diff --git a/app.py b/app.py index 8b3fece..b31ece5 100644 --- a/app.py +++ b/app.py @@ -1161,2266 +1161,21 @@ def _get_filtered_sales_data(cache_key): return target_df, cols_map, None -@app.route('/sales_analysis') -def sales_analysis(): - """業績分析報表頁面""" - try: - db = DatabaseManager() - table_name = 'realtime_sales_monthly' - - # 1. 檢查資料表是否存在 - inspector = inspect(db.engine) - if table_name not in inspector.get_table_names(): - return render_template('sales_analysis.html', - error="尚未匯入「即時業績(全月)」資料，請先至設定頁面匯入 Excel。", - table_name=table_name, - selected_metric='amount', - no_filter=False, - data_range_months=0, - start_date='', - end_date='', - total_records=0, - db_data_range='') - - # V-New: 查詢資料庫的資料期間範圍 - db_data_range = '' - try: - # 取得日期欄位的最小值和最大值 - from sqlalchemy import text - date_query = text(f"SELECT MIN(日期) as min_date, MAX(日期) as max_date FROM {table_name}") - # V-Fix: SQLAlchemy 2.0 需要使用 connection 對象 - with db.engine.connect() as conn: - result = conn.execute(date_query).fetchone() - if result and result[0] and result[1]: - min_date = result[0] - max_date = result[1] - # 格式化為 YYYY年MM月 格式 - if isinstance(min_date, str): - from datetime import datetime - try: - min_date_obj = datetime.strptime(min_date.split()[0], '%Y-%m-%d') - max_date_obj = datetime.strptime(max_date.split()[0], '%Y-%m-%d') - db_data_range = f"{min_date_obj.year}年{min_date_obj.month}月 ~ {max_date_obj.year}年{max_date_obj.month}月" - except: - db_data_range = f"{min_date} ~ {max_date}" - else: - db_data_range = f"{min_date.year}年{min_date.month}月 ~ {max_date.year}年{max_date.month}月" - except Exception as e: - sys_log.warning(f"[Sales Analysis] 無法取得資料期間範圍: {e}") - - # V-New: 取得篩選參數 - data_range_param = request.args.get('data_range', '') # 不再設預設值 - start_date = request.args.get('start_date', '') - end_date = request.args.get('end_date', '') - - # V-New: 按需載入 - 如果沒有任何篩選條件，顯示引導頁面 - if not data_range_param and not start_date and not end_date: - sys_log.info("[Sales Analysis] 👋 首次進入頁面，等待用戶選擇篩選條件") - - # V-Fix: 即使在引導頁面，也要提供下拉選單選項 - # V-Opt: 讀取較多筆數（1000筆）以獲得更完整的月份資訊 - preview_df = pd.read_sql(f"SELECT * FROM {table_name} LIMIT 1000", db.engine) - preview_categories = [] - preview_brands = [] - preview_vendors = [] - preview_activities = [] - preview_payments = [] - preview_months = [] # V-New: 新增月份列表 - - if not preview_df.empty: - cols = preview_df.columns.tolist() - def find_col(keywords): - for k in keywords: - for col in cols: - if k in str(col): return col - return None - - col_category = find_col(['館別', '商品館', '分類', 'Category']) - col_brand = find_col(['品牌', 'Brand']) - col_vendor = find_col(['廠商名稱', 'Vendor Name', '廠商', '供應商', 'Vendor', 'Supplier']) - # V-Fix: 優先匹配具體的活動欄位名稱 - col_activity = find_col(['折扣活動名稱', '折價券活動名稱', '滿額再折扣活動名稱', '活動', 'Activity', 'Campaign']) - col_payment = find_col(['付款', 'Payment', 'Pay']) - # V-Fix: 優先匹配「日期」欄位（「訂單日期」是固定文字，不是實際日期） - col_date_part = find_col(['日期', '交易日期', 'Date', 'Day']) - col_time_part = find_col(['訂單時間', '成立時間', '下單時間', '購買時間', '時間', 'Time', 'Created']) - - # V-Fix: 篩選掉空字串，只保留有效數據 - if col_category: - preview_categories = sorted([x for x in preview_df[col_category].dropna().astype(str).unique().tolist() if x and x.strip()]) - if col_brand: - preview_brands = sorted([x for x in preview_df[col_brand].dropna().astype(str).unique().tolist() if x and x.strip()]) - if col_vendor: - preview_vendors = sorted([x for x in preview_df[col_vendor].dropna().astype(str).unique().tolist() if x and x.strip()]) - if col_activity: - preview_activities = sorted([x for x in preview_df[col_activity].dropna().astype(str).unique().tolist() if x and x.strip()]) - if col_payment: - preview_payments = sorted([x for x in preview_df[col_payment].dropna().astype(str).unique().tolist() if x and x.strip()]) - - # V-Fix: 從數據庫直接查詢所有月份（而不是從預覽數據提取，避免只獲得部分月份） - if col_date_part: - try: - from sqlalchemy import text - with db.engine.connect() as conn: - result = conn.execute(text(f""" - SELECT DISTINCT replace(substr("{col_date_part}", 1, 7), '/', '-') as month - FROM {table_name} - WHERE "{col_date_part}" IS NOT NULL AND "{col_date_part}" != '' - ORDER BY month - """)).fetchall() - preview_months = [row[0] for row in result if row[0] and '-' in str(row[0])] - sys_log.info(f"[Sales Analysis] 預覽模式從「{col_date_part}」欄位提取到 {len(preview_months)} 個月份") - except Exception as e: - sys_log.warning(f"[Sales Analysis] 無法從「{col_date_part}」欄位提取月份: {e}") - pass - - # 傳遞必要的變數以避免模板錯誤 - selected_metric = request.args.get('metric', 'amount') - # 建立空的數據結構 - empty_data = {'labels': [], 'chart_values': [], 'values': [], 'metric_label': ''} - return render_template('sales_analysis.html', - no_filter=True, - table_name=table_name, - selected_metric=selected_metric, - total_records=0, - items=[], - kpi={'revenue': 0, 'qty': 0, 'count': 0, 'cost': 0, 'gross_margin': 0, 'gross_margin_rate': 0, 'avg_price': 0}, - insights={}, - abc_stats={}, - vendor_stats=[], - seasonality_data={'datasets': [], 'yLabels': [], 'xLabels': []}, - bar_data=empty_data, - cat_data=empty_data, - price_dist_data=empty_data, - scatter_data=[], - bcg_data=[], - dow_data=empty_data, - hourly_data=empty_data, - monthly_data=empty_data, - weekly_data=empty_data, - heatmap_data=[], - treemap_data=[], - cols={'name': True, 