "⚠️ 匯率資料暫時無法取得"):
|
||
return
|
||
rates = data.get('rates', {})
|
||
if rates:
|
||
lines = ["💱 *台灣銀行即時匯率*\n"]
|
||
for code, info in rates.items():
|
||
name = info.get('name', code)
|
||
if 'buy' in info:
|
||
lines.append(f"• {name}（{code}） 買入 `{info['buy']}` / 賣出 `{info['sell']}`")
|
||
elif 'mid' in info:
|
||
lines.append(f"• {name}（{code}） `{info['mid']}`")
|
||
lines.append(f"\n_資料來源：台灣銀行 {data.get('fetched_at','')[:16]}_")
|
||
send_message(chat_id, "\n".join(lines), reply_to, parse_mode='Markdown')
|
||
else:
|
||
send_message(chat_id, "⚠️ 匯率資料暫時無法取得", reply_to)
|
||
except Exception as e:
|
||
send_message(chat_id, f"⚠️ 取得失敗: {e}", reply_to)
|
||
|
||
elif cmd == 'calendar':
|
||
try:
|
||
from services.mcp_context_service import get_upcoming_events
|
||
data = get_upcoming_events()
|
||
if _send_mcp_text_result("📅 近期電商節慶行事曆", data, "✅ 近 60 天無重大電商節慶"):
|
||
return
|
||
events = data.get('events', [])
|
||
if events:
|
||
lines = ["📅 *近期電商節慶行事曆*\n"]
|
||
for ev in events:
|
||
days = ev['days_to_event']
|
||
day_str = f"還有 {days} 天" if days > 0 else ("今天" if days == 0 else f"進行中（+{-days}天）")
|
||
lines.append(f"{ev['status']} *{ev['name']}*（{ev['date']}，{day_str}）")
|
||
lines.append(f" 建議提前 {ev['warmup_days']} 天開始備戰")
|
||
send_message(chat_id, "\n".join(lines), reply_to, parse_mode='Markdown')
|
||
else:
|
||
send_message(chat_id, "✅ 近 60 天無重大電商節慶", reply_to)
|
||
except Exception as e:
|
||
send_message(chat_id, f"⚠️ 取得失敗: {e}", reply_to)
|
||
|
||
elif cmd == 'youtube':
|
||
try:
|
||
from services.mcp_context_service import get_youtube_trending
|
||
data = get_youtube_trending()
|
||
if _send_mcp_text_result("▶️ YouTube 熱門開箱/推薦影片", data, "⚠️ YouTube 資料暫時無法取得"):
|
||
return
|
||
videos = data.get('videos', [])
|
||
if videos:
|
||
lines = ["▶️ *YouTube 熱門開箱/推薦影片*\n"]
|
||
for v in videos[:6]:
|
||
title = v['title'][:55]
|
||
url = v.get('url', '')
|
||
if url:
|
||
lines.append(f"• <a href='{url}'>{title}</a>")
|
||
else:
|
||
lines.append(f"• {title}")
|
||
send_message(chat_id, "\n".join(lines), reply_to, parse_mode='HTML')
|
||
else:
|
||
send_message(chat_id, "⚠️ YouTube 資料暫時無法取得", reply_to)
|
||
except Exception as e:
|
||
send_message(chat_id, f"⚠️ 取得失敗: {e}", reply_to)
|
||
|
||
# ── PChome 比價指令 ───────────────────────────────────────────
|
||
|
||
elif cmd in ('competitor', '比價', 'price'):
|
||
# /competitor → 昨日 TOP30 競品比對日報
|
||
# /competitor 商品名 → 單品即時比價
|
||
if not _PCHOME_AVAILABLE:
|
||
send_message(chat_id, '⚠️ PChome 比價服務未啟用', reply_to)
|
||
return
|
||
|
||
keyword = arg.strip() if arg else ''
|
||
is_date = bool(keyword and re.match(r'\d{4}[/-]\d{1,2}[/-]\d{1,2}', keyword))
|
||
if keyword and not is_date:
|
||
# 即時比價（商品關鍵字）
|
||
send_message(chat_id, f'🔍 正在比價「{keyword}」，請稍候...', reply_to, parse_mode=None)
|
||
|
||
def _compare_bg(_kw, _chat_id, _reply_to):
|
||
try:
|
||
from services.pchome_crawler import search_pchome as _sp, find_best_match as _fm
|
||
# 搜尋 momo products
|
||
with _db().connect() as c:
|
||
rows = c.execute(text("""
|
||
SELECT p.name, p.i_code,
|
||
COALESCE(pr.price, 0)
|
||
FROM products p
|
||
LEFT JOIN (
|
||
SELECT product_id, price,
|
||
ROW_NUMBER() OVER (PARTITION BY product_id ORDER BY timestamp DESC) as rn
|
||
FROM price_records
|
||
) pr ON p.id=pr.product_id AND pr.rn=1
|
||
WHERE p.name ILIKE :kw
|
||
ORDER BY pr.price DESC NULLS LAST
|
||
LIMIT 5
|
||
"""), {'kw': f'%{_kw}%'}).fetchall()
|
||
|
||
results = []
|
||
for row in rows:
|
||
name, icode, price = row[0], row[1], float(row[2] or 0)
|
||
cmp = pchome_compare(name, price, icode)
|
||
results.append(cmp)
|
||
import time; time.sleep(0.6)
|
||
|
||
if not results:
|
||
# 沒有 momo 商品 → 直接搜尋 PChome
|
||
pc_results = _sp(_kw, limit=5)
|
||
if pc_results:
|
||
lines = [f'🔍 *PChome 商品搜尋：{_kw}*', '']
|
||
for i, p in enumerate(pc_results[:5], 1):
|
||
stk = '✅' if p['in_stock'] else '❌'
|
||
lines.append(f'{i}. {p["name"][:30]}')
|
||
lines.append(f' {stk} `NT$ {p["price"]:,.0f}` [查看]({p["url"]})')
|
||
send_message(_chat_id, '\n'.join(lines), _reply_to)
|
||
else:
|
||
send_message(_chat_id, f'⚠️ 在 PChome 找不到「{_kw}」相關商品', _reply_to)
|
||
return
|
||
|
||
msg = pchome_fmt_compare(results, _kw)
|
||
kb = [_row(('🔍 重新搜尋', 'await:search_compare'),
|
||
('📊 競品日報', 'menu:competitor'))]
|
||
send_message(_chat_id, msg, _reply_to, kb)
|
||
except Exception as _e:
|
||
sys_log.error(f'[PChome] compare_bg: {_e}', exc_info=True)
|
||
send_message(_chat_id, f'⚠️ 比價失敗：{str(_e)[:80]}', _reply_to)
|
||
|
||
threading.Thread(target=_compare_bg, args=(keyword, chat_id, reply_to), daemon=True).start()
|
||
|
||
else:
|
||
# 無關鍵字或日期參數 → 昨日（或指定日期）熱銷競品日報（背景執行）
|
||
if is_date:
|
||
yesterday = normalize_date(keyword)
|
||
date_label = yesterday
|
||
else:
|
||
yesterday = (datetime.now(TAIPEI_TZ).date() - timedelta(days=1)).strftime('%Y/%m/%d')
|
||
date_label = f'昨日 ({yesterday[-5:]})'
|
||
send_message(chat_id,
|
||
f'📊 正在分析 {date_label} TOP30 熱銷商品 vs PChome 比價，預計 30~60 秒...',
|
||
reply_to, parse_mode=None)
|
||
|
||
def _daily_report_bg(_date_str, _chat_id, _reply_to):
|
||
try:
|
||
results = pchome_batch(_db(), top_n=30, date_str=_date_str)
|
||
pchome_save(_db(), results)
|
||
msg = pchome_fmt_report(results, _date_str)
|
||
kb = [_row(('🔍 搜尋比價', 'await:search_compare'),
