1778 lines
72 KiB
Python
1778 lines
72 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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商品看板路由模組
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包含：首頁儀表板、商品列表、統計數據
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"""
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import os
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import json
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import math
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import time
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import hashlib
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import pickle
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import threading
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from datetime import datetime, timezone, timedelta
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from flask import Blueprint, request, render_template
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from sqlalchemy import func, and_, text, bindparam
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from sqlalchemy.orm import joinedload
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from auth import login_required
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from config import BASE_DIR, SYSTEM_VERSION, public_url
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from database.manager import DatabaseManager
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from database.models import Product, PriceRecord
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from services.logger_manager import SystemLogger
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from services.cache_manager import (
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_DASHBOARD_DATA_CACHE,
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_DASHBOARD_CACHE_TTL,
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_DASHBOARD_SHARED_CACHE_FILE,
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_DASHBOARD_STALE_CACHE_FILE,
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)
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from utils.momo_url_utils import build_momo_product_url, normalize_momo_product_url
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# 時區設定
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TAIPEI_TZ = timezone(timedelta(hours=8))
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# Logger
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sys_log = SystemLogger("DashboardRoutes").get_logger()
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# Blueprint 定義
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dashboard_bp = Blueprint('dashboard', __name__)
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PRODUCT_PICK_LIST_LIMIT = 50
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PCHOME_MATCH_SCORE_FLOOR = 0.76
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def _build_pchome_product_url(product_id):
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if not product_id:
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return None
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return f"https://24h.pchome.com.tw/prod/{str(product_id).strip()}"
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def _build_momo_product_url(i_code):
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return build_momo_product_url(i_code)
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def _to_float(value):
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if value is None:
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return None
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try:
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return float(value)
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except (TypeError, ValueError):
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return None
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def _build_pchome_match_status(attempt=None, ineligible=None):
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if attempt:
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status = attempt.get('attempt_status') or 'unknown'
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if status == 'matched':
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score = _to_float(attempt.get('best_match_score'))
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score_text = f"最佳候選 {round(score * 100)}%" if score is not None else "已完成身份比對"
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return {
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'label': '已配對待刷新',
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'tone': 'watch',
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'summary': '曾通過 identity_v2，但目前沒有有效價格快取，等待下一輪刷新',
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'detail': score_text,
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}
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if status == 'expired_match':
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score = _to_float(attempt.get('best_match_score'))
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score_text = f"身份分數 {round(score * 100)}%" if score is not None else "已完成身份比對"
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return {
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'label': '價格過期待刷新',
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'tone': 'watch',
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'summary': '同款身份已確認，但 PChome 價格快取過期，不顯示舊價避免誤判',
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'detail': score_text,
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}
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if status == 'identity_veto':
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score = _to_float(attempt.get('best_match_score'))
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score_text = f"最佳候選 {round(score * 100)}%" if score is not None else "已拒絕候選"
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return {
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'label': '身份否決',
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'tone': 'neutral',
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'summary': '新版 identity_v2 判定不是同款，已阻擋自動比價',
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'detail': score_text,
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}
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if ineligible:
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reason = ineligible.get('reason') or 'not_eligible'
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score = _to_float(ineligible.get('match_score'))
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score_text = f"match {round(score * 100)}%" if score is not None else None
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if reason == 'expired_match':
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return {
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'label': '價格過期待刷新',
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'tone': 'watch',
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'summary': '已有高信心同款配對，但 PChome 價格快取過期，等待補抓刷新',
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'detail': score_text,
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}
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if reason == 'legacy_without_identity_v2':
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return {
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'label': '舊版配對待重驗',
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'tone': 'neutral',
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'summary': '舊版 PChome 配對尚未通過 identity_v2，不進入正式決策',
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'detail': score_text,
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}
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if reason == 'below_score_floor':
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return {
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'label': '低分配對待審',
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'tone': 'neutral',
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'summary': '已有候選但低於高信心門檻，避免錯配所以暫不採用',
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'detail': score_text,
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}
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if reason == 'invalid_price':
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return {
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'label': '價格無效待刷新',
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'tone': 'watch',
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'summary': 'PChome 配對缺少有效價格，等待下一輪補抓',
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'detail': None,
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}
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if not attempt:
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return {
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'label': '尚未搜尋',
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'tone': 'neutral',
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'summary': '尚未進入 PChome 補抓佇列',
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'detail': None,
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}
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status = attempt.get('attempt_status') or 'unknown'
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score = _to_float(attempt.get('best_match_score'))
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candidate_count = int(attempt.get('candidate_count') or 0)
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score_text = f"最佳候選 {round(score * 100)}%" if score is not None else "尚無候選分數"
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if status == 'low_score':
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diagnostic_text = attempt.get('error_message') or ''
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if 'brand_conflict' in diagnostic_text:
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label = '品牌衝突待審'
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summary = f'{score_text}，品牌不一致，已停止自動採用'
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elif any(token in diagnostic_text for token in ('volume_conflict', 'weight_conflict', 'count_conflict')):
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label = '規格衝突待審'
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summary = f'{score_text}，容量/件數不一致，已停止自動採用'
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elif 'type_conflict' in diagnostic_text:
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label = '品類衝突待審'
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summary = f'{score_text}，品類不一致，已停止自動採用'
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else:
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label = '低信心待審'
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summary = f'{score_text}，不自動採用以避免錯配'
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return {
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'label': label,
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'tone': 'neutral',
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'summary': summary,
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'detail': f'{candidate_count} 筆候選',
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}
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if status == 'needs_review':
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return {
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'label': '配對衝突待審',
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'tone': 'neutral',
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'summary': '新候選與既有配對不同，需人工確認後再覆蓋',
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'detail': f'{score_text} / {candidate_count} 筆候選',
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}
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if status in {'no_result', 'no_match'}:
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return {
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'label': '找不到同款',
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'tone': 'neutral',
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'summary': 'PChome 搜尋無可信候選，需補關鍵字或人工覆核',
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'detail': f'{candidate_count} 筆候選',
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}
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if status == 'error':
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return {
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'label': '抓取異常',
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'tone': 'risk',
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'summary': 'PChome 比對流程發生錯誤，請查看嘗試紀錄',
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'detail': attempt.get('error_message'),
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}
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return {
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'label': '待比對',
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'tone': 'neutral',
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'summary': '尚無有效 PChome 對應商品或價格快取',
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'detail': score_text,
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}
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def _build_competitor_decision(momo_price, pchome_price, match_status=None):
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if not pchome_price:
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status = match_status or _build_pchome_match_status()
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return {
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'label': status['label'],
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'tone': status['tone'],
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'gap_amount': None,
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'gap_pct': None,
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'summary': status['summary']
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}
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momo_price = float(momo_price or 0)
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pchome_price = float(pchome_price)
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gap_amount = momo_price - pchome_price
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gap_pct = (gap_amount / pchome_price * 100) if pchome_price else 0
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if gap_pct >= 5:
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return {
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'label': 'PChome 優勢',
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'tone': 'win',
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'gap_amount': gap_amount,
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'gap_pct': gap_pct,
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'summary': 'PChome 較便宜，可加強曝光與轉換'
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}
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if gap_pct <= -5:
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return {
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'label': 'MOMO 威脅',
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'tone': 'risk',
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'gap_amount': gap_amount,
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'gap_pct': gap_pct,
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'summary': 'MOMO 較便宜，需評估價格或促銷因應'
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}
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return {
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'label': '價格接近',
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'tone': 'watch',
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'gap_amount': gap_amount,
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'gap_pct': gap_pct,
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'summary': '價差有限，建議主打服務、到貨或回饋'
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}
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def _load_pchome_competitor_map(session, skus):
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sku_list = [str(sku) for sku in skus if sku]
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if not sku_list:
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return {}
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try:
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stmt = text("""
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SELECT
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sku,
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price,
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original_price,
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discount_pct,
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competitor_product_id,
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competitor_product_name,
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match_score,
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tags,
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crawled_at,
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expires_at
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FROM competitor_prices
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WHERE source = 'pchome'
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AND sku IN :skus
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AND (expires_at IS NULL OR expires_at > CURRENT_TIMESTAMP)
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AND COALESCE(match_score, 0) >= :match_score_floor
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AND COALESCE(tags, '[]'::jsonb) ? 'identity_v2'
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""").bindparams(bindparam("skus", expanding=True))
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rows = session.execute(
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stmt,
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{"skus": sku_list, "match_score_floor": PCHOME_MATCH_SCORE_FLOOR},
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).mappings().all()
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except Exception as exc:
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sys_log.warning(f"[Dashboard] PChome 競品價格資料讀取略過: {exc}")
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return {}
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result = {}
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for row in rows:
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competitor_product_id = row.get('competitor_product_id')
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result[str(row.get('sku'))] = {
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'source': 'pchome',
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'price': _to_float(row.get('price')),
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'original_price': _to_float(row.get('original_price')),
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'discount_pct': row.get('discount_pct'),
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'product_id': competitor_product_id,
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'product_name': row.get('competitor_product_name'),
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'product_url': _build_pchome_product_url(competitor_product_id),
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'match_score': _to_float(row.get('match_score')),
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'tags': row.get('tags'),
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'crawled_at': row.get('crawled_at'),
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'expires_at': row.get('expires_at'),
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}
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return result
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def _load_pchome_ineligible_competitor_map(session, skus):
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"""Read non-decision PChome rows so the UI can explain why a SKU is pending."""