'amount': True, 'qty': True, 'cat': True, 'date': True, - 'cost': True, 'profit': True, 'vendor': True, 'brand': True, - 'return_qty': True, 'pid': True}, - all_categories=preview_categories, all_brands=preview_brands, all_vendors=preview_vendors, - all_activities=preview_activities, all_payments=preview_payments, all_months=preview_months, - selected_category='all', selected_brand='all', selected_vendor='all', - selected_activity='all', selected_payment='all', selected_dow='all', - selected_hour='all', selected_month='all', - keyword='', min_price='', max_price='', min_margin='', max_margin='', - data_range_months=0, start_date='', end_date='', - db_data_range=db_data_range, - marketing_data=None) - - # 解析 data_range_months（有篩選時才處理） - data_range_months = int(data_range_param or '0') - - # V-New: 如果有自訂日期區間，則優先使用 - if start_date or end_date: - cache_key = f"{table_name}_custom_{start_date}_{end_date}" - else: - cache_key = f"{table_name}_{data_range_months}m" - - # 2. 讀取與處理資料 (V-Opt: 使用二級快取機制 Raw -> Processed) - df = None - cols_map = {} - - # A. 優先檢查是否已有處理好的快取 (最快) - if cache_key in _SALES_PROCESSED_CACHE: - cache_data = _SALES_PROCESSED_CACHE[cache_key] - df = cache_data['df'] - cols_map = cache_data['cols'] - - # 恢復欄位變數 - col_name = cols_map.get('name') - col_date = cols_map.get('date') - col_amount = cols_map.get('amount') - col_qty = cols_map.get('qty') - col_category = cols_map.get('category') - col_brand = cols_map.get('brand') - col_vendor = cols_map.get('vendor') - col_activity = cols_map.get('activity') - col_payment = cols_map.get('payment') - col_pid = cols_map.get('pid') # V-New: 取得 PID 欄位 - col_price = cols_map.get('price') - col_cost = cols_map.get('cost') - col_profit = cols_map.get('profit') - col_return_qty = cols_map.get('return_qty') - - cached_pie_data = cache_data.get('pie_data', {'labels': [], 'chart_values': []}) # V-Opt: 讀取圓餅圖快取 - else: - # B. 若無處理後快取，則從 Raw Cache 或 DB 讀取並處理 - # V-Opt: 加入日期範圍篩選以減少記憶體使用 - # (data_range_months 已在上方定義) - - # 先讀取小樣本以識別日期欄位 - sample_df = pd.read_sql(f"SELECT * FROM {table_name} LIMIT 100", db.engine) - if sample_df.empty: - return render_template('sales_analysis.html', - error="資料表為空，請重新匯入。", - table_name=table_name, - selected_metric=request.args.get('metric', 'amount'), - no_filter=False, - data_range_months=data_range_months, - start_date=start_date, - end_date=end_date, - total_records=0, - db_data_range=db_data_range, - marketing_data=None) - - # 自動識別日期欄位（V-Fix: 優先匹配「日期」，因為「訂單日期」可能是固定文字） - sample_cols = sample_df.columns.tolist() - date_col_name = None - for col in sample_cols: - if any(keyword in str(col) for keyword in ['日期', '交易日期', 'Date', '訂單時間', '成立時間', '下單時間', '購買時間', '時間', 'Time', 'Created']): - date_col_name = col - break - - # 根據是否有日期欄位決定查詢方式 - if date_col_name: - from datetime import datetime, timedelta, timezone - TAIPEI_TZ = timezone(timedelta(hours=8)) - - # V-New: 優先處理自訂日期區間 - if start_date or end_date: - # V-Fix: 處理日期格式轉換 (2025-01-01 -> 2025/01/01) - start_date_slash = start_date.replace('-', '/') if start_date else '' - end_date_slash = end_date.replace('-', '/') if end_date else '' - - # 有自訂日期區間 - 使用 BETWEEN 或單邊範圍 - if start_date and end_date: - sql_query = f"SELECT * FROM {table_name} WHERE \"{date_col_name}\" BETWEEN '{start_date_slash}' AND '{end_date_slash}'" - sys_log.info(f"[Sales Analysis] 📅 使用自訂日期範圍: {start_date} ~ {end_date} (DB格式: {start_date_slash} ~ {end_date_slash})") - elif start_date: - sql_query = f"SELECT * FROM {table_name} WHERE \"{date_col_name}\" >= '{start_date_slash}'" - sys_log.info(f"[Sales Analysis] 📅 使用自訂開始日期: >= {start_date} (DB格式: {start_date_slash})") - else: # only end_date - sql_query = f"SELECT * FROM {table_name} WHERE \"{date_col_name}\" <= '{end_date_slash}'" - sys_log.info(f"[Sales Analysis] 📅 使用自訂結束日期: <= {end_date} (DB格式: {end_date_slash})") - elif data_range_months > 0: - # 使用相對日期範圍（最近N個月） - # V-Fix: 使用斜線格式以匹配資料庫格式 - cutoff_date = (datetime.now(TAIPEI_TZ) - timedelta(days=data_range_months * 30)).strftime('%Y/%m/%d') - sql_query = f"SELECT * FROM {table_name} WHERE \"{date_col_name}\" >= '{cutoff_date}'" - sys_log.info(f"[Sales Analysis] 📊 使用日期範圍篩選: 最近 {data_range_months} 個月 (>= {cutoff_date})") - else: - # data_range_months == 0，載入全部資料 - sql_query = f"SELECT * FROM {table_name}" - sys_log.info(f"[Sales Analysis] 📊 載入全部資料（用戶選擇）") - else: - # 無日期欄位 - 載入全部 - sql_query = f"SELECT * FROM {table_name}" - sys_log.info(f"[Sales Analysis] ⚠️ 未找到日期欄位，載入全部資料") - - # V-Opt (2026-01-23): 優先使用 PostgreSQL 聚合視圖 (mv_sales_summary) - # 聚合視圖已預先計算：資料量 -20%, 大小 -73%, 欄位類型已轉換 - from sqlalchemy import text as sql_text - from config import DATABASE_TYPE - - use_materialized_view = False - if DATABASE_TYPE == 'postgresql': - # 檢查聚合視圖是否存在 - try: - with db.engine.connect() as conn: - check_mv = conn.execute(sql_text( - "SELECT EXISTS (SELECT 1 FROM pg_matviews WHERE matviewname = 'mv_sales_summary')" - )).fetchone() - use_materialized_view = check_mv[0] if check_mv else False - except: - use_materialized_view = False - - if use_materialized_view: - # 使用聚合視圖 - 欄位已標準化為英文 - sys_log.info(f"[Sales Analysis] 📊 使用 PostgreSQL 聚合視圖 (mv_sales_summary)") - - # 構建日期篩選條件 - mv_where = "" - if start_date or end_date: - if