|
||
('📄 比價簡報', 'menu:competitor_ppt'))]
|
||
send_message(_chat_id, msg, _reply_to, kb)
|
||
except Exception as _e:
|
||
sys_log.error(f'[PChome] daily_report_bg: {_e}', exc_info=True)
|
||
send_message(_chat_id, f'⚠️ 競品分析失敗：{str(_e)[:80]}', _reply_to)
|
||
|
||
threading.Thread(target=_daily_report_bg, args=(yesterday, chat_id, reply_to), daemon=True).start()
|
||
|
||
# ── v5 新增指令 ──────────────────────────────────────────────
|
||
|
||
elif cmd in ('compare', '同比'):
|
||
data = query_comparison(target)
|
||
send_message(
|
||
chat_id,
|
||
fmt_comparison(data, target),
|
||
reply_to,
|
||
[_row(('📊 今日業績', f'cmd:sales:{target}'), ('🧬 策略矩陣', f'cmd:strategy:{target}'))],
|
||
)
|
||
|
||
elif cmd in ('category', '分類'):
|
||
cats = query_category_sales(target)
|
||
kb = [_row(('🗂 鑽取分類', 'menu:category'), ('🏆 熱銷商品', f'cmd:top:{target}'))]
|
||
send_message(chat_id, fmt_category(cats, target), reply_to, kb)
|
||
|
||
elif cmd in ('catdetail', '分類細項'):
|
||
# arg = "母嬰:2026/04/15" or "母嬰"
|
||
parts_cd = arg.split(':') if arg else []
|
||
cat_name = parts_cd[0].strip() if parts_cd else ''
|
||
date_cd = parts_cd[1].strip() if len(parts_cd) > 1 else target
|
||
if not cat_name:
|
||
send_message(chat_id, "⚠️ 請指定分類名稱", reply_to)
|
||
else:
|
||
items = query_category_detail(cat_name, date_cd)
|
||
d_label = date_cd[-5:] if date_cd else '近7日'
|
||
msg = fmt_category_detail(cat_name, items, d_label)
|
||
kb = [_row(('⬅ 分類總覽', 'menu:category'), ('🏆 熱銷商品', f'cmd:top:{target}'))]
|
||
send_message(chat_id, msg, reply_to, kb)
|
||
|
||
elif cmd in ('restock', '補貨'):
|
||
send_message(chat_id, "⏳ 正在計算補貨預測...", reply_to, parse_mode=None)
|
||
items = query_restock_forecast(20)
|
||
msg = fmt_restock_forecast(items)
|
||
kb = [
|
||
_row(('🏆 熱銷商品', f'cmd:top:{target}'), ('🧬 商品健康', f'cmd:health:{target}')),
|
||
_row(('🔄 重新計算', 'cmd:restock')),
|
||
]
|
||
send_message(chat_id, msg, reply_to, kb)
|
||
|
||
elif cmd in ('promo', '促銷'):
|
||
# arg = "2026/04/01-2026/04/07"
|
||
if arg and '-' in arg and re.search(r'\d{4}[/-]\d{1,2}[/-]\d{1,2}', arg):
|
||
parts_p = arg.split('-')
|
||
# 可能是 "2026/04/01-2026/04/07" → split on '-' 中間需要處理日期格式
|
||
# 先嘗試用空格分割，再用 '-' 分割最後部分
|
||
m = re.findall(r'\d{4}[/\-]\d{1,2}[/\-]\d{1,2}', arg)
|
||
if len(m) >= 2:
|
||
start_s = normalize_date(m[0])
|
||
end_s = normalize_date(m[1])
|
||
send_message(chat_id, f"⏳ 正在比較 {start_s} ~ {end_s} 促銷效益...", reply_to, parse_mode=None)
|
||
data = query_promo_comparison(start_s, end_s)
|
||
msg = fmt_promo_comparison(data)
|
||
promo_range_arg = f'{start_s}-{end_s}'
|
||
kb = [
|
||
_row(('🎉 再查一個促銷', 'await:promo_range'),
|
||
('📊 業績查詢', f'cmd:sales:{start_s}')),
|
||
_row(('📊 產出促銷簡報', f'cmd:ppt:promo {promo_range_arg}')),
|
||
]
|
||
send_message(chat_id, msg, reply_to, kb)
|
||
else:
|
||
send_message(chat_id,
|
||
"⚠️ 格式錯誤\n請輸入：`2026/04/01-2026/04/07`", reply_to)
|
||
else:
|
||
send_message(chat_id,
|
||
"🎉 *促銷效益追蹤*\n\n請輸入活動日期範圍：\n`/promo 2026/04/01-2026/04/07`\n或點選 🎉 促銷追蹤 按鈕",
|
||
reply_to, [_row(('🎉 設定促銷範圍', 'await:promo_range'))])
|
||
|
||
elif cmd in ('goal', '目標'):
|
||
# /goal 200000 設定日目標
|
||
# /goal monthly 5000000 設定月目標
|
||
if arg:
|
||
parts2 = arg.split()
|
||
if parts2[0].lower() == 'monthly' and len(parts2) > 1:
|
||
try:
|
||
_GOALS['monthly'] = float(parts2[1].replace(',', ''))
|
||
send_message(chat_id,
|
||
f"✅ 月目標設定為 `NT$ {_GOALS['monthly']:,.0f}`",
|
||
reply_to, parse_mode='Markdown')
|
||
except ValueError:
|
||
send_message(chat_id, "⚠️ 格式：`/goal monthly 5000000`",
|
||
reply_to, parse_mode='Markdown')
|
||
else:
|
||
try:
|
||
_GOALS['daily'] = float(parts2[0].replace(',', ''))
|
||
send_message(chat_id,
|
||
f"✅ 日目標設定為 `NT$ {_GOALS['daily']:,.0f}`",
|
||
reply_to, parse_mode='Markdown')
|
||
except ValueError:
|
||
send_message(chat_id, "⚠️ 格式：`/goal 200000`",
|
||
reply_to, parse_mode='Markdown')
|
||
else:
|
||
status = get_goal_status(target)
|
||
kb = [_row(('📊 今日業績', f'cmd:sales:{target}'), ('🔄 同期比較', f'cmd:compare:{target}'))]
|
||
send_message(chat_id, fmt_goal_status(status), reply_to, kb)
|
||
|
||
elif cmd in ('chart', '圖表'):
|
||
send_message(chat_id, "⏳ 正在產生趨勢圖...", reply_to, parse_mode=None)
|
||
try:
|
||
png = gen_trend_chart(14)
|
||
if png:
|
||
send_photo(chat_id, png,
|
||
caption=f"📉 近14日業績趨勢",
|
||
reply_to=reply_to)
|
||
os.unlink(png)
|
||
# 再發熱銷商品圖
|
||
png2 = gen_products_chart(target, 10)
|
||
if png2:
|
||
send_photo(chat_id, png2,
|
||
caption=f"🏆 {target} 熱銷商品 TOP10",
|
||
reply_to=None)
|
||
os.unlink(png2)
|
||
else:
|
||
send_message(chat_id,
|
||
"⚠️ 圖表功能需安裝 matplotlib（pip install matplotlib）",
|
||
reply_to, parse_mode=None)
|
||
except Exception as e:
|
||
sys_log.error(f"[OpenClawBot] chart cmd error: {e}")
|
||
send_message(chat_id, "⚠️ 圖表產生失敗", reply_to, parse_mode=None)
|
||
|
||
elif cmd in ('health', '健康'):
|
||
anomalies = query_anomalies(target)
|
||
strat = analyze_product_strategy(target, 10)
|
||
|
||
lines = [f"🏥 *商品健康報告* _({target})_", ""]
|
||
|
||
# ── 異常偵測 ──
|
||
if anomalies:
|
||
lines.append(f"⚠️ *業績異常商品* _（偏差 > 30%）_")
|
||
for a in anomalies[:6]:
|
||
pct = a.get('pct') or 0
|
||
icon = '📈' if pct > 0 else '📉'
|
||
sid = _short_id(a['id'])
|
||
name = _esc(a['name'][:18])
|
||
today_r = a.get('today', 0)