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sku_list = [str(sku) for sku in skus if sku]
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if not sku_list:
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return {}
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try:
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stmt = text("""
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WITH ineligible AS (
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SELECT
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sku,
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price,
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competitor_product_id,
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competitor_product_name,
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match_score,
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tags,
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crawled_at,
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expires_at,
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CASE
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WHEN price IS NULL OR price <= 0 THEN 'invalid_price'
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WHEN (expires_at IS NOT NULL AND expires_at <= CURRENT_TIMESTAMP)
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AND COALESCE(match_score, 0) >= :match_score_floor
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AND COALESCE(tags, '[]'::jsonb) ? 'identity_v2'
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THEN 'expired_match'
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WHEN NOT (COALESCE(tags, '[]'::jsonb) ? 'identity_v2')
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THEN 'legacy_without_identity_v2'
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WHEN COALESCE(match_score, 0) < :match_score_floor
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THEN 'below_score_floor'
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ELSE 'not_eligible'
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END AS reason,
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ROW_NUMBER() OVER (
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PARTITION BY sku
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ORDER BY
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CASE
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WHEN (expires_at IS NOT NULL AND expires_at <= CURRENT_TIMESTAMP)
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AND COALESCE(match_score, 0) >= :match_score_floor
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AND COALESCE(tags, '[]'::jsonb) ? 'identity_v2'
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THEN 0
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WHEN NOT (COALESCE(tags, '[]'::jsonb) ? 'identity_v2') THEN 1
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WHEN COALESCE(match_score, 0) < :match_score_floor THEN 2
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ELSE 3
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END,
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crawled_at DESC NULLS LAST,
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match_score DESC NULLS LAST
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) AS rn
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FROM competitor_prices
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WHERE source = 'pchome'
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AND sku IN :skus
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AND NOT (
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(expires_at IS NULL OR expires_at > CURRENT_TIMESTAMP)
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AND price IS NOT NULL
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AND price > 0
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AND COALESCE(match_score, 0) >= :match_score_floor
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AND COALESCE(tags, '[]'::jsonb) ? 'identity_v2'
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)
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)
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SELECT *
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FROM ineligible
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WHERE rn = 1
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""").bindparams(bindparam("skus", expanding=True))
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rows = session.execute(
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stmt,
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{"skus": sku_list, "match_score_floor": PCHOME_MATCH_SCORE_FLOOR},
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).mappings().all()
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except Exception as exc:
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sys_log.warning(f"[Dashboard] PChome 非有效配對原因讀取略過: {exc}")
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return {}
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result = {}
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for row in rows:
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result[str(row.get('sku'))] = {
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||
'reason': row.get('reason'),
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||
'price': _to_float(row.get('price')),
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'product_id': row.get('competitor_product_id'),
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'product_name': row.get('competitor_product_name'),
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'match_score': _to_float(row.get('match_score')),
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'tags': row.get('tags'),
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'crawled_at': row.get('crawled_at'),
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'expires_at': row.get('expires_at'),
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}
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return result
|
||
|
||
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def _load_pchome_match_attempt_map(session, skus):
|
||
sku_list = [str(sku) for sku in skus if sku]
|
||
if not sku_list:
|
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return {}
|
||
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||
try:
|
||
stmt = text("""
|
||
WITH ranked AS (
|
||
SELECT
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||
sku,
|
||
attempt_status,
|
||
candidate_count,
|
||
best_competitor_product_id,
|
||
best_competitor_product_name,
|
||
best_competitor_price,
|
||
best_match_score,
|
||
error_message,
|
||
attempted_at,
|
||
ROW_NUMBER() OVER (PARTITION BY sku ORDER BY attempted_at DESC) AS rn
|
||
FROM competitor_match_attempts
|
||
WHERE source = 'pchome'
|
||
AND sku IN :skus
|
||
)
|
||
SELECT *
|
||
FROM ranked
|
||
WHERE rn = 1
|
||
""").bindparams(bindparam("skus", expanding=True))
|
||
rows = session.execute(stmt, {"skus": sku_list}).mappings().all()
|
||
except Exception as exc:
|
||
sys_log.warning(f"[Dashboard] PChome 比對嘗試資料讀取略過: {exc}")
|
||
return {}
|
||
|
||
return {str(row.get('sku')): dict(row) for row in rows}
|
||
|
||
|
||
def _format_dashboard_dt(value):
|
||
if not value:
|
||
return None
|
||
if hasattr(value, "strftime"):
|
||
return value.strftime("%Y-%m-%d %H:%M")
|
||
return str(value)
|
||
|
||
|
||
def _parse_agent_footprint(value):
|
||
if not value:
|
||
return {}
|
||
if isinstance(value, str):
|
||
try:
|
||
value = json.loads(value)
|
||
except Exception:
|
||
return {}
|
||
if not isinstance(value, dict):
|
||
return {}
|
||
agent = value.get('agent')
|
||
return agent if isinstance(agent, dict) else {}
|
||
|
||
|
||
def _ai_pick_evidence_fields(model_footprint):
|
||
agent = _parse_agent_footprint(model_footprint)
|
||
missing_evidence = agent.get('missing_evidence') or []
|
||
if isinstance(missing_evidence, str):
|
||
missing_evidence = [missing_evidence]
|
||