start_date and end_date: - mv_where = f"WHERE sale_date BETWEEN '{start_date}' AND '{end_date}'" - elif start_date: - mv_where = f"WHERE sale_date >= '{start_date}'" - else: - mv_where = f"WHERE sale_date <= '{end_date}'" - elif data_range_months > 0: - from datetime import datetime, timedelta, timezone - TAIPEI_TZ = timezone(timedelta(hours=8)) - cutoff = (datetime.now(TAIPEI_TZ) - timedelta(days=data_range_months * 30)).strftime('%Y-%m-%d') - mv_where = f"WHERE sale_date >= '{cutoff}'" - - mv_query = f""" - SELECT - sale_date as "日期", - product_id as "商品ID", - product_name as "商品名稱", - category as "商品館", - brand as "品牌", - vendor_name as "廠商名稱", - payment as "付款", - total_revenue as "總業績", - total_qty as "數量", - total_cost as "總成本", - order_count - FROM mv_sales_summary - {mv_where} - """ - df = pd.read_sql(mv_query, db.engine) - sys_log.info(f"[Sales Analysis] 📊 聚合視圖載入完成: {len(df):,} 筆記錄") - else: - # 原始邏輯：使用原始表 - sys_log.info(f"[Sales Analysis] 📊 使用原始表載入...") - df = pd.read_sql(sql_query, db.engine) - sys_log.info(f"[Sales Analysis] 📊 載入完成: {len(df):,} 筆記錄") - - # 聚合模式標記 - is_aggregated_mode = use_materialized_view - - # V-Opt: 不再快取完整 DataFrame 到 _SALES_DF_CACHE (避免記憶體累積) - # 改用輕量級處理後快取 (_SALES_PROCESSED_CACHE) - - if df.empty: - return render_template('sales_analysis.html', - error="資料表為空，請重新匯入。", - table_name=table_name, - selected_metric=request.args.get('metric', 'amount'), - no_filter=False, - data_range_months=data_range_months, - start_date=start_date, - end_date=end_date, - total_records=0, - db_data_range=db_data_range, - marketing_data=None) - - # 3. 自動識別關鍵欄位 (模糊比對) - cols = df.columns.tolist() - def find_col(keywords): - # V-Opt: 改為優先遍歷關鍵字，確保優先匹配更精確的名稱 (例如 '廠商名稱' 優於 '廠商') - for k in keywords: - for col in cols: - if k in str(col): return col - return None - - col_name = find_col(['商品名稱', '品名', 'Name', 'Product']) - col_pid = find_col(['商品ID', 'Product ID', 'ID', 'i_code', 'Item Code']) # V-New: 偵測商品ID欄位 - - # V-Fix: 優先匹配「日期」欄位（「訂單日期」是固定文字，不是實際日期） - col_date_part = find_col(['日期', '交易日期', 'Date', 'Day']) - col_time_part = find_col(['訂單時間', '成立時間', '下單時間', '購買時間', '時間', 'Time', 'Created']) - - col_brand = find_col(['品牌', 'Brand']) # V-New: 品牌欄位 - col_vendor = find_col(['廠商名稱', 'Vendor Name', '廠商', '供應商', 'Vendor', 'Supplier']) # V-Opt: 優先抓取名稱 - col_activity = find_col(['活動', '折扣', 'Activity', 'Campaign', 'Promotion', '專案']) # V-New: 活動欄位 - col_payment = find_col(['付款方式', 'Payment', 'Pay']) # V-New: 付款方式欄位 - col_price = find_col(['單價', 'Price', '價格', 'Avg Price']) # V-New: 嘗試尋找單價欄位 - col_cost = find_col(['成本', 'Cost', '進價', 'Cost Price', 'Wholesale']) # V-New: 成本欄位 - col_profit = find_col(['毛利', 'Profit', '利潤']) # V-New: 直接尋找毛利欄位 (若有) - col_return_qty = find_col(['退貨數量', 'Return Qty', '退貨']) # V-New: 退貨欄位 - col_amount = find_col(['銷售金額', '業績', '金額', 'Amount', 'Sales', 'Total']) - col_qty = find_col(['銷售數量', '銷量', '數量', 'Qty', 'Quantity']) - col_category = find_col(['館別', '分類', 'Category']) - - if not col_name or not col_amount: - return render_template('sales_analysis.html', - error=f"無法自動識別關鍵欄位 (需包含 '名稱' 與 '金額')。偵測到的欄位: {cols}", - table_name=table_name, - selected_metric=request.args.get('metric', 'amount'), - no_filter=False, - data_range_months=data_range_months, - start_date=start_date, - end_date=end_date, - total_records=0, - db_data_range=db_data_range, - marketing_data=None) - - # 4. 資料處理 (Heavy Lifting - 只在快取建立時執行一次) - # 確保金額與數量是數字 - df[col_amount] = pd.to_numeric(df[col_amount], errors='coerce').fillna(0) - if col_qty: - df[col_qty] = pd.to_numeric(df[col_qty], errors='coerce').fillna(0) - - if col_cost: - df[col_cost] = pd.to_numeric(df[col_cost], errors='coerce').fillna(0) - - if col_profit: - df[col_profit] = pd.to_numeric(df[col_profit], errors='coerce').fillna(0) - - if col_return_qty: - df[col_return_qty] = pd.to_numeric(df[col_return_qty], errors='coerce').fillna(0) - - # V-Fix: 智慧日期時間合併邏輯 (聚合模式下跳過) - col_date = None - if not is_aggregated_mode: - if col_date_part and col_time_part: - # 兩者都有，嘗試合併 - try: - df['combined_dt'] = pd.to_datetime(df[col_date_part].astype(str) + ' ' + df[col_time_part].astype(str), errors='coerce') - col_date = 'combined_dt' - except: - # 合併失敗，退回使用時間欄位 (假設包含日期) 或日期欄位 - col_date = col_time_part or col_date_part - elif col_time_part: - # 只有時間欄位 (可能包含日期) - df[col_time_part] = pd.to_datetime(df[col_time_part], errors='coerce') - col_date = col_time_part - elif col_date_part: - # 只有日期欄位 - df[col_date_part] = pd.to_datetime(df[col_date_part], errors='coerce') - col_date = col_date_part - - # V-New: 若無明確單價欄位，則自動計算 (金額 / 數量) - if not col_price and col_amount and col_qty: - col_price = 'calculated_price' - # V-Opt: 使用 numpy 向量化運算加速 (取代 apply) - df[col_price] = np.where(df[col_qty] > 0, df[col_amount] / df[col_qty], 0) - - if col_price: - df[col_price] = pd.to_numeric(df[col_price], errors='coerce').fillna(0) - - # V-New: 預先計算毛利率 (Margin Rate) 用於篩選 - # 邏輯: (毛利 / 金額) * 100 - col_margin_rate = 'calculated_margin_rate' - with np.errstate(divide='ignore', invalid='ignore'): - if col_profit: - df[col_margin_rate] = (df[col_profit] / df[col_amount]) * 100 - elif col_cost: - df[col_margin_rate] = ((df[col_amount] - df[col_cost]) / df[col_amount]) * 100 - else: - df[col_margin_rate] = 0.0 - # 處理無限大與 NaN (轉為 0) - df[col_margin_rate] = df[col_margin_rate].replace([np.inf, -np.inf, np.nan], 0) - - # === V-Opt: 效能優化預計算 (V9.98) === - # 1. 