|
||
avg_r = a.get('avg7', 0)
|
||
lines.append(
|
||
f"{icon} *{abs(pct):.0f}%* {'急升' if pct > 0 else '急降'} · {name}"
|
||
)
|
||
lines.append(
|
||
f" 今 `${today_r:,.0f}` ／ 7日均 `${avg_r:,.0f}`"
|
||
f" `{sid}`"
|
||
)
|
||
lines.append("")
|
||
else:
|
||
lines.append("✅ *無業績異常商品* _（7日均值偏差均在 30% 以內）_")
|
||
lines.append("")
|
||
|
||
# ── 策略分佈 ──
|
||
if strat:
|
||
from collections import Counter
|
||
cnt = Counter(s['strategy'] for s in strat)
|
||
total = len(strat)
|
||
tag_icon = {'加碼': '🔥', '機會': '💡', '收割': '⚡', '觀察': '⚠️', '持穩': '✅'}
|
||
lines.append(f"📊 *策略分佈* _（共 {total} 件）_")
|
||
for k, v in cnt.most_common():
|
||
bar = '▓' * v + '░' * (total - v)
|
||
icon = tag_icon.get(k, '•')
|
||
lines.append(f" {icon} {k} {v} 件 `{bar}`")
|
||
|
||
kb = [
|
||
_row(('🎲 策略矩陣', f'cmd:strategy:{target}'), ('🏆 熱銷商品', f'cmd:top:{target}')),
|
||
_row(('📊 今日業績', f'cmd:sales:{target}'), ('🔄 同期比較', f'cmd:compare:{target}')),
|
||
]
|
||
send_message(chat_id, "\n".join(lines), reply_to, kb)
|
||
|
||
elif cmd in ('strategy', '策略'):
|
||
strat = analyze_product_strategy(target, 10)
|
||
kb = [
|
||
_row(('🏥 商品健康', f'cmd:health:{target}'), ('🔄 同期比較', f'cmd:compare:{target}')),
|
||
_row(('🏆 熱銷商品', f'cmd:top:{target}'), ('📊 今日業績', f'cmd:sales:{target}')),
|
||
]
|
||
send_message(chat_id, fmt_strategy(strat, target), reply_to, kb)
|
||
|
||
elif cmd in ('cache', '快取'):
|
||
# /cache status 任何已授權用戶可看
|
||
# /cache flush <type> admin only — 清除指定/全部 PPT 快取
|
||
# /cache cleanup [days] [confirm] admin only（confirm 才實刪）— 清磁碟
|
||
sub = (arg or '').strip().lower()
|
||
# critic Medium-3：破壞性操作前先取當前 user_id 並驗 admin
|
||
_curr_uid = _CURRENT_USER_ID_CTX.get()
|
||
|
||
if sub.startswith('flush'):
|
||
# admin guard
|
||
if not _is_admin(_curr_uid):
|
||
send_message(chat_id,
|
||
"⛔ `/cache flush` 限管理員執行。\n"
|
||
"請聯繫系統管理員，或設定 `OPENCLAW_ADMIN_USER_IDS` 環境變數。",
|
||
reply_to, parse_mode='Markdown')
|
||
return
|
||
parts = sub.split(maxsplit=1)
|
||
target_type = parts[1].strip() if len(parts) > 1 else None
|
||
try:
|
||
affected = _invalidate_ppt_cache(target_type if target_type and target_type != 'all' else None)
|
||
from services.ppt_generator import TEMPLATE_VERSIONS
|
||
ver = TEMPLATE_VERSIONS.get(target_type, '—') if target_type else '—'
|
||
msg = (f"✅ PPT 快取已強制失效\n"
|
||
f"類型：{target_type or '全部'}\n"
|
||
f"當前模板版本：{ver}\n"
|
||
f"影響筆數：{affected}\n"
|
||
f"下次請求將以新模板重新生成。\n"
|
||
f"執行者：user_id={_curr_uid}")
|
||
sys_log.warning(f"[PPT cache flush] admin={_curr_uid} type={target_type or 'ALL'} affected={affected}")
|
||
except Exception as e:
|
||
msg = f"❌ 快取清除失敗：{e}"
|
||
send_message(chat_id, msg, reply_to, parse_mode=None)
|
||
elif sub.startswith('status') or sub == '':
|
||
try:
|
||
from database.manager import DatabaseManager
|
||
from database.ppt_reports import PPTReport
|
||
from sqlalchemy import func as _f
|
||
session = DatabaseManager().get_session()
|
||
now_naive = datetime.now(TAIPEI_TZ).replace(tzinfo=None)
|
||
rows = (session.query(PPTReport.report_type,
|
||
_f.count(PPTReport.id),
|
||
_f.sum(PPTReport.file_size))
|
||
.filter(or_(PPTReport.expires_at.is_(None),
|
||
PPTReport.expires_at > now_naive))
|
||
.group_by(PPTReport.report_type).all())
|
||
session.close()
|
||
from services.ppt_generator import TEMPLATE_VERSIONS
|
||
lines = ["📦 *PPT 快取狀態（未過期）*", ""]
|
||
if not rows:
|
||
lines.append("（目前無有效快取）")
|
||
else:
|
||
for rt, cnt, size in rows:
|
||
size_kb = (size or 0) / 1024
|
||
ver = TEMPLATE_VERSIONS.get(rt, '—')
|
||
lines.append(f"• `{rt}`：{cnt} 筆 · {size_kb:,.0f} KB · 模板 `{ver}`")
|
||
lines.append("")
|
||
lines.append("使用 `/cache flush <type>` 強制清除")
|
||
send_message(chat_id, '\n'.join(lines), reply_to, parse_mode='Markdown')
|
||
except Exception as e:
|
||
send_message(chat_id, f"❌ 查詢快取失敗：{e}", reply_to, parse_mode=None)
|
||
elif sub.startswith('cleanup'):
|
||
# /cache cleanup [days] 乾跑（任何已授權用戶可預覽）
|
||
# /cache cleanup [days] confirm admin only — 真正執行刪除
|
||
# 注意：days < 1 強制乾跑（critic Medium-3 防呆）
|
||
parts = sub.split()
|
||
confirm = 'confirm' in parts or 'real' in parts
|
||
days = 7
|
||
for p in parts[1:]:
|
||
if p.isdigit():
|
||
days = int(p)
|
||
# 防呆：days < 1 強制乾跑
|
||
forced_dry = days < 1
|
||
# admin guard：只有實刪需要 admin（乾跑任何人可看）
|
||
if confirm and not forced_dry and not _is_admin(_curr_uid):
|
||
send_message(chat_id,
|
||
"⛔ `/cache cleanup ... confirm` 限管理員執行。\n"
|
||
"可改用乾跑模式（不加 `confirm`）預覽影響範圍。",
|
||
reply_to, parse_mode='Markdown')
|
||
return
|
||
dry = forced_dry or (not confirm)
|
||
try:
|
||
stat = cleanup_expired_ppt_cache(days_old=days, dry_run=dry)
|
||
if not dry:
|
||
sys_log.warning(f"[PPT cleanup] admin={_curr_uid} days={days} stat={stat}")
|
||
tag = "（乾跑—未實刪）" if dry else "（已實刪）"
|
||
if forced_dry:
|
||
tag += " [days<1 強制乾跑]"
|
||
msg = (f"🧹 PPT 磁碟清理 {tag}\n"
|
||
f"門檻：過期超過 {days} 天\n"
|
||
f"{'將刪檔' if dry else '刪檔'}：{stat['deleted_files']} 個\n"
|
||
f"{'將刪 row' if dry else '刪 row'}：{stat['deleted_rows']} 筆\n"
|
||
f"{'將釋放' if dry else '釋放'}空間：{stat['freed_bytes']/1024:,.0f} KB"
|
||
+ (f"\n錯誤：{len(stat['errors'])} 筆" if stat['errors'] else "")
|
||
+ ("\n\n如要真正刪除，加上 `confirm`：\n"
|
||
f"`/cache cleanup {days} confirm`" if dry and not forced_dry else ""))
|
||
except Exception as e:
|
||