missing_evidence = [str(item) for item in missing_evidence if item]
|
||
return {
|
||
'opportunity_score': _to_float(agent.get('opportunity_score')) or 0,
|
||
'evidence_quality': _to_float(agent.get('evidence_quality')) or 0,
|
||
'confidence_band': agent.get('confidence_band') or 'needs_evidence',
|
||
'missing_evidence': missing_evidence,
|
||
'missing_evidence_text': '、'.join(missing_evidence),
|
||
'margin_rate': _to_float(agent.get('margin_rate')),
|
||
}
|
||
|
||
|
||
def _dashboard_decision_row(row, tone):
|
||
sku = str(row.get('sku') or '')
|
||
pchome_id = row.get('competitor_product_id')
|
||
momo_url = normalize_momo_product_url(row.get('momo_url'), sku) or _build_momo_product_url(sku)
|
||
return {
|
||
'sku': sku,
|
||
'name': row.get('name') or '',
|
||
'category': row.get('category') or '',
|
||
'momo_price': _to_float(row.get('momo_price')) or 0,
|
||
'pchome_price': _to_float(row.get('pchome_price')) or 0,
|
||
'gap_pct': _to_float(row.get('gap_pct')) or 0,
|
||
'gap_amount': _to_float(row.get('gap_amount')) or 0,
|
||
'confidence': _to_float(row.get('confidence')),
|
||
'reason': row.get('reason') or '',
|
||
'tone': tone,
|
||
'momo_url': momo_url,
|
||
'pchome_id': pchome_id,
|
||
'pchome_name': row.get('competitor_product_name') or '',
|
||
'pchome_url': _build_pchome_product_url(pchome_id),
|
||
'crawled_at': _format_dashboard_dt(row.get('crawled_at') or row.get('created_at')),
|
||
**_ai_pick_evidence_fields(row.get('model_footprint')),
|
||
}
|
||
|
||
|
||
def _load_competitor_decision_overview(session, latest_items=None):
|
||
"""讀取商品看板第一屏使用的 PChome 比價決策摘要。全部來自正式 DB。"""
|
||
cache_key = 'competitor_decision_overview'
|
||
cache_ts_key = 'competitor_decision_overview_timestamp'
|
||
cached = _DASHBOARD_DATA_CACHE.get(cache_key)
|
||
cached_ts = _DASHBOARD_DATA_CACHE.get(cache_ts_key)
|
||
if cached and cached_ts:
|
||
age = time.time() - cached_ts
|
||
if age < min(_DASHBOARD_CACHE_TTL, 300):
|
||
return cached
|
||
|
||
default = {
|
||
'total_active': 0,
|
||
'matched_count': 0,
|
||
'match_rate': 0,
|
||
'pchome_advantage_count': 0,
|
||
'momo_threat_count': 0,
|
||
'near_count': 0,
|
||
'pending_match_count': 0,
|
||
'ai_pick_count': 0,
|
||
'avg_advantage_gap': 0,
|
||
'last_pchome_crawled': None,
|
||
'top_picks': [],
|
||
'top_pchome_advantages': [],
|
||
'top_momo_threats': [],
|
||
'pending_priority': [],
|
||
}
|
||
|
||
if latest_items:
|
||
try:
|
||
item_map = {}
|
||
for item in latest_items:
|
||
record = item.get('record')
|
||
product = getattr(record, 'product', None)
|
||
sku = str(getattr(product, 'i_code', '') or '')
|
||
if not sku:
|
||
continue
|
||
safe_product_url = normalize_momo_product_url(getattr(product, 'url', None), sku)
|
||
item_map[sku] = {
|
||
'sku': sku,
|
||
'name': getattr(product, 'name', '') or '',
|
||
'category': getattr(product, 'category', '') or '',
|
||
'momo_url': safe_product_url or _build_momo_product_url(sku),
|
||
'momo_price': _to_float(getattr(record, 'price', None)) or 0,
|
||
}
|
||
|
||
competitor_map = _load_pchome_competitor_map(session, item_map.keys())
|
||
compared = []
|
||
for sku, item in item_map.items():
|
||
competitor = competitor_map.get(sku)
|
||
pchome_price = _to_float(competitor.get('price')) if competitor else None
|
||
if not pchome_price:
|
||
continue
|
||
gap_amount = item['momo_price'] - pchome_price
|
||
gap_pct = gap_amount / pchome_price * 100 if pchome_price else 0
|
||
compared.append({
|
||
**item,
|
||
'pchome_price': pchome_price,
|
||
'competitor_product_id': competitor.get('product_id'),
|
||
'competitor_product_name': competitor.get('product_name'),
|
||
'match_score': competitor.get('match_score'),
|
||
'crawled_at': competitor.get('crawled_at'),
|
||
'gap_amount': gap_amount,
|
||
'gap_pct': gap_pct,
|
||
})
|
||
|
||
picks_rows = session.execute(text("""
|
||
SELECT
|
||
sku,
|
||
name,
|
||
momo_price,
|
||
pchome_price,
|
||
gap_pct,
|
||
confidence,
|
||
reason,
|
||
model_footprint,
|
||
created_at
|
||
FROM ai_price_recommendations
|
||
WHERE strategy = 'product_pick'
|
||
AND status = 'pending'
|
||
ORDER BY confidence DESC NULLS LAST, gap_pct DESC NULLS LAST, created_at DESC
|
||
LIMIT 3
|
||
""")).mappings().all()
|
||
ai_pick_count = session.execute(text("""
|
||
SELECT COUNT(*)
|
||
FROM ai_price_recommendations
|
||
WHERE strategy = 'product_pick'
|
||
AND status = 'pending'
|
||
""")).scalar() or 0
|
||
|
||
total_active = len(item_map)
|
||
matched_count = len(compared)
|
||
pchome_advantages = [row for row in compared if row['gap_pct'] >= 5]
|
||
momo_threats = [row for row in compared if row['gap_pct'] <= -5]
|
||
near_items = [row for row in compared if -5 < row['gap_pct'] < 5]
|
||
pending_items = [
|
||
row for sku, row in item_map.items()
|
||
if sku not in competitor_map
|
||
]
|
||
last_crawled = max(
|
||
(row.get('crawled_at') for row in compared if row.get('crawled_at')),
|
||
default=None,
|
||
)
|
||
|
||
overview = dict(default)
|
||
overview.update({
|
||
'total_active': total_active,
|
||
'matched_count': matched_count,
|
||
'match_rate': round(matched_count / max(total_active, 1) * 100, 1),
|
||
'pchome_advantage_count': len(pchome_advantages),
|
||
'momo_threat_count': len(momo_threats),
|
||
'near_count': len(near_items),
|
||
'pending_match_count': max(total_active - matched_count, 0),
|
||
'ai_pick_count': int(ai_pick_count),
|
||
'avg_advantage_gap': round(
|
||
sum(row['gap_pct'] for row in pchome_advantages) / len(pchome_advantages),
|
||
1,
|
||
) if pchome_advantages else 0,
|
||
'last_pchome_crawled': _format_dashboard_dt(last_crawled),
|
||
})
|
||
overview['top_pchome_advantages'] = [
|
||
_dashboard_decision_row(row, 'win')
|
||
for row in sorted(pchome_advantages, key=lambda row: row['gap_pct'], reverse=True)[:3]
|
||
]
|
||
overview['top_momo_threats'] = [
|
||
_dashboard_decision_row(row, 'risk')
|
||
for row in sorted(momo_threats, key=lambda row: row['gap_pct'])[:3]
|
||
]
|
||
overview['top_picks'] = []
|
||
for row in picks_rows:
|
||
pick = dict(row)
|
||
competitor = competitor_map.get(str(pick.get('sku') or '')) or {}
|
||
pick['competitor_product_id'] = competitor.get('product_id')
|
||
pick['competitor_product_name'] = competitor.get('product_name')
|
||
pick['crawled_at'] = competitor.get('crawled_at')
|
||
overview['top_picks'].append(_dashboard_decision_row(pick, 'pick'))
|
||
overview['pending_priority'] = [
|
||
{
|
||
'sku': row['sku'],
|
||
'name': row['name'],
|
||
'category': row['category'],
|
||
'momo_price': row['momo_price'],
|
||
'momo_url': normalize_momo_product_url(row.get('momo_url'), row.get('sku')) or _build_momo_product_url(row.get('sku')),
|
||
}
|
||
for row in sorted(pending_items, key=lambda row: row['momo_price'], reverse=True)[:3]
|
||
]
|
||
_DASHBOARD_DATA_CACHE[cache_key] = overview
|
||
_DASHBOARD_DATA_CACHE[cache_ts_key] = time.time()
|
||
return overview
|
||
except Exception as exc:
|
||
sys_log.warning(f"[Dashboard] PChome 比價快取摘要讀取略過，改用 SQL 後備: {exc}")
|
||
try:
|
||
session.rollback()
|
||
except Exception:
|
||
pass
|
||
|
||
latest_compared_cte = f"""
|
||
WITH latest_momo AS (
|
||
SELECT
|
||
p.id AS product_id,
|
||
p.i_code AS sku,
|
||
p.name,
|
||
p.url AS momo_url,
|
||
p.category,
|
||
pr.price AS momo_price,
|
||
ROW_NUMBER() OVER (PARTITION BY p.id ORDER BY pr.timestamp DESC, pr.id DESC) AS rn
|
||
FROM products p
|
||
JOIN price_records pr ON pr.product_id = p.id
|
||
WHERE p.status = 'ACTIVE'
|
||
),
|
||
latest_products AS (
|
||
SELECT * FROM latest_momo WHERE rn = 1
|
||
),
|
||
valid_competitor AS (
|
||
SELECT DISTINCT ON (cp.sku)
|
||
cp.sku,
|
||
cp.price AS pchome_price,
|
||
cp.competitor_product_id,
|
||
cp.competitor_product_name,
|
||
cp.match_score,
|
||
cp.crawled_at
|
||
FROM competitor_prices cp
|
||
WHERE cp.source = 'pchome'
|
||
AND (cp.expires_at IS NULL OR cp.expires_at > CURRENT_TIMESTAMP)
|
||
AND cp.price IS NOT NULL
|
||
AND cp.price > 0
|
||
AND COALESCE(cp.match_score, 0) >= {PCHOME_MATCH_SCORE_FLOOR}
|
||
AND COALESCE(cp.tags, '[]'::jsonb) ? 'identity_v2'
|
||