日期維度 (加速篩選與聚合，避免重複呼叫 .dt 存取器) - # 聚合模式下跳過日期維度計算 - if col_date and not is_aggregated_mode: - df['_dow'] = df[col_date].dt.dayofweek - df['_hour'] = df[col_date].dt.hour - df['_week'] = df[col_date].dt.strftime('%G-W%V') - df['_month_str'] = df[col_date].dt.strftime('%Y-%m') # V-New: 月份維度 (YYYY-MM) - - # 2. 毛利額 (加速 Top 3 分析，避免 runtime 計算) - if col_profit: - df['calculated_profit'] = df[col_profit] - elif col_cost: - df['calculated_profit'] = df[col_amount] - df[col_cost] - else: - df['calculated_profit'] = 0.0 - - # 3. 全站分類圓餅圖 (已移至下方使用 target_df 計算) - - - # 建立/更新處理後快取 - cache_entry = { - 'df': df, - 'cols': { - 'name': col_name, 'date': col_date, 'amount': col_amount, - 'qty': col_qty, 'category': col_category, 'brand': col_brand, - 'vendor': col_vendor, 'activity': col_activity, 'payment': col_payment, - 'price': col_price, 'cost': col_cost, 'profit': col_profit, - 'return_qty': col_return_qty, - 'pid': col_pid # V-New: 儲存商品ID欄位 - }, - 'pid': col_pid # V-New: 儲存商品ID欄位 - } - _SALES_PROCESSED_CACHE[cache_key] = cache_entry - # V-Fix: 同時使用固定的 table_name 作為 key 儲存副本，供其他路由使用 - _SALES_PROCESSED_CACHE[table_name] = cache_entry - # V-Opt (2026-01-23): 定期清理過期快取 - _cleanup_sales_cache() - - # 🚩 V-Opt: 使用共用篩選函式 - target_df, cols_map, err = _get_filtered_sales_data(cache_key) - if err: - # V-Fix: 若快取失效，重新導向自己以觸發重新讀取（保留所有查詢參數） - params = {k: v for k, v in request.args.items()} - return redirect(url_for('sales_analysis', **params)) - - # 重新取得變數 (因為 _get_filtered_sales_data 內部使用了 cols_map) - col_name = cols_map.get('name') - col_amount = cols_map.get('amount') - col_qty = cols_map.get('qty') - col_category = cols_map.get('category') - col_brand = cols_map.get('brand') - col_vendor = cols_map.get('vendor') - col_activity = cols_map.get('activity') - col_payment = cols_map.get('payment') - col_price = cols_map.get('price') - col_cost = cols_map.get('cost') - col_profit = cols_map.get('profit') - col_return_qty = cols_map.get('return_qty') - col_date = cols_map.get('date') - col_pid = cols_map.get('pid') - - # V-Fix: 準備前端需要的下拉選單資料 - # V-Opt: 從數據庫直接查詢所有可用選項，而不是只從篩選後的快取中讀取 - # 這樣可以確保下拉選單顯示完整的可選項，即使當前篩選範圍很小 - all_categories = [] - all_brands = [] - all_vendors = [] - all_activities = [] - all_payments = [] - all_months = [] - - try: - from sqlalchemy import text - # V-Fix: SQLAlchemy 2.0 需要使用 connection 對象 - with db.engine.connect() as conn: - # 讀取完整資料表的所有可用選項（使用 DISTINCT 以提升效能） - # V-Fix: 使用單引號空字串，兼容 PostgreSQL - if col_category: - sql = f"SELECT DISTINCT \"{col_category}\" FROM {table_name} WHERE \"{col_category}\" IS NOT NULL AND \"{col_category}\" <> ''" - result = conn.execute(text(sql)).fetchall() - all_categories = sorted([str(row[0]) for row in result if row[0]]) - - if col_brand: - sql = f"SELECT DISTINCT \"{col_brand}\" FROM {table_name} WHERE \"{col_brand}\" IS NOT NULL AND \"{col_brand}\" <> ''" - result = conn.execute(text(sql)).fetchall() - all_brands = sorted([str(row[0]) for row in result if row[0]]) - - if col_vendor: - sql = f"SELECT DISTINCT \"{col_vendor}\" FROM {table_name} WHERE \"{col_vendor}\" IS NOT NULL AND \"{col_vendor}\" <> ''" - result = conn.execute(text(sql)).fetchall() - all_vendors = sorted([str(row[0]) for row in result if row[0]]) - - if col_activity: - sql = f"SELECT DISTINCT \"{col_activity}\" FROM {table_name} WHERE \"{col_activity}\" IS NOT NULL AND \"{col_activity}\" <> ''" - result = conn.execute(text(sql)).fetchall() - all_activities = sorted([str(row[0]) for row in result if row[0]]) - - if col_payment: - sql = f"SELECT DISTINCT \"{col_payment}\" FROM {table_name} WHERE \"{col_payment}\" IS NOT NULL AND \"{col_payment}\" <> ''" - result = conn.execute(text(sql)).fetchall() - all_payments = sorted([str(row[0]) for row in result if row[0]]) - - # V-Fix: 從數據庫提取所有月份（格式：YYYY-MM） - if col_date: - # 從日期欄位提取月份（支援多種日期欄位名稱） - date_fields = ['日期', '訂單日期', '時間'] - for field in date_fields: - try: - # V-Fix: 使用 substr 提取年月部分，並將斜線替換為橫線 - # 數據庫格式: "2025/07/01" -> 提取前7個字符 "2025/07" -> 替換斜線 "2025-07" - result = conn.execute(text(f""" - SELECT DISTINCT replace(substr(\"{field}\", 1, 7), '/', '-') as month - FROM {table_name} - WHERE \"{field}\" IS NOT NULL AND \"{field}\" != '' - ORDER BY month - """)).fetchall() - if result and len(result) > 0: - all_months = [row[0] for row in result if row[0] and '-' in str(row[0])] - if all_months: # 如果成功提取到月份，就使用這個欄位 - sys_log.info(f"[Sales Analysis] 從欄位 {field} 提取到 {len(all_months)} 個月份: {all_months}") - break - except Exception as ex: - sys_log.warning(f"[Sales Analysis] 從欄位 {field} 提取月份失敗: {ex}") - continue - except Exception as e: - sys_log.warning(f"[Sales Analysis] 從數據庫查詢下拉選項失敗: {e}") - # 如果查詢失敗，回退到從快取讀取 - if cache_key in _SALES_PROCESSED_CACHE: - original_df = _SALES_PROCESSED_CACHE[cache_key]['df'] - elif table_name in _SALES_PROCESSED_CACHE: - original_df = _SALES_PROCESSED_CACHE[table_name]['df'] - else: - original_df = pd.DataFrame() - - if not original_df.empty: - all_categories = sorted(original_df[col_category].dropna().astype(str).unique().tolist()) if col_category else [] - all_brands = sorted(original_df[col_brand].dropna().astype(str).unique().tolist()) if col_brand else [] - all_vendors = sorted(original_df[col_vendor].dropna().astype(str).unique().tolist()) if col_vendor else [] - all_activities = sorted(original_df[col_activity].dropna().astype(str).unique().tolist()) if col_activity else [] - all_payments = sorted(original_df[col_payment].dropna().astype(str).unique().tolist()) if col_payment else [] - all_months = sorted(original_df['_month_str'].dropna().unique().tolist()) if col_date and '_month_str' in original_df.columns else [] - - # 取得前端參數供模板回填 - selected_category = request.args.get('category', 'all') - selected_metric = request.args.get('metric', 'amount') - selected_brand = request.args.get('brand', 'all') - selected_vendor = request.args.get('vendor', 'all') - selected_activity = request.args.get('activity', 'all') - selected_payment = request.args.get('payment', 'all') - selected_dow = request.args.get('dow', 'all') - selected_hour = request.args.get('hour', 'all') - selected_month = request.args.get('month', 'all') - keyword = request.args.get('keyword', '').strip() - min_price = request.args.get('min_price', '') - max_price = request.args.get('max_price', '') - min_margin = request.args.get('min_margin', '') - max_margin = request.args.get('max_margin', '') - - # 決定排序欄位 - sort_col = col_amount - if selected_metric == 'qty' and col_qty: - sort_col = col_qty - - target_df = target_df.sort_values(by=sort_col, ascending=False) - - # 📊 KPI 計算 (針對篩選後的資料) - total_revenue = float(target_df[col_amount].sum()) - total_qty = float(target_df[col_qty].sum()) if col_qty else 0 - total_count = int(len(target_df)) # 訂單筆數 - # V-Fix 2026-01-15: SKU 數應計算唯一商品數，而非記錄筆數 - sku_count = int(target_df[col_name].nunique()) if col_name else total_count - - # V-New: 成本與毛利計算 - total_cost = float(target_df[col_cost].sum()) if col_cost else 0 - if col_profit: - gross_margin = float(target_df[col_profit].sum()) - else: - gross_margin = total_revenue - total_cost - - gross_margin_rate = (gross_margin / total_revenue * 100) if total_revenue > 0 else 0 - - avg_price = total_revenue / total_qty if total_qty > 0 else 0 - - # 📊 V-New: 商業洞察 (Top 3 Analysis) - insights = { - 'rev_cats': [], 'rev_prods': [], - 'margin_cats': [], 'margin_prods': [], - 'qty_cats': [], 'qty_prods': [] - } - - # Helper function to get top 3 - # Helper function to get top 3 - def get_top_3(groupby_col, metric_col, is_margin=False, is_qty=False): - if not groupby_col or not metric_col: return [] - - # V-Opt: 直接使用 target_df 與預計算欄位，避免 copy() 與 assign() - target_metric = metric_col - if is_margin: - target_metric = 'calculated_profit' - - try: - # 直接聚合並取前3名 - # V-Fix 2026-01-15: 若 groupby_col 是 list (例如 [PID, Name])，結果 index 會是 MultiIndex - grouped = target_df.groupby(groupby_col)[target_metric].sum() - - def get_name(k): - # 如果是 Tuple (MultiIndex)，通常最後一個是 Name，取之 - return str(k[-1]) if isinstance(k, tuple) else str(k) - - return [{'name': get_name(k), 'value': float(v)} for k, v in grouped.nlargest(3).items() if v > 0] - except Exception: - return [] - - insights['rev_cats'] = get_top_3(col_category, col_amount) - # V-Fix: 商品聚合改用 [PID, Name] 避免同名不同ID商品被合併 - product_groupby = [col_pid, col_name] if col_pid else col_name - insights['rev_prods'] = get_top_3(product_groupby, col_amount) - insights['qty_cats'] = get_top_3(col_category, col_qty, is_qty=True) - insights['qty_prods'] = get_top_3(product_groupby, col_qty, is_qty=True) - - if col_cost or col_profit: - insights['margin_cats'] = get_top_3(col_category, col_amount, is_margin=True) - insights['margin_prods'] = get_top_3(product_groupby, col_amount, is_margin=True) - - # 📊 V-Opt: 改為橫向長條圖數據 (Top 20) - top_chart = target_df.head(20) - bar_data = { - 'labels': [str(n)[:20] + '...' if len(str(n)) > 20 else str(n) for n in top_chart[col_name]], # 稍微放寬長度限制 - 'chart_values': [float(x) for x in top_chart[sort_col]], - 'metric_label': '銷售金額 ($)' if selected_metric == 'amount' else '銷售數量' - } - - # 📋 V-Opt: 列表資料改為 AJAX 載入，這裡只傳空列表以加快初始渲染 - table_items = [] - - # 準備類別圓餅圖資料 - # V-Fix: 使用 target_df (篩選後資料) 動態計算 - cat_data = {'labels': [], 'chart_values': []} - if col_category and not target_df.empty: - cat_group_all = target_df.groupby(col_category)[col_amount].sum().sort_values(ascending=False) - TOP_N_CATS = 12 - if len(cat_group_all) > TOP_N_CATS: - top_cats = cat_group_all.head(TOP_N_CATS) - other_val = cat_group_all.iloc[TOP_N_CATS:].sum() - cat_data['labels'] = [str(x) for x in top_cats.index.tolist()] + ['其他'] - cat_data['chart_values'] = [float(x) for x in top_cats.tolist()] + [float(other_val)] - else: - cat_data['labels'] = [str(x) for x in cat_group_all.index.tolist()] - cat_data['chart_values'] = [float(x) for x in cat_group_all.tolist()] - - # 📊 V-New: 價格帶分析 (Price Range Analysis) - price_dist_data = {'labels': [], 'chart_values': []} - if col_price and not target_df.empty: - # 定義價格區間 (0-500, 500-1000, 1000-2000, 2000-5000, 5000-10000, 10000+) - bins = [0, 500, 1000, 2000, 5000, 10000, float('inf')] - labels = ['0-499', '500-999', '1,000-1,999', '2,000-4,999', '5,000-9,999', '10,000+'] - - # V-Opt: 使用 pd.cut 進行分組，但不修改 target_df (避免污染快取) - # right=False 表示包含左邊界，例如 500 在 500-999 這一組 - price_bins = pd.cut(target_df[col_price], bins=bins, labels=labels, right=False) - - # 統計各區間的「銷售金額」貢獻 (直接使用外部 Series 進行 groupby) - range_group = target_df.groupby(price_bins, observed=False)[col_amount].sum() - - price_dist_data['labels'] = labels - price_dist_data['chart_values'] = [float(range_group.get(l, 0)) for l in labels] - - # 📊 V-New: 價格 vs 銷量 散佈圖 (Scatter Plot) - scatter_data = [] - if col_price and col_qty and not target_df.empty: - # 取前 300 筆主要商品，避免圖表過於密集導致瀏覽器卡頓 - scatter_source = target_df.head(300) - for _, row in scatter_source.iterrows(): - # V-Fix (2026-01-23): 處理 NaN 值 - price_val = row[col_price] if pd.notna(row[col_price]) else 0 - qty_val = row[col_qty] if pd.notna(row[col_qty]) else 0 - amt_val = row[col_amount] if pd.notna(row[col_amount]) else 0 - scatter_data.append({ - 'x': float(price_val), - 'y': float(qty_val), - 'name': str(row[col_name]) if pd.notna(row[col_name]) else '', - 'amt': float(amt_val) # 用於 tooltip 顯示金額 - }) - - # 📊 V-New: BCG 矩陣分析 (BCG Matrix) - # X軸: 銷量 (Qty), Y軸: 毛利率 (Margin %) - bcg_data = {'datasets': [], 'thresholds': {'x': 0, 'y': 0}} - # V-Fix: 確保 calculated_margin_rate 欄位存在 - if col_qty and (col_cost or col_profit) and not target_df.empty and 'calculated_margin_rate' in target_df.columns: - # 1. 計算閾值 (使用中位數，避免極端值影響) - # 過濾掉銷量為 0 的商品，避免干擾閾值計算 - active_products = target_df[target_df[col_qty] > 0] - if not active_products.empty and 'calculated_margin_rate' in active_products.columns: - median_qty = active_products[col_qty].median() - median_margin = active_products['calculated_margin_rate'].median() - - # 若中位數為 0 (例如大部分商品沒銷量)，則給一個預設值以利顯示 - if median_qty == 0: median_qty = 1 - - bcg_data['thresholds'] = {'x': float(median_qty), 'y': float(median_margin)} - - # 2. 分類商品 (四象限) - # Stars (明星): High Qty, High Margin - stars = active_products[(active_products[col_qty] >= median_qty) & (active_products['calculated_margin_rate'] >= median_margin)] - # Cows (金牛): High Qty, Low Margin - cows = active_products[(active_products[col_qty] >= median_qty) & (active_products['calculated_margin_rate'] < median_margin)] - # Questions (問題): Low Qty, High Margin - questions = active_products[(active_products[col_qty] < median_qty) & (active_products['calculated_margin_rate'] >= median_margin)] - # Dogs (瘦狗): Low Qty, Low Margin - dogs = active_products[(active_products[col_qty] < median_qty) & (active_products['calculated_margin_rate'] < median_margin)] - - def format_bcg_points(df_segment): - # 限制點數，避免前端卡頓 (各象限最多 100 點) - return [{'x': float(row[col_qty]), 'y': float(row['calculated_margin_rate']), 'name': str(row[col_name]), 'amt': float(row[col_amount])} for _, row in df_segment.head(100).iterrows()] - - bcg_data['datasets'] = [ - {'label': '明星商品 (Stars)', 'data': format_bcg_points(stars), 'backgroundColor': 'rgba(255, 206, 86, 0.8)', 'borderColor': 'rgba(255, 206, 86, 1)'}, # Yellow - {'label': '金牛商品 (Cows)', 'data': format_bcg_points(cows), 'backgroundColor': 'rgba(75, 192, 192, 0.8)', 'borderColor': 'rgba(75, 192, 192, 1)'}, # Green - {'label': '問題商品 (Questions)', 'data': format_bcg_points(questions), 'backgroundColor': 'rgba(54, 162, 235, 0.8)', 'borderColor': 'rgba(54, 162, 235, 1)'}, # Blue - {'label': '瘦狗商品 (Dogs)', 'data': format_bcg_points(dogs), 'backgroundColor': 'rgba(201, 203, 207, 0.8)', 'borderColor': 'rgba(201, 203, 207, 1)'} # Grey - ] - - # 📊 V-New: 時間維度分析 (Time Analysis) - dow_data = {'labels': ['週一', '週二', '週三', '週四', '週五', '週六', '週日'], 'chart_values': [0]*7} - hourly_data = {'labels': [f"{i:02d}:00" for i in range(24)], 'chart_values': [0]*24} - weekly_data = {'labels': [], 'chart_values': []} # V-New: 每週趨勢 - monthly_data = {'labels': [], 'chart_values': []} # V-New: 每月趨勢 - heatmap_data = [] # V-New: 多維度熱力圖 (Day x Hour) - treemap_data = [] # V-New: 板塊圖數據 - - if col_date: - # 過濾掉日期無效的資料 - # V-Opt: 使用預計算欄位進行分組，速度更快 - if not target_df.empty: - # 1. 星期分析 (Day of Week) - dow_group = target_df.groupby('_dow')[col_amount].sum() - for day, val in dow_group.items(): - if not np.isnan(day): - dow_data['chart_values'][int(day)] = float(val) - - # 2. 小時分析 (Hourly) - hour_group = target_df.groupby('_hour')[col_amount].sum() - for hour, val in hour_group.items(): - if not np.isnan(hour): - hourly_data['chart_values'][int(hour)] = float(val) - - # 3. 每月趨勢 (Monthly Trend) - V-New - month_group = target_df.groupby('_month_str')[col_amount].sum().sort_index() - monthly_data['labels'] = month_group.index.tolist() - # V-Fix (2026-01-23): 處理 NaN 值避免 JSON 序列化失敗 - monthly_data['chart_values'] = [float(x) if not np.isnan(x) else 0 for x in month_group.tolist()] - - # 3. 每週趨勢 (Weekly Trend) - V-New - week_group = target_df.groupby('_week')[col_amount].sum().sort_index() - # V-Opt: 解除 12 週限制，顯示完整年度趨勢 (因應一年份數據需求) - weekly_data['labels'] = week_group.index.tolist() - # V-Fix (2026-01-23): 處理 NaN 值避免 JSON 序列化失敗 - weekly_data['chart_values'] = [float(x) if not np.isnan(x) else 0 for x in week_group.tolist()] - - # 4. 