msg = f"❌ 清理失敗：{e}"
|
||
send_message(chat_id, msg, reply_to, parse_mode='Markdown' if dry and not forced_dry else None)
|
||
else:
|
||
send_message(chat_id,
|
||
"用法：\n"
|
||
"• `/cache status` 查看快取狀態\n"
|
||
"• `/cache flush monthly` 清月報快取\n"
|
||
"• `/cache flush all` 清全部\n"
|
||
"• `/cache cleanup [N天] [dry]` 清磁碟過期檔案",
|
||
reply_to, parse_mode='Markdown')
|
||
|
||
elif cmd in ('ppt', 'slides', '簡報'):
|
||
# /ppt daily [日期]
|
||
# /ppt weekly
|
||
# /ppt monthly [YYYY/MM]
|
||
# /ppt strategy [日期]
|
||
sub_type = arg.lower().strip() if arg else 'daily'
|
||
sub_arg = ''
|
||
if ' ' in sub_type:
|
||
parts = sub_type.split(' ', 1)
|
||
sub_type = parts[0]
|
||
sub_arg = parts[1].strip()
|
||
|
||
send_message(chat_id, f"⏳ 正在生成 *{sub_type}* 簡報，請稍候（30~60秒）...",
|
||
reply_to, parse_mode='Markdown')
|
||
|
||
def _ppt_background(_sub_type, _sub_arg, _chat_id, _target, _reply_to):
|
||
try:
|
||
ppt_path = _generate_ppt_cmd(_sub_type, _sub_arg, _chat_id, _target,
|
||
_reply_to=_reply_to)
|
||
if ppt_path and os.path.exists(ppt_path):
|
||
type_labels = {
|
||
'daily': '📊 日報', 'weekly': '📈 週報',
|
||
'monthly': '📅 月報', 'strategy': '🧬 策略簡報',
|
||
'competitor': '🔍 競品比較', 'compare': '🔍 競品比較',
|
||
'競品': '🔍 競品比較', 'promo': '🎉 促銷效益報告',
|
||
}
|
||
label = type_labels.get(_sub_type, '簡報')
|
||
caption = f"{label} — 由 OpenClaw AI 自動生成\n💡 可用 PowerPoint / Keynote / Google Slides 開啟"
|
||
send_document(_chat_id, ppt_path, caption=caption, reply_to=_reply_to)
|
||
if not _is_cached_ppt_file(ppt_path):
|
||
try:
|
||
os.unlink(ppt_path)
|
||
except Exception:
|
||
pass
|
||
else:
|
||
send_message(_chat_id, "⚠️ 簡報生成失敗，請稍後再試", _reply_to)
|
||
except PPTDataInsufficientError as e:
|
||
# ADR-019 Phase 1：用戶已收到 inline keyboard 詢問，靜默結束
|
||
sys_log.info(f"[OpenClawBot] /ppt skipped due to data gap: {e}")
|
||
except Exception as e:
|
||
sys_log.error(f"[OpenClawBot] /ppt bg error: {e}", exc_info=True)
|
||
send_message(_chat_id, f"⚠️ 簡報生成失敗：{str(e)[:100]}", _reply_to)
|
||
|
||
threading.Thread(
|
||
target=_ppt_background,
|
||
args=(sub_type, sub_arg, chat_id, target, reply_to),
|
||
daemon=True
|
||
).start()
|
||
|
||
elif cmd in ('history', 'monthly', '月報', '月份'):
|
||
# /history [YYYY/MM] — 顯示月份業績，不帶參數時列出所有可用月份
|
||
months = query_available_months()
|
||
if arg and re.match(r'\d{4}/\d{2}', arg):
|
||
yr, mo_s = arg.split('/')
|
||
ms = query_monthly_summary(int(yr), int(mo_s))
|
||
msg = fmt_monthly(ms)
|
||
else:
|
||
# 列出可用月份清單
|
||
if months:
|
||
lines = ["📅 *業績月份索引*",
|
||
f"共 {len(months)} 個月份有資料", ""]
|
||
for am in months:
|
||
lines.append(f" 📊 `{am['month']}` _{am['days']} 天_")
|
||
lines.append("\n_用法：`/history 2026/03` 查看指定月份業績_")
|
||
msg = "\n".join(lines)
|
||
else:
|
||
msg = "⚠️ 暫無月份資料"
|
||
# 快捷按鈕：最近3個月 + 月報PPT（僅在查看特定月份時顯示）
|
||
kb_months = _chunk_rows(
|
||
[(f"📊 {am['month']}", f"cmd:history:{am['month']}") for am in months[:3]],
|
||
row_size=2,
|
||
)
|
||
if arg and re.match(r'\d{4}/\d{2}', arg):
|
||
# 查看特定月份時，額外顯示「產出月報PPT」按鈕
|
||
kb = kb_months if kb_months else []
|
||
kb.append(_row((f'📊 產出 {arg} 月報', f'cmd:ppt:monthly {arg}')))
|
||
else:
|
||
kb = kb_months if kb_months else None
|
||
send_message(chat_id, msg, reply_to, kb)
|
||
|
||
# ── 原有指令 ─────────────────────────────────────────────────
|
||
|
||
elif cmd in ('menu', 'start', '選單'):
|
||
send_message(chat_id,
|
||
"👋 *OpenClaw（小O）* — 電商智能助理\n\n"
|
||
"點下方按鈕，或直接用中文跟我說話 👇",
|
||
reply_to, main_menu_keyboard())
|
||
|
||
elif cmd in ('help', '幫助', '說明'):
|
||
reply = (
|
||
"📖 *OpenClaw 完整功能指南*\n"
|
||
"━━━━━━━━━━━━━━━━━━━━━━━━━━━\n\n"
|
||
|
||
"💬 *自然語言對話（直接發訊息）*\n"
|
||
" 「今天業績多少？」\n"
|
||
" 「哪個商品最需要加碼？」\n"
|
||
" 「上個月整體表現如何？」\n"
|
||
" 「幫我看外部熱搜跟銷售的落差」\n"
|
||
" 「怎麼用圖片比價？」 ← 也可問功能說明\n\n"
|
||
|
||
"📊 *業績查詢 ▸ 點「業績查詢」按鈕*\n"
|
||
" 今日/昨日業績 → 業績摘要 + TOP3 商品（可點連結）\n"
|
||
" 每週/每月/每季/半年 → 趨勢走勢\n"
|
||
" 同期比較 → vs 上週同日\n"
|
||
" 分類業績 → 各品類佔比\n"
|
||
" 日期/區間 → 單日或起迄區間 格式: `2026/04/01-2026/04/15`\n"
|
||
" 月份總覽 → 列出所有可查月份，點月份查詳情\n\n"
|
||
|
||
"🏆 *商品廠商 ▸ 點「商品廠商」按鈕*\n"
|
||
" 熱銷商品 TOP10 → 含商品ID + 可點 PChome 連結\n"
|
||
" 熱銷廠商 TOP10\n"
|
||
" 商品健康分析 → 偏低/異常/策略分佈\n"
|
||
" 補貨預測 → 動銷率 × 庫存天數估算\n"
|
||
" 分類鑽取 → 進分類後再看商品排行\n\n"
|
||
|
||
"🎯 *目標管理 ▸ 點「目標管理」按鈕*\n"
|
||
" 查看達成率 → 日/週（自動推算）/月/季/半年/年\n"
|
||
" 設定目標 → 點按鈕輸入金額\n"
|
||
" 月倒計時 → 顯示每日需達金額\n\n"
|
||
|
||
"📈 *智能分析 ▸ 點「智能分析」按鈕*\n"
|
||
" 策略矩陣 → 加碼/機會/收割/觀察/持穩\n"
|
||
" 業績趨勢 → 7日/30日/季/半年/年，含趨勢圖\n"
|
||
" 商品健康 → 異常商品 + 策略建議\n"
|
||
" 促銷追蹤 → 輸入活動區間，計算業績增幅\n"
|
||
" 📊 趨勢圖表 → 直接生成折線圖 + 熱銷橫條圖\n"
|
||
" 同期比較 → vs 上週/上月同日\n\n"
|
||
|
||
"📄 *簡報報表 ▸ 點「簡報報表」按鈕*\n"
|
||
" 日報/週報/月報 → 自動生成 .pptx\n"
|
||
" 策略簡報(日/週/月/季/半年/年)\n"
|
||
" 促銷效益簡報 → 輸入活動區間\n"
|
||
" 下載 Excel 報表\n"
|
||
" 指定日期日報 / 指定月份月報\n\n"
|
||
|
||
"🌐 *市場情報 ▸ 點「市場情報」按鈕*\n"
|
||
" 電商新聞 → 即時台灣電商資訊\n"
|
||
" 台北天氣 → 含行銷建議\n"
|
||
" 關鍵字比價 → 輸入商品名，搜尋 PChome 比價\n"
|
||
" 📷 圖片比價 → 直接傳商品照片，自動辨識比價\n\n"
|
||
|
||
"🔍 *競品日報 ▸ 點「競品日報」按鈕*\n"
|
||
" 今日/昨日/本週/本月競品比價簡報\n\n"
|
||
|
||
"⏰ *自動推播（每日無需操作）*\n"
|
||
" 08:00 競品比價日報\n"
|
||
" 08:30 每日早報（昨日業績 + TOP15 熱銷）\n"
|
||
" 21:00 每日晚報（今日業績 + TOP15 + 明日建議）\n"
|
||
" 週一 09:00 週報\n"
|
||
" 09/12/15/18 時 異常偵測（偏差>30% 即告警）\n\n"
|
||
|
||
"💡 *日期格式*：`2026/04/10` 或 `2026-04-10` 皆可\n"
|
||
"💡 *有疑問*：直接用中文問我，我會回答！"
|
||
)
|
||
help_kb = quick_menu_keyboard()
|
||
send_message(chat_id, reply, reply_to, help_kb)