ORDER BY cp.sku, cp.crawled_at DESC NULLS LAST
|
||
),
|
||
compared AS (
|
||
SELECT
|
||
lp.*,
|
||
vc.pchome_price,
|
||
vc.competitor_product_id,
|
||
vc.competitor_product_name,
|
||
vc.match_score,
|
||
vc.crawled_at,
|
||
(lp.momo_price - vc.pchome_price) AS gap_amount,
|
||
((lp.momo_price - vc.pchome_price) / vc.pchome_price * 100) AS gap_pct
|
||
FROM latest_products lp
|
||
JOIN valid_competitor vc ON vc.sku = lp.sku
|
||
)
|
||
"""
|
||
|
||
stats_sql = text(latest_compared_cte + """
|
||
SELECT
|
||
(SELECT COUNT(*) FROM products WHERE status = 'ACTIVE') AS total_active,
|
||
(SELECT COUNT(*) FROM compared) AS matched_count,
|
||
(SELECT COUNT(*) FROM compared WHERE gap_pct >= 5) AS pchome_advantage_count,
|
||
(SELECT COUNT(*) FROM compared WHERE gap_pct <= -5) AS momo_threat_count,
|
||
(SELECT COUNT(*) FROM compared WHERE gap_pct > -5 AND gap_pct < 5) AS near_count,
|
||
(SELECT COALESCE(ROUND(AVG(gap_pct)::numeric, 1), 0) FROM compared WHERE gap_pct >= 5) AS avg_advantage_gap,
|
||
(SELECT COUNT(*) FROM ai_price_recommendations WHERE strategy = 'product_pick' AND status = 'pending') AS ai_pick_count,
|
||
(SELECT MAX(crawled_at) FROM competitor_prices WHERE source = 'pchome') AS last_pchome_crawled
|
||
""")
|
||
|
||
advantage_sql = text(latest_compared_cte + """
|
||
SELECT *
|
||
FROM compared
|
||
WHERE gap_pct >= 5
|
||
ORDER BY gap_pct DESC NULLS LAST, crawled_at DESC NULLS LAST
|
||
LIMIT 3
|
||
""")
|
||
|
||
threat_sql = text(latest_compared_cte + """
|
||
SELECT *
|
||
FROM compared
|
||
WHERE gap_pct <= -5
|
||
ORDER BY gap_pct ASC NULLS LAST, crawled_at DESC NULLS LAST
|
||
LIMIT 3
|
||
""")
|
||
|
||
pending_sql = text(f"""
|
||
WITH latest_momo AS (
|
||
SELECT
|
||
p.i_code AS sku,
|
||
p.name,
|
||
p.url AS momo_url,
|
||
p.category,
|
||
pr.price AS momo_price,
|
||
ROW_NUMBER() OVER (PARTITION BY p.id ORDER BY pr.timestamp DESC, pr.id DESC) AS rn
|
||
FROM products p
|
||
JOIN price_records pr ON pr.product_id = p.id
|
||
WHERE p.status = 'ACTIVE'
|
||
)
|
||
SELECT lm.*
|
||
FROM latest_momo lm
|
||
LEFT JOIN competitor_prices cp
|
||
ON cp.sku = lm.sku
|
||
AND cp.source = 'pchome'
|
||
AND (cp.expires_at IS NULL OR cp.expires_at > CURRENT_TIMESTAMP)
|
||
AND cp.price IS NOT NULL
|
||
AND cp.price > 0
|
||
AND COALESCE(cp.match_score, 0) >= {PCHOME_MATCH_SCORE_FLOOR}
|
||
AND COALESCE(cp.tags, '[]'::jsonb) ? 'identity_v2'
|
||
WHERE lm.rn = 1
|
||
AND cp.sku IS NULL
|
||
ORDER BY lm.momo_price DESC NULLS LAST
|
||
LIMIT 3
|
||
""")
|
||
|
||
picks_sql = text(f"""
|
||
WITH valid_competitor AS (
|
||
SELECT DISTINCT ON (cp.sku)
|
||
cp.sku,
|
||
cp.competitor_product_id,
|
||
cp.competitor_product_name,
|
||
cp.crawled_at
|
||
FROM competitor_prices cp
|
||
WHERE cp.source = 'pchome'
|
||
AND (cp.expires_at IS NULL OR cp.expires_at > CURRENT_TIMESTAMP)
|
||
AND cp.price IS NOT NULL
|
||
AND cp.price > 0
|
||
AND COALESCE(cp.match_score, 0) >= {PCHOME_MATCH_SCORE_FLOOR}
|
||
AND COALESCE(cp.tags, '[]'::jsonb) ? 'identity_v2'
|
||
ORDER BY cp.sku, cp.crawled_at DESC NULLS LAST
|
||
)
|
||
SELECT
|
||
ar.sku,
|
||
ar.name,
|
||
ar.momo_price,
|
||
ar.pchome_price,
|
||
ar.gap_pct,
|
||
ar.confidence,
|
||
ar.reason,
|
||
ar.model_footprint,
|
||
ar.created_at,
|
||
vc.competitor_product_id,
|
||
vc.competitor_product_name,
|
||
vc.crawled_at
|
||
FROM ai_price_recommendations ar
|
||
LEFT JOIN valid_competitor vc ON vc.sku = ar.sku
|
||
WHERE ar.strategy = 'product_pick'
|
||
AND ar.status = 'pending'
|
||
ORDER BY ar.confidence DESC NULLS LAST, ar.gap_pct DESC NULLS LAST, ar.created_at DESC
|
||
LIMIT 3
|
||
""")
|
||
|
||
try:
|
||
stats = session.execute(stats_sql).mappings().first()
|
||
overview = dict(default)
|
||
if stats:
|
||
total_active = int(stats.get('total_active') or 0)
|
||
matched_count = int(stats.get('matched_count') or 0)
|
||
overview.update({
|
||
'total_active': total_active,
|
||
'matched_count': matched_count,
|
||
'match_rate': round(matched_count / max(total_active, 1) * 100, 1),
|
||
'pchome_advantage_count': int(stats.get('pchome_advantage_count') or 0),
|
||
'momo_threat_count': int(stats.get('momo_threat_count') or 0),
|
||
'near_count': int(stats.get('near_count') or 0),
|
||
'pending_match_count': max(total_active - matched_count, 0),
|
||
'ai_pick_count': int(stats.get('ai_pick_count') or 0),
|
||
'avg_advantage_gap': _to_float(stats.get('avg_advantage_gap')) or 0,
|
||
'last_pchome_crawled': _format_dashboard_dt(stats.get('last_pchome_crawled')),
|
||
})
|
||
|
||
overview['top_pchome_advantages'] = [
|
||
_dashboard_decision_row(row, 'win')
|
||
for row in session.execute(advantage_sql).mappings().all()
|
||
]
|
||
overview['top_momo_threats'] = [
|
||
_dashboard_decision_row(row, 'risk')
|
||
for row in session.execute(threat_sql).mappings().all()
|
||
]
|
||
overview['top_picks'] = [
|
||
_dashboard_decision_row(row, 'pick')
|
||
for row in session.execute(picks_sql).mappings().all()
|
||
]
|
||
overview['pending_priority'] = [
|
||
{
|
||
'sku': str(row.get('sku') or ''),
|
||
'name': row.get('name') or '',
|
||
'category': row.get('category') or '',
|
||
'momo_price': _to_float(row.get('momo_price')) or 0,
|
||
'momo_url': normalize_momo_product_url(row.get('momo_url'), row.get('sku')) or _build_momo_product_url(row.get('sku')),
|
||
}
|
||
for row in session.execute(pending_sql).mappings().all()
|
||
]
|
||
_DASHBOARD_DATA_CACHE[cache_key] = overview
|
||
_DASHBOARD_DATA_CACHE[cache_ts_key] = time.time()
|
||
return overview
|
||
except Exception as exc:
|
||
sys_log.warning(f"[Dashboard] PChome 比價決策摘要讀取略過: {exc}")
|
||
try:
|
||
session.rollback()
|
||
except Exception:
|
||
pass
|
||
_DASHBOARD_DATA_CACHE[cache_key] = default
|
||
_DASHBOARD_DATA_CACHE[cache_ts_key] = time.time()
|
||
return default
|
||
|
||
|
||
def _load_ai_pick_selection(session, limit=PRODUCT_PICK_LIST_LIMIT):
|
||
"""讀取商品看板 AI 挑品清單排序，供列表篩選使用。"""
|
||
sql = text("""
|
||
SELECT
|
||
sku,
|
||
confidence,
|
||
reason,
|
||
momo_price,
|
||
pchome_price,
|
||
gap_pct,
|
||
model_footprint,
|
||
created_at
|
||
FROM ai_price_recommendations
|
||
WHERE strategy = 'product_pick'
|
||
AND status = 'pending'
|
||
ORDER BY confidence DESC NULLS LAST, gap_pct DESC NULLS LAST, created_at DESC
|
||
LIMIT :limit
|
||
""")
|
||
|
||
try:
|
||
rows = session.execute(sql, {"limit": limit}).mappings().all()
|
||
except Exception as exc:
|
||
sys_log.warning(f"[Dashboard] AI 挑品清單讀取略過: {exc}")
|
||
try:
|
||
session.rollback()
|
||
except Exception:
|
||
pass
|
||
return [], {}
|
||
|
||
skus = []
|
||
pick_map = {}
|
||
for idx, row in enumerate(rows, start=1):
|
||
sku = str(row.get('sku') or '')
|
||
if not sku or sku in pick_map:
|
||
continue
|
||
skus.append(sku)
|
||
pick_map[sku] = {
|
||
'rank': idx,
|
||
'confidence': _to_float(row.get('confidence')) or 0,
|
||
'reason': row.get('reason') or '',
|
||
'momo_price': _to_float(row.get('momo_price')) or 0,
|
||
'pchome_price': _to_float(row.get('pchome_price')) or 0,
|
||
'gap_pct': _to_float(row.get('gap_pct')) or 0,
|
||
'created_at': _format_dashboard_dt(row.get('created_at')),
|
||
**_ai_pick_evidence_fields(row.get('model_footprint')),
|
||
}
|
||
return skus, pick_map
|
||
|
||
|
||
def _summarize_ai_pick_selection(ai_pick_map):
|
||
"""彙整目前 AI 挑品清單的可操作摘要，全部來自 ai_price_recommendations。"""
|
||
picks = list(ai_pick_map.values())
|
||
if not picks:
|
||
return {
|
||
'count': 0,
|
||
'avg_confidence': 0,
|
||
'avg_evidence_quality': 0,
|
||
'avg_opportunity_score': 0,
|
||
'avg_gap_pct': 0,
|
||
'max_gap_pct': 0,
|
||
'total_gap_amount': 0,
|
||
'high_confidence_count': 0,
|
||
'needs_evidence_count': 0,
|
||
'top_missing_evidence': [],
|
||
'generated_at': None,
|
||
}
|
||
|
||
confidence_values = [pick.get('confidence', 0) for pick in picks]
|
||
evidence_values = [pick.get('evidence_quality', 0) for pick in picks]
|
||
opportunity_values = [pick.get('opportunity_score', 0) for pick in picks]
|
||
gap_values = [pick.get('gap_pct', 0) for pick in picks]
|
||
missing_counts = {}
|
||
for pick in picks:
|
||