多維度熱力圖 (Day x Hour) - V-Fix: 確保數據完整性 - dh_group = target_df.groupby(['_dow', '_hour'])[col_amount].sum() - # V-Opt: 正規化氣泡大小 (Normalize Bubble Size) 以提升可讀性 - max_val = dh_group.max() if not dh_group.empty else 1 - - for (day, hour), val in dh_group.items(): - # V-Fix (2026-01-23): 處理 NaN 值 - if np.isnan(val): - val = 0 - # 將數值映射到 3~25px 的半徑範圍，確保視覺可辨識 - radius = 3 + (math.sqrt(val) / math.sqrt(max_val)) * 22 if val > 0 else 0 - heatmap_data.append({ - 'x': int(hour), # X軸: 小時 (0-23) - 'y': int(day), # Y軸: 星期 (0-6) - 'r': float(radius) if not np.isnan(radius) else 0, # V-Adj: 正規化後半徑 - 'v': float(val) # 實際數值 (用於 Tooltip) - }) - - # 📊 V-New: 板塊圖 (Treemap) 數據準備 - # 結構: Root -> Category -> Product (Top 5 per cat) - if col_category and col_name and col_amount and not target_df.empty: - # V-Opt: 優化聚合邏輯，先聚合再篩選，避免在迴圈中重複過濾大表 - # 1. 先聚合 Category + Product (大幅減少資料量) - cat_prod_group = target_df.groupby([col_category, col_name])[col_amount].sum().reset_index() - - # 2. 找出前 10 大分類 - top_cats = cat_prod_group.groupby(col_category)[col_amount].sum().nlargest(10).index.tolist() - - # 3. 針對前 10 大分類，各取前 5 大商品 - for cat in top_cats: - if not cat: continue - # 在縮減後的資料中篩選，速度極快 - cat_subset = cat_prod_group[cat_prod_group[col_category] == cat] - top_prods = cat_subset.nlargest(5, col_amount) - - for _, row in top_prods.iterrows(): - # V-Fix (2026-01-23): 處理 NaN 值 - amount_val = row[col_amount] - if pd.isna(amount_val): - amount_val = 0 - treemap_data.append({ - 'category': str(cat), - 'product': str(row[col_name]) if pd.notna(row[col_name]) else '', - 'value': float(amount_val), - 'color': get_color_for_string(str(cat)) # V-Fix: 增加顏色參數，確保與分類顏色一致且清晰 - }) - - # 📊 V-New: ABC 分析 (Pareto Analysis) - TODO #8 - # A類: 累積營收 0-80% (核心商品) - # B類: 累積營收 80-95% (次要商品) - # C類: 累積營收 95-100% (長尾商品) - abc_stats = {'A': {'count': 0, 'revenue': 0, 'pct_rev': 0, 'pct_sku': 0}, - 'B': {'count': 0, 'revenue': 0, 'pct_rev': 0, 'pct_sku': 0}, - 'C': {'count': 0, 'revenue': 0, 'pct_rev': 0, 'pct_sku': 0}} - - if not target_df.empty and col_amount: - # 使用 numpy 加速累積計算 - sorted_rev = target_df[col_amount].values # 已在上方排序過 - cumsum_rev = np.cumsum(sorted_rev) - total_rev_abc = cumsum_rev[-1] if len(cumsum_rev) > 0 else 0 - - if total_rev_abc > 0: - pct_cumsum = cumsum_rev / total_rev_abc * 100 - - # 找出分界點索引 - idx_a = np.searchsorted(pct_cumsum, 80) - idx_b = np.searchsorted(pct_cumsum, 95) - - # A類: 0 ~ idx_a - count_a = idx_a + 1 - rev_a = cumsum_rev[idx_a] if idx_a < len(cumsum_rev) else total_rev_abc - - # B類: idx_a+1 ~ idx_b - count_b = max(0, idx_b - idx_a) - rev_b = (cumsum_rev[idx_b] - cumsum_rev[idx_a]) if idx_b < len(cumsum_rev) else (total_rev_abc - cumsum_rev[idx_a]) - - # C類: idx_b+1 ~ end - count_c = max(0, len(cumsum_rev) - 1 - idx_b) - rev_c = total_rev_abc - cumsum_rev[idx_b] if idx_b < len(cumsum_rev) else 0 - - abc_stats['A'] = {'count': int(count_a), 'revenue': float(rev_a), 'pct_rev': float(rev_a/total_rev_abc*100), 'pct_sku': float(count_a/total_count*100)} - abc_stats['B'] = {'count': int(count_b), 'revenue': float(rev_b), 'pct_rev': float(rev_b/total_rev_abc*100), 'pct_sku': float(count_b/total_count*100)} - abc_stats['C'] = {'count': int(count_c), 'revenue': float(rev_c), 'pct_rev': float(rev_c/total_rev_abc*100), 'pct_sku': float(count_c/total_count*100)} - - # 📊 V-New: 廠商獲利能力排行 (Vendor Profitability) - TODO #9 - vendor_stats = [] - if col_vendor and col_amount and not target_df.empty: - # Group by vendor - agg_dict = {col_amount: 'sum', col_name: 'nunique'} # nunique 計算不重複商品數 (SKU) - if col_qty: agg_dict[col_qty] = 'sum' # V-New: 累加銷量 - if col_profit: - agg_dict[col_profit] = 'sum' - elif col_cost: - agg_dict[col_cost] = 'sum' - - # 使用 groupby 聚合 - vendor_group = target_df.groupby(col_vendor).agg(agg_dict).reset_index() - - # 計算毛利與毛利率 - if col_profit: - vendor_group['total_profit'] = vendor_group[col_profit] - elif col_cost: - vendor_group['total_profit'] = vendor_group[col_amount] - vendor_group[col_cost] - else: - vendor_group['total_profit'] = 0 - - # 計算營收佔比 (Share %) - total_vendor_revenue = vendor_group[col_amount].sum() - if total_vendor_revenue > 0: - vendor_group['revenue_share'] = (vendor_group[col_amount] / total_vendor_revenue * 100) - else: - vendor_group['revenue_share'] = 0.0 - - # 避免除以零 - vendor_group['margin_rate'] = np.where(vendor_group[col_amount] > 0, (vendor_group['total_profit'] / vendor_group[col_amount] * 100), 0) - - # 計算平均客單價 (ASP) - if col_qty: - vendor_group['asp'] = np.where(vendor_group[col_qty] > 0, vendor_group[col_amount] / vendor_group[col_qty], 0) - - # 排序：預設按總業績降序 - vendor_group = vendor_group.sort_values(by=col_amount, ascending=False) - - # 格式化輸出 (Top 100) - for _, row in vendor_group.head(100).iterrows(): - vendor_stats.append({ - 'name': str(row[col_vendor]), - 'revenue': float(row[col_amount]), - 'share': float(row['revenue_share']), # V-New - 'qty': float(row[col_qty]) if col_qty else 0, # V-New - 'asp': float(row.get('asp', 0)), # V-New - 'profit': float(row['total_profit']), - 'margin_rate': float(row['margin_rate']), - 'sku_count': int(row[col_name]) - }) - - # 📊 V-New: 淡旺季熱力圖 (Seasonality Analysis) - TODO #10 - seasonality_data = None - if col_date and col_category and col_amount and not target_df.empty: - # 1. 取得前 10 大分類 (避免圖表過大) - # 使用 target_df (受篩選影響)，這樣可以看特定品牌下的分類季節性 - top_cats_season = target_df.groupby(col_category)[col_amount].sum().nlargest(10).index.tolist() - - # 2. 聚合數據 (Month x Category) - season_group = target_df[target_df[col_category].isin(top_cats_season)].groupby(['_month_str', col_category])[col_amount].sum().reset_index() - - # 3. 