|
||
|
||
elif cmd == 'ack':
|
||
# 告警確認（來自 anomaly alert 的 inline button）
|
||
action_map = {'anomaly': '已知悉', 'tracking': '追蹤中'}
|
||
label = action_map.get(arg or '', '確認')
|
||
send_message(chat_id, f"✅ *{label}* — 感謝回覆，繼續監控中。", reply_to)
|
||
# ── 回饋學習：告警 ack → quality_score +0.1 ────────
|
||
if _LEARNING_ENABLED:
|
||
import threading as _thr
|
||
feedback_type = 'acknowledged' if arg == 'anomaly' else 'tracking'
|
||
_thr.Thread(
|
||
target=update_feedback,
|
||
args=(feedback_type, msg.get('from', {}).get('id', 0)),
|
||
daemon=True
|
||
).start()
|
||
|
||
elif cmd == 'learn':
|
||
# ── 學習系統狀態查詢 ──────────────────────────────────
|
||
if _LEARNING_ENABLED:
|
||
st = get_learning_stats()
|
||
msg_text = (
|
||
"🧠 *OpenClaw 自主學習狀態*\n\n"
|
||
f"📚 知識庫：`{st.get('total_knowledge',0):,}` 條\n"
|
||
f"💬 對話記憶：`{st.get('total_conversations',0):,}` 筆\n"
|
||
f"📊 市場洞察：`{st.get('total_insights',0):,}` 份\n"
|
||
f"🛒 商品知識：`{st.get('total_products',0):,}` 個\n"
|
||
f"👍 用戶回饋：`{st.get('total_feedback',0):,}` 次\n"
|
||
f"⭐ 平均品質分：`{st.get('avg_quality',0):.2f}`\n"
|
||
f"📈 本週新增：`{st.get('knowledge_last7d',0)}` 條\n\n"
|
||
"_每次 AI 回答、PPT 分析、告警確認都會自動加入學習庫_"
|
||
)
|
||
else:
|
||
msg_text = "⚠️ 學習系統暫未啟用"
|
||
send_message(chat_id, msg_text, reply_to, parse_mode='Markdown')
|
||
|
||
elif cmd == 'import_confirm':
|
||
# ── Excel 匯入確認 ────────────────────────────────────
|
||
pending = _excel_pending.pop(chat_id, None)
|
||
if not pending:
|
||
send_message(chat_id, "⚠️ 匯入已逾期或不存在，請重新上傳 Excel 檔案。", reply_to)
|
||
return
|
||
|
||
file_path = pending['file_path']
|
||
filename = pending['filename']
|
||
|
||
def _do_import():
|
||
try:
|
||
send_message(chat_id,
|
||
f"⏳ *正在匯入 Excel 資料...*\n`{filename}`\n\n請稍候，資料量大時需要一點時間。",
|
||
None, parse_mode='Markdown')
|
||
|
||
from services.import_service import ImportService, Session
|
||
from database.import_models import ImportJob
|
||
import pytz as _pytz
|
||
_TAIPEI = _pytz.timezone('Asia/Taipei')
|
||
from datetime import datetime as _dt
|
||
svc = ImportService()
|
||
|
||
# 建立匯入任務
|
||
session = Session()
|
||
job = ImportJob(
|
||
job_type='telegram_upload',
|
||
status='pending',
|
||
drive_file_name=filename,
|
||
local_file_path=file_path,
|
||
created_at=_dt.now(_TAIPEI).replace(tzinfo=None)
|
||
)
|
||
session.add(job)
|
||
session.commit()
|
||
job_id = job.id
|
||
session.close()
|
||
|
||
# 執行匯入
|
||
success = svc.process_daily_sales_import(job_id, file_path)
|
||
status = svc.get_job_status(job_id)
|
||
|
||
# 清理暫存檔
|
||
try:
|
||
import os as _os
|
||
_os.unlink(file_path)
|
||
except Exception:
|
||
pass
|
||
|
||
if success and status.get('status') == 'completed':
|
||
sr = status.get('success_rows', 0) or 0
|
||
tr = status.get('total_rows', 0) or 0
|
||
summ = status.get('import_summary') or {}
|
||
if isinstance(summ, str):
|
||
import json as _json
|
||
try: summ = _json.loads(summ)
|
||
except Exception: summ = {}
|
||
|
||
date_min = summ.get('date_min', '')
|
||
date_max = summ.get('date_max', '')
|
||
synced = summ.get('synced_to', '')
|
||
date_range_str = (
|
||
f"`{date_min}` ~ `{date_max}`"
|
||
if date_min and date_max and date_min != date_max
|
||
else f"`{date_min}`" if date_min else '—'
|
||
)
|
||
|
||
result_msg = (
|
||
f"✅ *Excel 匯入成功！*\n"
|
||
f"{'─' * 26}\n\n"
|
||
f"📄 *檔案*：`{filename}`\n"
|
||
f"📦 *匯入筆數*：`{sr:,}` / `{tr:,}` 筆\n"
|
||
f"📅 *涵蓋日期*：{date_range_str}\n"
|
||
)
|
||
if synced:
|
||
result_msg += f"🔄 *同步至*：`{synced}`\n"
|
||
result_msg += f"\n_✨ 業績資料已更新，可立即查詢！_"
|
||
|
||
# 取涵蓋日期的 latest date 顯示快速按鈕
|
||
quick_date = date_max.replace('-', '/') if date_max else (latest_date() or '')
|
||
import_kb = [
|
||
_row((f'📊 查看 {quick_date} 業績', f'cmd:sales:{quick_date}'),
|
||
('🏆 熱銷商品排行', f'cmd:top:{quick_date}')),
|
||
_row(('📄 產出日報 PPT', f'cmd:ppt:daily {quick_date}'),
|
||
('📅 月份總覽', 'cmd:history')),
|
||
] if quick_date else None
|
||
send_message(chat_id, result_msg, None, import_kb)
|
||
else:
|
||
err = status.get('error_message', '') or '未知錯誤'
|
||
send_message(chat_id,
|
||
f"❌ *匯入失敗*\n\n`{filename}`\n\n錯誤訊息：\n`{err[:200]}`",
|
||
None, parse_mode='Markdown')
|
||
except Exception as _ie:
|
||
sys_log.error(f"[ExcelImport] import_confirm error: {_ie}", exc_info=True)
|
||
send_message(chat_id, f"❌ 匯入過程發生例外錯誤：`{str(_ie)[:150]}`", None)
|
||
|
||
threading.Thread(target=_do_import, daemon=True).start()
|
||
|
||
elif cmd == 'import_cancel':
|
||
# ── Excel 匯入取消 ────────────────────────────────────
|
||
pending = _excel_pending.pop(chat_id, None)
|
||
if pending:
|
||
try:
|
||
import os as _os
|
||
_os.unlink(pending['file_path'])
|
||
except Exception:
|
||
pass
|
||
send_message(chat_id, "✖ 已取消匯入，暫存檔案已刪除。", reply_to)
|
||
|
||
elif cmd == 'photo_search_help':
|
||
photo_help = (
|
||
"📷 *圖片比價功能說明*\n\n"
|
||
"直接在此群組傳送商品圖片，我會自動：\n"
|
||
" 1️⃣ 辨識圖片中的商品名稱\n"
|
||
" 2️⃣ 在 PChome 搜尋同款商品\n"
|
||
" 3️⃣ 回傳比價結果與競品連結\n\n"
|
||
"*使用方式*\n"
|
||
" 📤 直接拍照或截圖傳送\n"
|
||
" 🏷 或輸入「搜尋 商品名稱」（文字比價）\n\n"
|
||
"*支援商品類型*\n"
|
||
" ✅ 美妝保養品 ✅ 保健食品\n"
|
||
" ✅ 母嬰用品 ✅ 個人清潔\n"
|
||
" ✅ 食品飲料 ✅ 家電用品\n\n"
|
||
"_💡 圖片越清晰、商品標籤越完整，辨識準確度越高_"
|
||
)
|
||
send_message(chat_id, photo_help, reply_to, _submenu_market())
|
||
|
||
else:
|
||
# 不認識的指令 → 自然語言
|
||
txt, kb = openclaw_answer(cmd + (' ' + arg if arg else ''), chat_id=chat_id)