for label in pick.get('missing_evidence', []):
|
||
missing_counts[label] = missing_counts.get(label, 0) + 1
|
||
total_gap_amount = sum(
|
||
max((pick.get('momo_price') or 0) - (pick.get('pchome_price') or 0), 0)
|
||
for pick in picks
|
||
)
|
||
|
||
return {
|
||
'count': len(picks),
|
||
'avg_confidence': round(sum(confidence_values) / len(confidence_values), 3),
|
||
'avg_evidence_quality': round(sum(evidence_values) / len(evidence_values), 1),
|
||
'avg_opportunity_score': round(sum(opportunity_values) / len(opportunity_values), 1),
|
||
'avg_gap_pct': round(sum(gap_values) / len(gap_values), 1),
|
||
'max_gap_pct': round(max(gap_values), 1),
|
||
'total_gap_amount': round(total_gap_amount),
|
||
'high_confidence_count': sum(1 for value in confidence_values if value >= 0.65),
|
||
'needs_evidence_count': sum(1 for pick in picks if pick.get('confidence_band') == 'needs_evidence'),
|
||
'top_missing_evidence': [
|
||
{'label': label, 'count': count}
|
||
for label, count in sorted(missing_counts.items(), key=lambda item: item[1], reverse=True)[:3]
|
||
],
|
||
'generated_at': max(
|
||
(pick.get('created_at') for pick in picks if pick.get('created_at')),
|
||
default=None,
|
||
),
|
||
}
|
||
|
||
|
||
# ==========================================
|
||
# 快取與監控變數
|
||
# ==========================================
|
||
import fcntl
|
||
|
||
_DASHBOARD_LOCK_FILE = os.path.join(BASE_DIR, 'data', '.dashboard_cache.lock') # V-Opt: 檔案鎖（跨進程）
|
||
|
||
|
||
class FileLock:
|
||
"""簡單的檔案鎖，用於 gunicorn 多進程環境"""
|
||
def __init__(self, lock_file):
|
||
self.lock_file = lock_file
|
||
self.fd = None
|
||
|
||
def acquire(self, blocking=True):
|
||
"""取得鎖"""
|
||
try:
|
||
self.fd = open(self.lock_file, 'w')
|
||
if blocking:
|
||
fcntl.flock(self.fd, fcntl.LOCK_EX)
|
||
else:
|
||
fcntl.flock(self.fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
|
||
return True
|
||
except (IOError, OSError):
|
||
if self.fd:
|
||
self.fd.close()
|
||
self.fd = None
|
||
return False
|
||
|
||
def release(self):
|
||
"""釋放鎖"""
|
||
if self.fd:
|
||
fcntl.flock(self.fd, fcntl.LOCK_UN)
|
||
self.fd.close()
|
||
self.fd = None
|
||
|
||
|
||
_DASHBOARD_STALE_CACHE_MAX_AGE = 86400
|
||
_DASHBOARD_REFRESH_STATE = {
|
||
'started_at': 0,
|
||
'running': False,
|
||
}
|
||
|
||
|
||
def _new_dashboard_file_lock():
|
||
return FileLock(_DASHBOARD_LOCK_FILE)
|
||
|
||
|
||
def _load_dashboard_cache_file(now, cache_path, *, allow_stale=False, label='共享'):
|
||
"""讀取跨 worker 商品看板深度快取；必要時可讀舊快取救援首屏。"""
|
||
cache_file = str(cache_path)
|
||
if not os.path.exists(cache_file):
|
||
return None
|
||
|
||
try:
|
||
with open(cache_file, 'rb') as f:
|
||
payload = pickle.load(f)
|
||
|
||
full_timestamp = payload.get('full_timestamp')
|
||
full_data = payload.get('full_data')
|
||
if not full_timestamp or not full_data:
|
||
return None
|
||
|
||
age = now.timestamp() - full_timestamp
|
||
max_age = _DASHBOARD_STALE_CACHE_MAX_AGE if allow_stale else _DASHBOARD_CACHE_TTL
|
||
if age >= max_age:
|
||
return None
|
||
|
||
_DASHBOARD_DATA_CACHE['full_data'] = full_data
|
||
_DASHBOARD_DATA_CACHE['full_timestamp'] = full_timestamp
|
||
_DASHBOARD_DATA_CACHE['consolidated_data'] = payload.get('consolidated_data')
|
||
_DASHBOARD_DATA_CACHE['consolidated_timestamp'] = payload.get('consolidated_timestamp')
|
||
_DASHBOARD_DATA_CACHE['today_start'] = payload.get('today_start')
|
||
level = sys_log.info if allow_stale and age >= _DASHBOARD_CACHE_TTL else sys_log.debug
|
||
level(f"[Dashboard] [Cache] ✅ 使用{label}完整看板快取 | 快取年齡: {age:.0f}秒")
|
||
return full_data
|
||
except Exception as exc:
|
||
sys_log.warning(f"[Dashboard] [Cache] {label}快取讀取失敗，改走資料庫重建: {exc}")
|
||
return None
|
||
|
||
|
||
def _load_shared_full_dashboard_cache(now):
|
||
return _load_dashboard_cache_file(now, _DASHBOARD_SHARED_CACHE_FILE)
|
||
|
||
|
||
def _load_stale_full_dashboard_cache(now):
|
||
stale_data = _load_dashboard_cache_file(
|
||
now,
|
||
_DASHBOARD_STALE_CACHE_FILE,
|
||
allow_stale=True,
|
||
label='舊版救援',
|
||
)
|
||
if stale_data:
|
||
return stale_data
|
||
return _load_dashboard_cache_file(
|
||
now,
|
||
_DASHBOARD_SHARED_CACHE_FILE,
|
||
allow_stale=True,
|
||
label='過期救援',
|
||
)
|
||
|
||
|
||
def _load_expired_shared_full_dashboard_cache(now):
|
||
"""只讀原 shared cache 檔的過期版本；不讀 clear_cache 後搬走的 stale 檔。"""
|
||
return _load_dashboard_cache_file(
|
||
now,
|
||
_DASHBOARD_SHARED_CACHE_FILE,
|
||
allow_stale=True,
|
||
label='過期共享',
|
||
)
|
||
|
||
|
||
def _trigger_dashboard_background_refresh(reason):
|
||
"""使用過期 shared cache 先回首頁時，背景補一次 fresh cache，避免使用者卡 10s。"""
|
||
now_ts = time.time()
|
||
if _DASHBOARD_REFRESH_STATE['running']:
|
||
return
|
||
if now_ts - _DASHBOARD_REFRESH_STATE['started_at'] < 60:
|
||
return
|
||
|
||
def _refresh():
|
||
_DASHBOARD_REFRESH_STATE['running'] = True
|
||
try:
|
||
warm_full_dashboard_cache(reason=reason, force_rebuild=True)
|
||
except Exception as exc:
|
||
sys_log.warning(f"[Dashboard] [Cache] 背景預熱失敗: {exc}")
|
||
finally:
|
||
_DASHBOARD_REFRESH_STATE['running'] = False
|
||
|
||
_DASHBOARD_REFRESH_STATE['started_at'] = now_ts
|
||
thread = threading.Thread(target=_refresh, name='dashboard-cache-refresh', daemon=True)
|
||
thread.start()
|
||
|
||
|
||
def _write_shared_full_dashboard_cache(full_data):
|
||
"""原子寫入跨 worker 共享的商品看板深度快取。"""
|
||
cache_file = str(_DASHBOARD_SHARED_CACHE_FILE)
|
||
tmp_file = f"{cache_file}.{os.getpid()}.tmp"
|
||
payload = {
|
||
'full_data': full_data,
|
||
'full_timestamp': _DASHBOARD_DATA_CACHE.get('full_timestamp'),
|
||
'consolidated_data': _DASHBOARD_DATA_CACHE.get('consolidated_data'),
|
||
'consolidated_timestamp': _DASHBOARD_DATA_CACHE.get('consolidated_timestamp'),
|
||
'today_start': _DASHBOARD_DATA_CACHE.get('today_start'),
|
||
}
|
||
try:
|
||
os.makedirs(os.path.dirname(cache_file), exist_ok=True)
|
||
with open(tmp_file, 'wb') as f:
|
||
pickle.dump(payload, f, protocol=pickle.HIGHEST_PROTOCOL)
|
||
os.replace(tmp_file, cache_file)
|
||
except Exception as exc:
|
||
sys_log.warning(f"[Dashboard] [Cache] 共享快取寫入失敗，仍保留記憶體快取: {exc}")
|
||
try:
|
||
if os.path.exists(tmp_file):
|
||
os.remove(tmp_file)
|
||
except OSError:
|
||
pass
|
||
|
||
|
||
def warm_full_dashboard_cache(reason='manual', force_rebuild=False):
|
||
"""供 API、排程與 Gunicorn worker 啟動時預熱商品看板完整快取。"""
|
||
started = time.time()
|
||
data = get_full_dashboard_data(force_rebuild=force_rebuild)
|
||
duration_ms = (time.time() - started) * 1000
|
||
sys_log.info(
|
||
f"[Dashboard] [Cache] ✅ 預熱完成 | reason={reason} | "
|
||
f"items={len(data.get('unique_items', [])) if data else 0} | 耗時={duration_ms:.0f}ms"
|
||
)
|
||
return data
|
||
|
||
# 慢查詢監控
|
||
_SLOW_QUERY_STATS = {
|
||
'total_queries': 0,
|
||
'slow_queries': 0,
|
||
'very_slow_queries': 0,
|
||
'total_query_time_ms': 0,
|
||
'last_slow_query': None,
|
||
'last_slow_query_time': None,
|
||
}
|
||
_SLOW_QUERY_THRESHOLD_MS = 1000
|
||
_VERY_SLOW_QUERY_THRESHOLD_MS = 5000
|
||
|
||
|
||
def track_query_time(query_name, duration_ms):
|
||
"""追蹤查詢時間，更新慢查詢統計"""
|
||
global _SLOW_QUERY_STATS
|
||
_SLOW_QUERY_STATS['total_queries'] += 1
|
||
_SLOW_QUERY_STATS['total_query_time_ms'] += duration_ms
|
||
|
||
if duration_ms >= _VERY_SLOW_QUERY_THRESHOLD_MS:
|
||
_SLOW_QUERY_STATS['very_slow_queries'] += 1
|
||
_SLOW_QUERY_STATS['slow_queries'] += 1
|
||
_SLOW_QUERY_STATS['last_slow_query'] = query_name
|
||
_SLOW_QUERY_STATS['last_slow_query_time'] = datetime.now(TAIPEI_TZ).isoformat()
|
||
elif duration_ms >= _SLOW_QUERY_THRESHOLD_MS:
|
||
_SLOW_QUERY_STATS['slow_queries'] += 1
|
||
_SLOW_QUERY_STATS['last_slow_query'] = query_name
|
||
_SLOW_QUERY_STATS['last_slow_query_time'] = datetime.now(TAIPEI_TZ).isoformat()
|
||
|
||
|
||
# ==========================================
|
||
# 輔助函數
|
||
# ==========================================
|
||
|
||
def get_color_for_string(s):
|
||
"""為字串生成一個穩定且美觀的 HSL 顏色"""
|
||
if not s:
|
||
return "hsl(0, 0%, 85%)"
|
||
hash_val = int(hashlib.md5(s.encode('utf-8'), usedforsecurity=False).hexdigest(), 16)
|
||
hue = hash_val % 360
|
||
return f"hsl({hue}, 60%, 88%)"
|
||
|
||
|
||
def load_scheduler_stats():
|
||
"""讀取排程統計資料"""
|
||
stats_path = os.path.join(BASE_DIR, 'data', 'scheduler_stats.json')
|
||
if os.path.exists(stats_path):
|
||
try:
|
||
with open(stats_path, 'r', encoding='utf-8') as f:
|
||
return json.load(f)
|
||
except (IOError, json.JSONDecodeError):
|
||
return {}
|
||
return {}
|
||
|
||
|
||
# ==========================================
|
||
# 核心數據函數
|
||
# ==========================================
|
||
|
||
def get_consolidated_data():
|
||
"""統一封裝：獲取全分類去重後的當前數據、昨日對比及差值 (帶快取)"""
|
||
global _DASHBOARD_DATA_CACHE
|
||
|
||
now = datetime.now(TAIPEI_TZ)
|
||
|
||
# V-Opt: 先檢查快取（無需鎖）
|
||
if (_DASHBOARD_DATA_CACHE['consolidated_data'] is not None and
|
||
_DASHBOARD_DATA_CACHE['consolidated_timestamp'] is not None):
|
||
cache_age = (now.timestamp() - _DASHBOARD_DATA_CACHE['consolidated_timestamp'])
|
||
if cache_age < _DASHBOARD_CACHE_TTL:
|
||
sys_log.debug(f"[Dashboard] [Cache] ✅ 使用快取資料 | 快取年齡: {cache_age:.1f}秒")
|
||
return _DASHBOARD_DATA_CACHE['consolidated_data'], _DASHBOARD_DATA_CACHE['today_start']
|
||
|
||
# V-Opt: 使用檔案鎖避免多 gunicorn worker 同時重建快取
|
||
# 注意: get_consolidated_data 通常由 get_full_dashboard_data 調用，
|
||
# 後者已持有 _DASHBOARD_FILE_LOCK，因此這裡可以不重複鎖定
|
||
# 但為避免直接調用時的競爭問題，仍保留快取檢查邏輯
|
||
|
||
# 再次檢查快取（可能其他 worker 已經更新）
|
||
if (_DASHBOARD_DATA_CACHE['consolidated_data'] is not None and
|
||
_DASHBOARD_DATA_CACHE['consolidated_timestamp'] is not None):
|
||
cache_age = (now.timestamp() - _DASHBOARD_DATA_CACHE['consolidated_timestamp'])
|
||
if cache_age < _DASHBOARD_CACHE_TTL:
|
||
sys_log.debug(f"[Dashboard] [Cache] ✅ 使用快取資料 (其他 worker 已更新) | 快取年齡: {cache_age:.1f}秒")
|
||
return _DASHBOARD_DATA_CACHE['consolidated_data'], _DASHBOARD_DATA_CACHE['today_start']
|
||
|
||
sys_log.debug("[Dashboard] [Cache] 🔄 快取過期或不存在，重新查詢資料庫")
|
||
query_start_time = time.time()
|
||
|
||
db = DatabaseManager()
|
||
session = db.get_session()
|
||
today_start = now.replace(hour=0, minute=0, second=0, microsecond=0) # 保持台北時區
|
||
seven_days_ago = today_start - timedelta(days=7)
|
||
thirty_days_ago = today_start - timedelta(days=30)
|
||
|
||
try:
|
||
# Query 1: Get the latest price record for every product
|
||
latest_price_subq = session.query(
|
||
func.max(PriceRecord.id).label('max_id')
|
||
).group_by(PriceRecord.product_id).subquery()
|
||
|
||
latest_records = session.query(PriceRecord).options(
|
||
joinedload(PriceRecord.product)
|
||
).join(latest_price_subq, PriceRecord.id == latest_price_subq.c.max_id).all()
|
||
|
||
product_ids = [r.product_id for r in latest_records]
|
||
if not product_ids:
|
||
session.close()
|
||
return [], today_start
|
||
|
||
# Query 2: Get yesterday's closing prices for all products
|
||
yesterday_prices_subq = session.query(
|
||
PriceRecord.product_id,
|
||
func.max(PriceRecord.id).label('max_id')
|
||
).filter(
|
||
PriceRecord.product_id.in_(product_ids),
|
||
PriceRecord.timestamp < today_start
|
||
).group_by(PriceRecord.product_id).subquery()
|
||
|
||
yesterday_prices_q = session.query(
|
||
PriceRecord.product_id, PriceRecord.price
|
||
).join(
|
||
yesterday_prices_subq,
|
||
PriceRecord.id == yesterday_prices_subq.c.max_id
|
||
)
|
||
yesterday_prices_map = {pid: price for pid, price in yesterday_prices_q}
|
||
|
||
# Query 3: Get specific historical price points (7 days ago and 30 days ago)
|
||
def get_price_map_before(target_date):
|
||
subq = session.query(
|
||
PriceRecord.product_id,
|
||
func.max(PriceRecord.timestamp).label('max_ts')
|
||
).filter(
|
||
PriceRecord.product_id.in_(product_ids),
|
||
PriceRecord.timestamp < target_date
|
||
).group_by(PriceRecord.product_id).subquery()
|
||
|
||
q = session.query(PriceRecord.product_id, PriceRecord.price).join(
|
||
subq,
|
||
and_(PriceRecord.product_id == subq.c.product_id, PriceRecord.timestamp == subq.c.max_ts)
|
||
)
|
||
return {pid: price for pid, price in q}
|
||
|
||
prices_7d_ago_map = get_price_map_before(seven_days_ago + timedelta(days=1))
|
||
prices_30d_ago_map = get_price_map_before(thirty_days_ago + timedelta(days=1))
|
||
|
||
# Query 4: Get TODAY's records only (for sparkline/intraday change)
|
||
today_records_q = session.query(PriceRecord).filter(
|
||
PriceRecord.product_id.in_(product_ids),
|
||
PriceRecord.timestamp >= today_start
|
||
).order_by(PriceRecord.product_id, PriceRecord.timestamp).all()
|
||
|
||
today_map = {}
|
||
for r in today_records_q:
|
||
if r.product_id not in today_map:
|
||
today_map[r.product_id] = []
|
||
today_map[r.product_id].append(r)
|
||
|
||
# Final Assembly
|
||
unique_items = []
|
||
for r in latest_records:
|
||
pid = r.product_id
|
||
product = r.product
|
||
safe_product_url = normalize_momo_product_url(getattr(product, 'url', None), getattr(product, 'i_code', ''))
|
||
|
||
price_7d = prices_7d_ago_map.get(pid)
|
||
price_30d = prices_30d_ago_map.get(pid)
|
||
|
||
stats_7d_diff = r.price - price_7d if price_7d is not None else 0
|
||
stats_30d_diff = r.price - price_30d if price_30d is not None else 0
|
||
|
||
today_records = today_map.get(pid, [])
|
||
today_diff = 0
|
||
today_changes = []
|
||
if len(today_records) > 1:
|
||
today_diff = today_records[-1].price - today_records[0].price
|
||
|
||
y_price = yesterday_prices_map.get(pid)
|
||
yesterday_diff = r.price - y_price if y_price is not None else 0
|
||
|
||
status = "NONE"
|
||
if yesterday_diff > 0:
|
||
status = "PRICE_UP"
|
||
elif yesterday_diff < 0:
|
||
status = "PRICE_DOWN"
|
||
|
||
last_p = y_price if y_price is not None else (today_records[0].price if today_records else r.price)
|
||
for tr in today_records:
|
||
if tr.price != last_p:
|
||
diff = tr.price - last_p
|
||
today_changes.append({
|
||
'time': tr.timestamp.strftime('%H:%M'),
|
||
'price': tr.price,
|
||
'diff': diff
|
||
})
|
||
last_p = tr.price
|
||
|
||
unique_items.append({
|
||
'record': r,
|
||
'safe_product_url': safe_product_url or _build_momo_product_url(getattr(product, 'i_code', '')),
|
||
'stats': {'7d_diff': stats_7d_diff, '30d_diff': stats_30d_diff, '1d_diff': today_diff},
|
||
'yesterday_diff': yesterday_diff,
|
||
'today_changes': today_changes,
|
||
'status': status
|
||
})
|
||
|
||
# 更新快取
|
||
_DASHBOARD_DATA_CACHE['consolidated_data'] = unique_items
|
||
_DASHBOARD_DATA_CACHE['consolidated_timestamp'] = now.timestamp()
|
||
_DASHBOARD_DATA_CACHE['today_start'] = today_start
|
||
|
||
query_duration_ms = (time.time() - query_start_time) * 1000
|
||
track_query_time('get_consolidated_data', query_duration_ms)
|
||
sys_log.debug(f"[Dashboard] [Cache] 快取已更新 | 商品數: {len(unique_items)} | 耗時: {query_duration_ms:.0f}ms")
|
||
|
||
return unique_items, today_start
|
||
finally:
|
||
session.close()
|
||
|
||
|
||
def get_full_dashboard_data(force_rebuild=False):
|
||
"""獲取完整的看板資料，包含快取清單與全部 KPIs (深度快取)"""
|
||
global _DASHBOARD_DATA_CACHE
|
||
now = datetime.now(TAIPEI_TZ)
|
||
|
||
# V-Opt: 先檢查快取（無需鎖）
|
||
if not force_rebuild and _DASHBOARD_DATA_CACHE.get('full_data') and _DASHBOARD_DATA_CACHE.get('full_timestamp'):
|
||
age = now.timestamp() - _DASHBOARD_DATA_CACHE['full_timestamp']
|
||
if age < _DASHBOARD_CACHE_TTL:
|
||
sys_log.debug(f"[Dashboard] [Cache] ✅ 使用完整看板快取 | 快取年齡: {age:.0f}秒")
|
||
return _DASHBOARD_DATA_CACHE['full_data']
|
||
|
||
shared_full_data = None if force_rebuild else _load_shared_full_dashboard_cache(now)
|
||
if shared_full_data:
|
||
return shared_full_data
|
||
|
||
expired_shared_data = None if force_rebuild else _load_expired_shared_full_dashboard_cache(now)
|
||
if expired_shared_data:
|
||
_trigger_dashboard_background_refresh('expired_shared_cache')
|
||
return expired_shared_data
|
||
|
||
# V-Opt: 使用檔案鎖避免多 gunicorn worker 同時計算
|
||
dashboard_lock = _new_dashboard_file_lock()
|
||
lock_acquired = dashboard_lock.acquire(blocking=False)
|
||
if not lock_acquired:
|
||
# 其他 worker 正在重建時，先用舊快取救首屏，避免使用者第一次打開卡住。
|
||
stale_full_data = None if force_rebuild else _load_stale_full_dashboard_cache(now)
|
||
if stale_full_data:
|
||
return stale_full_data
|
||
|
||
sys_log.info("[Dashboard] [Cache] ⏳ 等待其他 worker 重建快取...")