|
||
send_message(chat_id, txt, reply_to, kb)
|
||
|
||
|
||
# ── Webhook ───────────────────────────────────────────────────
|
||
@openclaw_bot_bp.route('/bot/telegram/webhook', methods=['POST'])
|
||
def telegram_webhook():
|
||
try:
|
||
update = request.get_json(silent=True)
|
||
if not update:
|
||
return jsonify({'ok': True})
|
||
|
||
# ── Telegram retry 去重 ───────────────────────────────
|
||
uid = update.get('update_id')
|
||
|
||
# ── Callback Query（按鈕）─────────────────────────────
|
||
if 'callback_query' in update:
|
||
cq = update['callback_query']
|
||
cq_id = cq['id']
|
||
data = _normalize_callback_data(cq.get('data', ''))
|
||
chat_id = cq['message']['chat']['id']
|
||
chat_type = cq['message']['chat'].get('type', '')
|
||
cq_from_id = (cq.get('from') or {}).get('id')
|
||
cq_message_id = cq.get('message', {}).get('message_id')
|
||
|
||
duplicate_key = _build_callback_dedupe_key(
|
||
update_id=uid,
|
||
cq_id=cq_id,
|
||
message_id=cq_message_id,
|
||
data=data,
|
||
chat_id=chat_id,
|
||
user_id=cq_from_id,
|
||
)
|
||
if _is_duplicate_update(duplicate_key):
|
||
sys_log.debug(
|
||
f"[OpenClawBot] duplicate callback uid={uid} cq_id={cq_id}, skip"
|
||
)
|
||
answer_callback(cq_id)
|
||
return jsonify({'ok': True})
|
||
|
||
sys_log.info(f'[OpenClawBot] CB: chat={chat_id} type={chat_type} data={data} allowed={ALLOWED_GROUP}')
|
||
|
||
# fail-closed：未授權一律安靜拒絕（關閉 loading，不回任何訊息避免偵察）
|
||
if not _is_authorized(chat_type, chat_id, cq_from_id):
|
||
sys_log.warning(
|
||
f'[OpenClawBot] CB rejected: chat={chat_id} type={chat_type} user={cq_from_id}'
|
||
)
|
||
answer_callback(cq_id)
|
||
return jsonify({'ok': False, 'error': 'forbidden'})
|
||
|
||
# critic Medium-3：把 user_id 放進 ContextVar 供 handle_cmd 內部 _is_admin 讀
|
||
_user_token = _CURRENT_USER_ID_CTX.set(cq_from_id)
|
||
|
||
answer_callback(cq_id)
|
||
send_typing(chat_id)
|
||
|
||
if data.startswith('menu:'):
|
||
# 顯示子選單或返回主選單
|
||
key = data[5:]
|
||
fn = _SUBMENUS.get(key)
|
||
if fn:
|
||
kb = fn()
|
||
titles = {
|
||
'main': '👋 *OpenClaw* — 請選擇功能類別',
|
||
'sales': '📊 *業績查詢* — 選擇日期或直接輸入',
|
||
'products': '🏆 *商品廠商* — 選擇查詢範圍',
|
||
'goals': '🎯 *目標管理* — 查看或設定業績目標',
|
||
'analysis': '📈 *智能分析* — 選擇分析類型',
|
||
'trend': '📈 *業績趨勢* — 選擇時間範圍',
|
||
'reports': '📄 *簡報報表* — 選擇報告類型',
|
||
'market': '🌐 *市場情報* — 即時資訊',
|
||
'competitor': '📊 *競品比價日報* — 選擇分析日期',
|
||
'competitor_ppt': '📄 *競品比價簡報* — 選擇時間範圍',
|
||
'category': '🗂 *分類業績鑽取* — 點選分類深入分析',
|
||
}
|
||
if cq_message_id:
|
||
result = edit_message_text(
|
||
chat_id,
|
||
cq_message_id,
|
||
titles.get(key, '請選擇'),
|
||
kb,
|
||
)
|
||
if _should_fallback_send_message(result):
|
||
send_message(chat_id, titles.get(key, '請選擇'), None, kb)
|
||
else:
|
||
send_message(chat_id, titles.get(key, '請選擇'), None, kb)
|
||
|
||
elif data.startswith('await:'):
|
||
# 進入輸入等待狀態
|
||
action = data[6:]
|
||
if action in _AWAIT_PROMPTS:
|
||
prompt_text, label = _AWAIT_PROMPTS[action]
|
||
_input_pending[chat_id] = {'action': action, 'label': label}
|
||
cancel_kb = [_row(('✖ 取消', 'menu:main'))]
|
||
if cq_message_id:
|
||
result = edit_message_text(
|
||
chat_id,
|
||
cq_message_id,
|
||
f"{prompt_text}\n\n_輸入 `/取消` 可退出_",
|
||
cancel_kb,
|
||
parse_mode='Markdown',
|
||
)
|
||
if _should_fallback_send_message(result):
|
||
send_message(
|
||
chat_id,
|
||
f"{prompt_text}\n\n_輸入 `/取消` 可退出_",
|
||
None,
|
||
cancel_kb,
|
||
parse_mode='Markdown',
|
||
)
|
||
else:
|
||
send_message(
|
||
chat_id,
|
||
f"{prompt_text}\n\n_輸入 `/取消` 可退出_",
|
||
None,
|
||
cancel_kb,
|
||
parse_mode='Markdown',
|
||
)
|
||
|
||
elif data.startswith('cmd:'):
|
||
parts = data[4:].split(':', 1)
|
||
with _run_with_callback_cmd_context():
|
||
if cq_message_id:
|
||
_orig_send_message = send_message
|
||
|
||
def _callback_send_message(
|
||
_chat_id,
|
||
_text,
|
||
_reply_to=None,
|
||
_keyboard=None,
|
||
_parse_mode="Markdown",
|
||
**_kwargs,
|
||
):
|
||
if _reply_to is None and "reply_to" in _kwargs:
|
||
_reply_to = _kwargs.pop("reply_to")
|
||
if "keyboard" in _kwargs:
|
||
_keyboard = _kwargs.pop("keyboard")
|
||
if "parse_mode" in _kwargs:
|
||
_parse_mode = _kwargs.pop("parse_mode")
|
||
|
||
if _reply_to == cq_message_id:
|
||
result = edit_message_text(
|
||
_chat_id,
|
||
cq_message_id,
|
||
_text,
|
||
_keyboard,
|
||
_parse_mode,
|
||
)
|
||
if not _should_fallback_send_message(result):
|
||
return result
|
||
|
||
return _orig_send_message(
|
||
_chat_id,
|
||
_text,
|
||
_reply_to,
|
||
_keyboard,
|
||
_parse_mode,
|
||
**_kwargs,
|
||
)
|
||
|
||
with _CALLBACK_SEND_LOCK:
|
||
try:
|
||
globals()['send_message'] = _callback_send_message
|
||
handle_cmd(parts[0], parts[1] if len(parts) > 1 else '', chat_id, cq_message_id)
|
||
finally:
|
||
globals()['send_message'] = _orig_send_message
|
||
else:
|
||
handle_cmd(parts[0], parts[1] if len(parts) > 1 else '', chat_id, cq_message_id)
|
||
|
||
return jsonify({'ok': True})
|
||
|
||
if _is_duplicate_update(uid):
|
||
sys_log.debug(f"[OpenClawBot] duplicate update_id={uid}, skip")
|
||
return jsonify({'ok': True})
|
||
|
||
# ── Message ───────────────────────────────────────────
|
||
msg = update.get('message') or update.get('edited_message')
|
||
if not msg:
|
||
return jsonify({'ok': True})