|
||
deadline = time.time() + 5
|
||
while time.time() < deadline:
|
||
time.sleep(0.25)
|
||
shared_full_data = None if force_rebuild else _load_shared_full_dashboard_cache(now)
|
||
if shared_full_data:
|
||
return shared_full_data
|
||
dashboard_lock = _new_dashboard_file_lock()
|
||
lock_acquired = dashboard_lock.acquire(blocking=False)
|
||
if lock_acquired:
|
||
break
|
||
|
||
shared_full_data = None if force_rebuild else _load_shared_full_dashboard_cache(now)
|
||
if shared_full_data:
|
||
return shared_full_data
|
||
if not lock_acquired:
|
||
stale_full_data = None if force_rebuild else _load_stale_full_dashboard_cache(now)
|
||
if stale_full_data:
|
||
return stale_full_data
|
||
dashboard_lock = _new_dashboard_file_lock()
|
||
lock_acquired = dashboard_lock.acquire(blocking=True)
|
||
|
||
try:
|
||
# 再次檢查快取（可能其他 worker 已經更新）
|
||
if not force_rebuild and _DASHBOARD_DATA_CACHE.get('full_data') and _DASHBOARD_DATA_CACHE.get('full_timestamp'):
|
||
age = now.timestamp() - _DASHBOARD_DATA_CACHE['full_timestamp']
|
||
if age < _DASHBOARD_CACHE_TTL:
|
||
sys_log.debug(f"[Dashboard] [Cache] ✅ 使用完整看板快取 (其他 worker 已更新) | 快取年齡: {age:.0f}秒")
|
||
return _DASHBOARD_DATA_CACHE['full_data']
|
||
|
||
shared_full_data = None if force_rebuild else _load_shared_full_dashboard_cache(now)
|
||
if shared_full_data:
|
||
return shared_full_data
|
||
|
||
sys_log.info("[Dashboard] [Cache] 🔄 完整快取過期，重新計算所有 KPIs 與統計數據...")
|
||
query_start_time = time.time()
|
||
|
||
unique_items, today_start = get_consolidated_data()
|
||
today_start_db = today_start # 保持台北時區
|
||
|
||
db = DatabaseManager()
|
||
session = db.get_session()
|
||
|
||
try:
|
||
# A. 基礎清單統計
|
||
increase_items = [item for item in unique_items if item['yesterday_diff'] > 0]
|
||
decrease_items = [item for item in unique_items if item['yesterday_diff'] < 0]
|
||
|
||
# B. 分類筆數統計
|
||
cat_counts = {}
|
||
for item in unique_items:
|
||
c = item['record'].product.category
|
||
if c:
|
||
cat_counts[c] = cat_counts.get(c, 0) + 1
|
||
all_categories = [f"{cat} ({count}筆)" for cat, count in sorted(cat_counts.items())]
|
||
|
||
# C. 核心 KPI 統計
|
||
total_products_history = session.query(Product).count()
|
||
total_price_records = session.query(PriceRecord).count()
|
||
today_updates = session.query(PriceRecord).filter(PriceRecord.timestamp >= today_start_db).count()
|
||
|
||
# 今日新增商品
|
||
new_pids_query = session.query(PriceRecord.product_id).group_by(
|
||
PriceRecord.product_id
|
||
).having(func.min(PriceRecord.timestamp) >= today_start_db)
|
||
new_product_ids = {r[0] for r in new_pids_query.all()}
|
||
today_new_products = len(new_product_ids)
|
||
|
||
# D. 今日下架商品處理
|
||
raw_delisted_items = session.query(Product).filter(
|
||
Product.status == 'INACTIVE',
|
||
Product.updated_at >= today_start_db
|
||
).all()
|
||
|
||
today_delisted_items = []
|
||
if raw_delisted_items:
|
||
delisted_ids = [p.id for p in raw_delisted_items]
|
||
last_prices_subq = session.query(
|
||
PriceRecord.product_id,
|
||
func.max(PriceRecord.id).label('max_id')
|
||
).filter(PriceRecord.product_id.in_(delisted_ids)).group_by(PriceRecord.product_id).subquery()
|
||
|
||
last_prices_q = session.query(PriceRecord.product_id, PriceRecord.price).join(
|
||
last_prices_subq, PriceRecord.id == last_prices_subq.c.max_id).all()
|
||
price_map = {pid: price for pid, price in last_prices_q}
|
||
|
||
for p in raw_delisted_items:
|
||
today_delisted_items.append({'product': p, 'last_price': price_map.get(p.id, 0)})
|
||
|
||
# E. 週增長
|
||
week_ago_db = now.replace(hour=0, minute=0, second=0, microsecond=0) - timedelta(days=7)
|
||
week_new_products = session.query(func.count(Product.id)).filter(
|
||
Product.id.in_(
|
||
session.query(PriceRecord.product_id)
|
||
.group_by(PriceRecord.product_id)
|
||
.having(func.min(PriceRecord.timestamp) >= week_ago_db)
|
||
)
|
||
).scalar() or 0
|
||
|
||
# F. 價格穩定商品數
|
||
try:
|
||
stable_count = session.query(PriceRecord.product_id).filter(
|
||
PriceRecord.timestamp >= week_ago_db
|
||
).group_by(PriceRecord.product_id).having(
|
||
func.count(func.distinct(PriceRecord.price)) == 1
|
||
).count()
|
||
except Exception:
|
||
stable_count = 0
|
||
|
||
# G. 最大變動計算
|
||
max_change_item = None
|
||
max_change_value = 0
|
||
for item in unique_items:
|
||
if abs(item['yesterday_diff']) > abs(max_change_value):
|
||
max_change_value = item['yesterday_diff']
|
||
max_change_item = item
|
||
|
||
# H. 最活躍分類
|
||
category_activity = {}
|
||
for item in increase_items + decrease_items:
|
||
cat = item['record'].product.category
|
||
if cat:
|
||
category_activity[cat] = category_activity.get(cat, 0) + 1
|
||
most_active_category_item = max(category_activity.items(), key=lambda x: x[1]) if category_activity else (None, 0)
|
||
|
||
# I. 組合結果
|
||
full_data = {
|
||
'unique_items': unique_items,
|
||
'today_start': today_start,
|
||
'today_start_db': today_start_db,
|
||
'increase_items_all': increase_items,
|
||
'decrease_items_all': decrease_items,
|
||
'all_categories': all_categories,
|
||
'new_product_ids': new_product_ids,
|
||
'total_products_history': total_products_history,
|
||
'total_price_records': total_price_records,
|
||
'today_updates': today_updates,
|
||
'today_new_products': today_new_products,
|
||
'today_delisted_count': len(raw_delisted_items),
|
||
'today_delisted_items': today_delisted_items,
|
||
'max_change_item': max_change_item,
|
||
'max_change_value': max_change_value,
|
||
'avg_increase': sum(item['yesterday_diff'] for item in increase_items) / len(increase_items) if increase_items else 0,
|
||
'avg_decrease': sum(item['yesterday_diff'] for item in decrease_items) / len(decrease_items) if decrease_items else 0,
|
||
'activity_rate': (len(increase_items) + len(decrease_items)) / total_products_history * 100 if total_products_history > 0 else 0,
|
||
'active_count': len(increase_items) + len(decrease_items),
|
||
'week_new_products': week_new_products,
|
||
'stable_count': stable_count,
|
||
'most_active_category': most_active_category_item[0],
|
||
'most_active_count': most_active_category_item[1]
|
||
}
|
||
full_data['competitor_overview'] = _load_competitor_decision_overview(session, unique_items)
|
||
|
||
# 更新快取
|
||
_DASHBOARD_DATA_CACHE['full_data'] = full_data
|
||
_DASHBOARD_DATA_CACHE['full_timestamp'] = now.timestamp()
|
||
_write_shared_full_dashboard_cache(full_data)
|
||
|
||
query_duration_ms = (time.time() - query_start_time) * 1000
|
||
track_query_time('get_full_dashboard_data', query_duration_ms)
|
||
sys_log.info(f"[Dashboard] [Cache] ✅ 完整看板快取已更新 | 耗時: {query_duration_ms:.0f}ms")
|
||
|
||
return full_data
|
||
except Exception as e:
|
||
sys_log.error(f"[Dashboard] KPI 計算失敗: {e}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
return None
|
||
finally:
|
||
session.close()
|
||
finally:
|
||
# V-Opt: 確保釋放檔案鎖
|
||
if lock_acquired:
|
||
dashboard_lock.release()
|
||
|
||
|
||
def get_dashboard_stats():
|
||
"""計算看板統計數據 (供通知使用) — backward-compat wrapper"""
|
||
from services.dashboard_service import get_dashboard_stats as _get_dashboard_stats
|
||
return _get_dashboard_stats()
|
||
|
||
|
||
# ==========================================
|
||
# 頁面路由
|
||
# ==========================================
|
||
|
||
@dashboard_bp.route('/')
|
||
@login_required
|
||
def index():
|
||
"""商品看板首頁"""
|
||
db = DatabaseManager()
|
||
session = db.get_session()
|
||
page = request.args.get('page', 1, type=int)
|
||
category_filter = request.args.get('category', 'all')
|
||
sort_by = request.args.get('sort_by', 'timestamp')
|
||
filter_type = request.args.get('filter', 'all')
|
||
order = request.args.get('order', 'desc')
|
||
search_query = request.args.get('q', '').strip()
|
||
per_page = 50
|
||
|
||
now_taipei = datetime.now(TAIPEI_TZ)
|
||
today_start_db = now_taipei.replace(hour=0, minute=0, second=0, microsecond=0) # 保持台北時區
|
||
|
||
try:
|
||
# 使用深度快取獲取所有數據
|
||
data = get_full_dashboard_data()
|
||
if not data:
|
||
return render_template('index.html', error="無法載入數據，請檢查資料庫。")
|
||
|
||
unique_items = data['unique_items']
|
||
today_start = data['today_start']
|
||
today_start_db = data['today_start_db']
|
||
increase_items = data['increase_items_all']
|
||
decrease_items = data['decrease_items_all']
|
||
all_categories = data['all_categories']
|
||
new_product_ids = data['new_product_ids']
|
||
total_products_history = data['total_products_history']
|
||
today_new_products = data['today_new_products']
|
||
total_price_records = data['total_price_records']
|
||
today_updates = data['today_updates']
|
||
today_delisted_count = data['today_delisted_count']
|
||
today_delisted_items = data['today_delisted_items']
|
||
max_change_item = data['max_change_item']
|
||
max_change_value = data['max_change_value']
|
||
avg_increase = data['avg_increase']
|
||
avg_decrease = data['avg_decrease']
|
||
activity_rate = data['activity_rate']
|
||
week_new_products = data['week_new_products']
|
||
stable_count = data['stable_count']
|
||
most_active_category = data['most_active_category']
|
||
most_active_count = data['most_active_count']
|
||
active_count = data.get('active_count', 0)
|
||
|
||
# 讀取系統狀態
|
||
system_status = {"status": "UNKNOWN", "message": "尚無執行紀錄", "timestamp": "-"}
|
||
status_path = os.path.join(BASE_DIR, 'data/system_status.json')
|
||
if os.path.exists(status_path):
|
||
try:
|
||
with open(status_path, 'r', encoding='utf-8') as f:
|
||
system_status = json.load(f)
|
||
except:
|
||
pass
|
||
|
||
# 後端篩選
|
||
scheduler_stats = load_scheduler_stats()
|
||
|
||
# Handle old scheduler stats format
|
||
if scheduler_stats.get('momo_task') and isinstance(scheduler_stats.get('momo_task'), dict):
|
||
scheduler_stats['momo_task'] = [scheduler_stats['momo_task']]
|
||
if scheduler_stats.get('edm_task') and isinstance(scheduler_stats.get('edm_task'), dict):
|
||
scheduler_stats['edm_task'] = [scheduler_stats['edm_task']]
|
||
|
||
filtered_items = []
|
||
ai_pick_skus = []
|
||
ai_pick_map = {}
|
||
ai_pick_summary = None
|
||
if filter_type == 'ai_picks':
|
||
ai_pick_skus, ai_pick_map = _load_ai_pick_selection(session, PRODUCT_PICK_LIST_LIMIT)
|
||
ai_pick_summary = _summarize_ai_pick_selection(ai_pick_map)
|
||
|
||
# 先處理搜尋
|
||
if search_query:
|
||
search_lower = search_query.lower()
|
||
base_items = [
|
||
item for item in unique_items
|
||
if (item['record'].product.name and search_lower in item['record'].product.name.lower()) or
|
||
(item['record'].product.i_code and search_lower in str(item['record'].product.i_code))
|
||
]
|
||
else:
|
||
base_items = unique_items
|
||
|
||
# 處理狀態篩選
|
||
if filter_type == 'increase':
|
||
filtered_items = [i for i in base_items if i in increase_items]
|
||
elif filter_type == 'decrease':
|
||
filtered_items = [i for i in base_items if i in decrease_items]
|
||
elif filter_type == 'new':
|
||
filtered_items = [i for i in base_items if i['record'].product_id in new_product_ids]
|
||
elif filter_type == 'ai_picks':
|
||
pick_set = set(ai_pick_skus)
|
||
filtered_items = [
|
||
i for i in base_items
|
||
if str(i['record'].product.i_code) in pick_set
|
||
]
|
||
elif filter_type == 'delisted':
|
||
for item in today_delisted_items:
|
||
class DelistedRecord:
|
||
def __init__(self, p, price):
|
||
self.product = p
|
||
self.price = price
|
||
self.timestamp = p.updated_at
|
||
|
||
if not search_query or search_query.lower() in item['product'].name.lower():
|
||
filtered_items.append({
|
||
'record': DelistedRecord(item['product'], item['last_price']),
|
||
'stats': {'1d_diff': 0, '7d_diff': 0, '30d_diff': 0},
|
||
'yesterday_diff': 0,
|
||
'today_changes': [],
|
||
'status': 'DELISTED'
|
||
})
|
||
else:
|
||
if category_filter != 'all':
|
||
real_category = category_filter
|
||
if "(" in category_filter and "筆)" in category_filter:
|
||
real_category = category_filter.rsplit(" (", 1)[0]
|
||
filtered_items = [item for item in base_items if item['record'].product.category == real_category]
|
||
else:
|
||
filtered_items = base_items
|
||
|
||
# 後端排序
|
||
reverse = (order == 'desc')
|
||
|
||
def get_sort_key(item):
|
||
def safe_get(value, default=0):
|
||
return default if value is None else value
|
||
|
||
if sort_by == 'i_code':
|
||
return int(safe_get(item['record'].product.i_code, 0))
|
||
if sort_by == 'category':
|
||
return safe_get(item['record'].product.category, '')
|
||
if sort_by == 'name':
|
||
return safe_get(item['record'].product.name, '')
|
||
if sort_by == 'price':
|
||
return safe_get(item['record'].price, 0)
|
||
if sort_by == 'today_change':
|
||
return safe_get(item['stats']['1d_diff'], 0)
|
||
if sort_by == 'yesterday_change':
|
||
return safe_get(item['yesterday_diff'], 0)
|
||
if sort_by == 'week_change':
|
||
return safe_get(item['stats']['7d_diff'], 0)
|
||
if filter_type == 'ai_picks':
|
||
sku = str(item['record'].product.i_code)
|
||
return -ai_pick_map.get(sku, {}).get('rank', 9999)
|
||
return item['record'].timestamp
|
||
|
||
sorted_items = sorted(filtered_items, key=get_sort_key, reverse=reverse)
|
||
|
||
# 分頁
|
||
total_items = len(sorted_items)
|
||
total_pages = math.ceil(total_items / per_page)
|
||
|
||
start_idx = (page - 1) * per_page
|
||
paged_items = sorted_items[start_idx: start_idx + per_page]
|
||
|
||
# 為前端準備安全的 created_at 屬性
|
||
for item in paged_items:
|
||
item['safe_created_at'] = getattr(item['record'].product, 'created_at', None)
|
||
sku = str(item['record'].product.i_code)
|
||
item['ai_pick'] = ai_pick_map.get(sku)
|
||
item['safe_momo_url'] = (
|
||
item.get('safe_product_url')
|
||
or normalize_momo_product_url(item['record'].product.url, sku)
|
||
or _build_momo_product_url(sku)
|
||
)
|
||
|
||
# 為當前頁面項目添加顏色
|
||
for item in paged_items:
|
||
category_name = item['record'].product.category
|
||
item['category_color'] = get_color_for_string(category_name)
|
||
|
||
pchome_map = _load_pchome_competitor_map(
|
||
session,
|
||
[item['record'].product.i_code for item in paged_items]
|
||
)
|
||
pchome_attempt_map = _load_pchome_match_attempt_map(
|
||
session,
|
||
[item['record'].product.i_code for item in paged_items]
|
||
)
|
||
pchome_ineligible_map = _load_pchome_ineligible_competitor_map(
|
||
session,
|
||
[item['record'].product.i_code for item in paged_items]
|
||
)
|
||
for item in paged_items:
|
||
product = item['record'].product
|
||
sku = str(product.i_code)
|
||
competitor = pchome_map.get(sku)
|
||
attempt = pchome_attempt_map.get(sku)
|
||
ineligible = pchome_ineligible_map.get(sku)
|
||
match_status = _build_pchome_match_status(attempt, ineligible=ineligible)
|
||
item['pchome_competitor'] = competitor
|
||
item['pchome_match_attempt'] = attempt
|
||
item['pchome_ineligible_competitor'] = ineligible
|
||
item['pchome_match_status'] = match_status
|
||
item['competitor_decision'] = _build_competitor_decision(
|
||
item['record'].price,
|
||
competitor.get('price') if competitor else None,
|
||
match_status=match_status,
|
||
)
|
||
|
||
competitor_overview = data.get('competitor_overview')
|
||
if not competitor_overview:
|
||
competitor_overview = _load_competitor_decision_overview(session, unique_items)
|
||
data['competitor_overview'] = competitor_overview
|
||
_DASHBOARD_DATA_CACHE['full_data'] = data
|
||
_write_shared_full_dashboard_cache(data)
|
||
template_name = 'dashboard_v2.html'
|
||
|
||
return render_template(template_name,
|
||
total_products=total_products_history,
|
||
today_new_products=today_new_products,
|
||
total_price_records=total_price_records,
|
||
cnt_increase=len(increase_items),
|
||
cnt_decrease=len(decrease_items),
|
||
today_delisted_count=today_delisted_count,
|
||
today_delisted_items=today_delisted_items,
|
||
system_status=system_status,
|
||
items=paged_items,
|
||
categories=all_categories,
|
||
current_page=page,
|
||
total_pages=total_pages,
|
||
total_items=total_items,
|
||
datetime_now=now_taipei.strftime('%Y-%m-%d %H:%M:%S'),
|
||
today_date=now_taipei.strftime('%Y-%m-%d'),
|
||
public_url=public_url,
|
||
current_category=category_filter,
|
||
current_filter=filter_type,
|
||
search_query=search_query,
|
||
current_sort=sort_by,
|
||
current_order=order,
|
||
ai_pick_summary=ai_pick_summary,
|
||
scheduler_stats=scheduler_stats,
|
||
avg_increase=avg_increase,
|
||
avg_decrease=avg_decrease,
|
||
activity_rate=activity_rate,
|
||
active_count=active_count,
|
||
max_change_item=max_change_item,
|
||
max_change_value=max_change_value,
|
||
week_new_products=week_new_products,
|
||
stable_count=stable_count,
|
||
most_active_category=most_active_category,
|
||
most_active_count=most_active_count,
|
||
competitor_overview=competitor_overview,
|
||
ai_pick_list_limit=PRODUCT_PICK_LIST_LIMIT,
|
||
active_page='dashboard')
|
||
except Exception as e:
|
||
sys_log.error(f"[Web] [Dashboard] 渲染錯誤 | Error: {e}")
|
||
return f"系統維護中，錯誤詳情：{e}"
|
||
finally:
|
||
session.close()
|