|
||
|
||
chat = msg.get('chat', {})
|
||
chat_id = chat.get('id')
|
||
chat_type = chat.get('type', '')
|
||
text_raw = (msg.get('text') or '').strip()
|
||
msg_id = msg.get('message_id')
|
||
|
||
# fail-closed 統一授權檢查（覆蓋 group/supergroup/private/channel/unknown）
|
||
_uid = (msg.get('from') or {}).get('id')
|
||
if not _is_authorized(chat_type, chat_id, _uid):
|
||
sys_log.warning(
|
||
f'[OpenClawBot] MSG rejected: chat={chat_id} type={chat_type} user={_uid}'
|
||
)
|
||
# 靜默拒絕：不回 Telegram 訊息（避免陌生人偵察 bot 存在與白名單機制）
|
||
return jsonify({'ok': False, 'error': 'forbidden'})
|
||
|
||
# critic Medium-3：把 user_id 放進 ContextVar 供 handle_cmd 內部 _is_admin 讀
|
||
_CURRENT_USER_ID_CTX.set(_uid)
|
||
|
||
if chat_type in ('group', 'supergroup'):
|
||
# 移除 @mention（不強制要求，但如有則移除）
|
||
question = text_raw.replace(BOT_USERNAME, '').strip()
|
||
else:
|
||
# 已通過授權的 private chat
|
||
question = text_raw
|
||
|
||
# ── 圖片訊息：Gemini Vision 商品辨識 ─────────────────────
|
||
if not question and msg.get('photo'):
|
||
send_typing(chat_id)
|
||
try:
|
||
photos = msg['photo']
|
||
file_id = photos[-1]['file_id'] # 取最大尺寸
|
||
# 取得 file path
|
||
r_file = requests.get(
|
||
f"{BOT_API_URL}/getFile", params={'file_id': file_id}, timeout=10
|
||
).json()
|
||
file_path_tg = r_file.get('result', {}).get('file_path', '')
|
||
if not file_path_tg:
|
||
send_message(chat_id, "⚠️ 無法取得圖片", msg_id)
|
||
return jsonify({'ok': True})
|
||
# 下載圖片
|
||
img_url = f"https://api.telegram.org/file/bot{BOT_TOKEN}/{file_path_tg}"
|
||
img_data = requests.get(img_url, timeout=15).content
|
||
import base64 as _b64
|
||
img_b64 = _b64.b64encode(img_data).decode()
|
||
# Gemini Vision 辨識商品名稱
|
||
vision_payload = {
|
||
'contents': [{
|
||
'parts': [
|
||
{'text': (
|
||
'這是一張商品圖片。請辨識商品名稱（品牌、型號、規格），'
|
||
'輸出格式：只回商品名稱，不超過 30 字，繁體中文。'
|
||
'如果是多個商品，只取最顯眼的一個。'
|
||
)},
|
||
{'inline_data': {'mime_type': 'image/jpeg', 'data': img_b64}}
|
||
]
|
||
}]
|
||
}
|
||
vis_r = requests.post(
|
||
f"{GEMINI_BASE_URL}/gemini-1.5-flash:generateContent?key={GEMINI_API_KEY}",
|
||
json=vision_payload, timeout=20
|
||
)
|
||
if vis_r.ok:
|
||
product_name = (
|
||
vis_r.json()
|
||
.get('candidates', [{}])[0]
|
||
.get('content', {})
|
||
.get('parts', [{}])[0]
|
||
.get('text', '').strip()
|
||
)
|
||
if product_name:
|
||
send_message(chat_id,
|
||
f"🔍 辨識到商品：*{product_name}*\n正在搜尋 momo 比價...",
|
||
msg_id, parse_mode='Markdown')
|
||
# 直接執行比價
|
||
handle_cmd('competitor', product_name, chat_id, msg_id)
|
||
else:
|
||
send_message(chat_id, "⚠️ 無法辨識圖片中的商品，請嘗試更清晰的圖片", msg_id)
|
||
else:
|
||
send_message(
|
||
chat_id,
|
||
"⚠️ 圖片辨識失敗，請直接輸入商品名稱搜尋",
|
||
msg_id,
|
||
[_row(('🔍 文字搜尋', 'await:search_compare'))],
|
||
)
|
||
except Exception as _img_e:
|
||
sys_log.error(f"[VisionSearch] {_img_e}")
|
||
send_message(chat_id, "⚠️ 圖片處理失敗，請直接輸入商品名稱搜尋", msg_id)
|
||
return jsonify({'ok': True})
|
||
|
||
# ── Excel 文件匯入（document）────────────────────────────
|
||
if not question and msg.get('document'):
|
||
doc = msg['document']
|
||
fname = doc.get('file_name', '')
|
||
if fname.lower().endswith(('.xlsx', '.xls')):
|
||
send_typing(chat_id)
|
||
threading.Thread(
|
||
target=_handle_excel_import,
|
||
args=(doc, chat_id, msg_id),
|
||
daemon=True
|
||
).start()
|
||
else:
|
||
send_message(chat_id,
|
||
f"⚠️ 僅支援 `.xlsx` / `.xls` 格式的業績 Excel 檔案\n"
|
||
f"收到的檔案：`{fname}`",
|
||
msg_id, parse_mode='Markdown')
|
||
return jsonify({'ok': True})
|
||
|
||
if not question:
|
||
return jsonify({'ok': True})
|
||
|
||
# ── 頻率限制 ─────────────────────────────────────────────
|
||
_uid_rl = msg.get('from', {}).get('id', 0)
|
||
if not _check_rate_limit(_uid_rl):
|
||
send_message(chat_id, "⚠️ 操作太頻繁，請稍後再試（每分鐘上限 30 次）。", msg_id)
|
||
return jsonify({'ok': True})
|
||
|
||
sys_log.info(f"[OpenClawBot] ← chat={chat_id} msg={question[:60]}")
|
||
send_typing(chat_id)
|
||
|
||
# ── 輸入等待狀態處理 ─────────────────────────────────────
|
||
if chat_id in _input_pending and question not in ('/取消', '/cancel'):
|
||
pending = _input_pending.pop(chat_id)
|
||
action = pending['action']
|
||
label = pending.get('label', '')
|
||
val = question.strip().replace(',', '').replace('NT$', '').replace('$', '').strip()
|
||
|
||
if action.startswith('goal_'):
|
||
period_map = {
|
||
'goal_daily': 'daily',
|
||
'goal_monthly': 'monthly',
|
||
'goal_quarterly': 'quarterly',
|
||
'goal_half': 'half',
|
||
'goal_yearly': 'yearly',
|
||
}
|
||
period = period_map[action]
|
||
try:
|
||
amount = float(val)
|
||
_GOALS[period] = amount
|
||
send_message(chat_id,
|
||
f"✅ *{label}* 已設定為 `NT$ {amount:,.0f}`",
|
||
msg_id, _submenu_goals(), parse_mode='Markdown')
|
||
except ValueError:
|
||
send_message(chat_id,
|
||
f"⚠️ 格式錯誤，請輸入數字（例如：`150000`）",
|
||
msg_id,
|
||
[_row((f'重新設定 {label}', f'await:{action}')), _BACK],
|
||
parse_mode='Markdown')
|
||
sys_log.info(f"[OpenClawBot] → replied chat={chat_id}")
|
||
return jsonify({'ok': True})
|
||
|
||
elif action == 'search_compare':
|
||
# PChome 比價關鍵字輸入
|
||
handle_cmd('competitor', val, chat_id, msg_id)
|
||
|
||
elif action == 'date_range_sales':
|
||
# 支援：單日 / 日期區間 / 月份
|
||
dates = re.findall(r'\d{4}[/\-]\d{1,2}[/\-]\d{1,2}', val)
|
||
month_only = re.match(r'(\d{4})[/\-](\d{1,2})$', val.strip())
|
||
if len(dates) >= 2:
|
||
s = normalize_date(dates[0])
|
||
e = normalize_date(dates[1])
|
||
if s == e:
|
||
# 起迄同日 → 單日業績
|
||
handle_cmd('sales', s, chat_id, msg_id)
|
||
else:
|
||
# 日期區間 → 趨勢
|
||
from datetime import datetime as _dt2
|
||
s_d = _dt2.strptime(s.replace('/', '-'), '%Y-%m-%d').date()
|
||
e_d = _dt2.strptime(e.replace('/', '-'), '%Y-%m-%d').date()
|
||
days_c = (e_d - s_d).days + 1
|
||
send_message(chat_id,
|
||
f"⏳ 查詢 {s} ~ {e}（{days_c}天）...",
|
||
msg_id, parse_mode=None)
|
||
data_r = query_trend_range(s, e)
|
||
if data_r:
|
||
period_label = f'{s} ~ {e}（{days_c}天）'
|
||
send_message(chat_id, fmt_trend(data_r, period_label),
|
||
msg_id, _submenu_sales())
|
||
else:
|
||
send_message(chat_id,
|
||
f"⚠️ `{s}` ~ `{e}` 查無業績資料",
|
||
msg_id, [_row(('重新輸入', 'await:date_range_sales'))],
|
||
parse_mode='Markdown')
|
||
elif len(dates) == 1:
|
||
# 單一日期
|
||
handle_cmd('sales', normalize_date(dates[0]), chat_id, msg_id)
|
||
elif month_only:
|
||
# 月份格式：2026/04
|
||
handle_cmd('history', f"{month_only.group(1)}/{int(month_only.group(2)):02d}",
|
||
chat_id, msg_id)
|
||
else:
|
||
send_message(chat_id,
|
||
"⚠️ 格式錯誤\n📌 單日：`2026/04/15`\n📌 區間：`2026/04/01-2026/04/15`\n📌 月份：`2026/04`",
|
||
msg_id,
|
||
[_row(('重新輸入', 'await:date_range_sales')), _BACK],
|
||
parse_mode='Markdown')
|
||
sys_log.info(f"[OpenClawBot] → replied chat={chat_id}")
|
||
return jsonify({'ok': True})
|
||
|
||
elif action.startswith('date_trend_'):
|
||
# 趨勢區間查詢（月份/年份/季度）
|
||
trend_val = val.replace('-', '/')
|
||
handle_cmd('trend', trend_val, chat_id, msg_id)
|
||
|
||
elif action.startswith('date_'):
|
||
# 驗證日期格式
|
||
date_val = val.replace('-', '/')
|
||
if re.match(r'\d{4}/\d{1,2}(/\d{1,2})?$', date_val):
|
||
if action == 'date_sales':
|
||
handle_cmd('sales', date_val, chat_id, msg_id)
|
||
elif action == 'date_top':
|
||
handle_cmd('top', date_val, chat_id, msg_id)
|
||
elif action == 'date_analysis':
|
||
# 分析日期：同時出矩陣 + 健康
|
||
handle_cmd('strategy', date_val, chat_id, msg_id)
|
||
elif action == 'date_ppt_daily':
|
||
handle_cmd('ppt', f'daily {date_val}', chat_id, msg_id)
|
||
elif action == 'date_ppt_monthly':
|
||
handle_cmd('ppt', f'monthly {date_val}', chat_id, msg_id)
|
||
elif action == 'date_competitor':
|
||
handle_cmd('ppt', f'competitor {date_val}', chat_id, msg_id)
|
||
else:
|
||
send_message(chat_id,
|
||
f"⚠️ 日期格式錯誤，請重新輸入（例如：`2026/04/15`）",
|
||
msg_id, [_row(('重新輸入', f'await:{action}')), _BACK], parse_mode='Markdown')
|
||
|
||
elif action == 'promo_range':
|
||
# 促銷範圍：格式 2026/04/01-2026/04/07
|
||
m = re.findall(r'\d{4}[/\-]\d{1,2}[/\-]\d{1,2}', val)
|
||
if len(m) >= 2:
|
||
handle_cmd('promo', f'{normalize_date(m[0])}-{normalize_date(m[1])}', chat_id, msg_id)
|
||
else:
|
||
send_message(chat_id,
|
||
"⚠️ 格式錯誤，例如：`2026/04/01-2026/04/07`",
|
||
msg_id, [_row(('重新輸入', 'await:promo_range'))],
|
||
parse_mode='Markdown')
|
||
sys_log.info(f"[OpenClawBot] → replied chat={chat_id}")
|
||
return jsonify({'ok': True})
|
||
|
||
# 取消輸入等待
|
||
if question in ('/取消', '/cancel') and chat_id in _input_pending:
|
||
_input_pending.pop(chat_id, None)
|
||
send_message(chat_id, '已取消', msg_id, main_menu_keyboard())
|
||
return jsonify({'ok': True})
|
||
|
||
# 解析指令（/xxx 或已知指令詞）
|
||
q = question.lstrip('/')
|
||
parts = q.split(None, 1)
|
||
cmd = parts[0].split('@', 1)[0].lower() if parts else ''
|
||
arg = parts[1] if len(parts) > 1 else ''
|
||
|
||
KNOWN = {
|
||
'sales', 'top', 'vendor', 'trend', 'report', 'news',
|
||
'weather', 'menu', 'start', 'help',
|
||
'compare', 'category', 'catdetail', 'goal', 'chart', 'health', 'strategy',
|
||
'competitor', 'price', 'restock', 'promo', 'ppt',
|
||
'業績', '熱銷', '廠商', '趨勢', '報表', '選單', '幫助',
|
||
'同比', '分類', '分類細項', '目標', '圖表', '健康', '策略', '比價',
|
||
'補貨', '促銷',
|
||
}
|
||
|
||
if question.startswith('/') or cmd in KNOWN:
|
||
handle_cmd(cmd, arg, chat_id, msg_id)
|
||
else:
|
||
txt, kb = openclaw_answer(question, chat_id=chat_id)
|
||
send_message(chat_id, txt, msg_id, kb)
|
||
|
||
sys_log.info(f"[OpenClawBot] → replied chat={chat_id}")
|
||
|
||
except Exception as e:
|
||
sys_log.error(f"[OpenClawBot] webhook error: {e}", exc_info=True)
|
||
|
||
return jsonify({'ok': True})
|
||
|
||
|
||
# ── 管理端點 ──────────────────────────────────────────────────
|
||
@openclaw_bot_bp.route('/bot/telegram/set_webhook', methods=['POST'])
|
||
def set_webhook():
|
||
webhook_url = f"{MOMO_BASE_URL}/bot/telegram/webhook"
|
||
result = _tg('setWebhook', {
|
||
'url': webhook_url,
|
||
'allowed_updates': ['message', 'callback_query'],
|
||
'drop_pending_updates': True,
|
||
})
|
||
register_commands()
|
||
sys_log.info(f"[OpenClawBot] setWebhook → {result}")
|
||
return jsonify({'ok': result.get('ok'), 'webhook_url': webhook_url})
|
||
|
||
|
||
@openclaw_bot_bp.route('/bot/telegram/webhook_info')
|
||
def webhook_info():
|
||
try:
|
||
r = requests.get(f"{BOT_API_URL}/getWebhookInfo", timeout=10)
|
||
return jsonify(r.json())
|
||
except Exception as e:
|
||
return jsonify({'ok': False, 'error': str(e)}), 500
|
||
|
||
|
||
@openclaw_bot_bp.record_once
|
||
def _on_register(_state):
|
||
"""Blueprint 被 app.register_blueprint 時自動啟動排程"""
|
||
start_scheduler()
|
||
sys_log.info("[OpenClawBot] Blueprint registered — scheduler boot triggered")
|