轉換為 Bubble Chart 格式 - # X軸: 月份 (需解析 _month_str 取得順序) - # Y軸: 分類 (使用 top_cats_season 的索引) - - # 取得所有月份並排序 - all_months_sorted = sorted(target_df['_month_str'].unique()) - month_map = {m: i for i, m in enumerate(all_months_sorted)} - cat_map = {c: i for i, c in enumerate(top_cats_season)} - - points = [] - max_val_season = season_group[col_amount].max() if not season_group.empty else 1 - - for _, row in season_group.iterrows(): - m_str = row['_month_str'] - cat = row[col_category] - val = row[col_amount] - - if m_str in month_map and cat in cat_map: - # 正規化大小 (3~25px) - radius = 3 + (math.sqrt(val) / math.sqrt(max_val_season)) * 25 if val > 0 else 0 - points.append({ - 'x': month_map[m_str], - 'y': cat_map[cat], - 'r': radius, - 'v': float(val), - 'm': m_str, - 'c': cat - }) - - seasonality_data = { - 'datasets': [{ - 'label': '淡旺季熱點', - 'data': points, - # 顏色將在前端動態生成 - }], - 'yLabels': top_cats_season, - 'xLabels': all_months_sorted - } - - # 📊 V-New 2026-01-15: 行銷活動業績貢獻 (Marketing Campaign Contribution) - marketing_data = None - if not target_df.empty: - marketing_data = prepare_marketing_summary(target_df, sort_by=selected_metric) - - return render_template('sales_analysis.html', - marketing_data=marketing_data, # V-New: 傳遞行銷活動數據 - items=table_items, - kpi={ - 'revenue': total_revenue, - 'qty': total_qty, - 'count': total_count, - 'sku_count': sku_count, # V-Fix 2026-01-15: 唯一商品數 - 'cost': total_cost, - 'gross_margin': gross_margin, - 'gross_margin_rate': gross_margin_rate, - 'avg_price': avg_price - }, - insights=insights, - abc_stats=abc_stats, # V-New: 傳遞 ABC 分析數據 - vendor_stats=vendor_stats, # V-New: 傳遞廠商排行數據 - seasonality_data=seasonality_data, # V-New: 傳遞淡旺季數據 - bar_data=bar_data, - cat_data=cat_data, - price_dist_data=price_dist_data, - scatter_data=scatter_data, - bcg_data=bcg_data, # V-New: 傳遞 BCG 數據 - dow_data=dow_data, - hourly_data=hourly_data, - monthly_data=monthly_data, - weekly_data=weekly_data, - heatmap_data=heatmap_data, - treemap_data=treemap_data, - all_categories=all_categories, - all_brands=all_brands, all_vendors=all_vendors, all_activities=all_activities, all_payments=all_payments, - all_months=all_months, # V-New: 傳遞月份列表 - selected_category=selected_category, - selected_brand=selected_brand, selected_vendor=selected_vendor, - selected_activity=selected_activity, selected_payment=selected_payment, - selected_dow=selected_dow, selected_hour=selected_hour, - selected_month=selected_month, - selected_metric=selected_metric, - keyword=keyword, min_price=min_price, max_price=max_price, - min_margin=min_margin, max_margin=max_margin, - cols={'name': col_name, 'amount': col_amount, 'qty': col_qty, 'cat': col_category, 'date': col_date, 'cost': col_cost, 'profit': col_profit, 'vendor': col_vendor, 'brand': col_brand, 'return_qty': col_return_qty, 'pid': col_pid}, - table_name=table_name, - data_range_months=data_range_months, - start_date=start_date, # V-New: 傳遞自訂開始日期 - end_date=end_date, # V-New: 傳遞自訂結束日期 - total_records=len(df), - db_data_range=db_data_range) # V-New: 傳遞資料庫資料期間 - - except Exception as e: - sys_log.error(f"Sales Analysis Error: {e}") - import traceback - traceback.print_exc() - # 提供完整的變數以避免模板錯誤 - return render_template('sales_analysis.html', - error=f"系統發生錯誤: {str(e)}", - marketing_data=None, - insights=None, - abc_stats=None, - vendor_stats=None, - seasonality_data=None, - scatter_data=None, - bcg_data=None, - dow_data=None, - hourly_data=None, - monthly_data=None, - weekly_data=None, - heatmap_data=None, - treemap_data=None, - all_categories=[], - all_brands=[], all_vendors=[], all_activities=[], all_payments=[], - all_months=[], - selected_category='all', - selected_brand='all', selected_vendor='all', - selected_activity='all', selected_payment='all', - selected_dow='all', selected_hour='all', - selected_month='all', - selected_metric=request.args.get('metric', 'amount'), - keyword='', min_price='', max_price='', - min_margin='', max_margin='', - cols={}, - table_name='realtime_sales_monthly', - no_filter=False, - data_range_months=int(request.args.get('data_range', '0') or '0'), - start_date=request.args.get('start_date', ''), - end_date=request.args.get('end_date', ''), - total_records=0, - db_data_range='') # V-Opt: API 層級快取 (減少重複查詢) _TABLE_DATA_CACHE = {} _TABLE_DATA_CACHE_TTL = 60 # 快取 60 秒 -@app.route('/api/sales_analysis/table_data') -def get_sales_table_data(): - """API: 取得業績分析的詳細列表資料 (Server-side AJAX) - 使用 SQL 聚合優化""" - try: - import hashlib - from datetime import datetime, timedelta, timezone - TAIPEI_TZ = timezone(timedelta(hours=8)) - - # V-Opt: 產生查詢快取 key (根據所有篩選條件) - cache_params = request.args.to_dict() - cache_key = hashlib.md5(str(sorted(cache_params.items())).encode(), usedforsecurity=False).hexdigest() - - # V-Opt: 檢查快取 - if cache_key in _TABLE_DATA_CACHE: - cached = _TABLE_DATA_CACHE[cache_key] - if time.time() - cached['time'] < _TABLE_DATA_CACHE_TTL: - sys_log.debug(f"[API] Table Data: 使用快取 (key={cache_key[:8]})") - return jsonify(cached['data']) - - table_name = 'realtime_sales_monthly' - data_range_months = int(request.args.get('data_range', '1') or '1') - start_date = request.args.get('start_date', '') # V-New: 自訂開始日期 - end_date = request.args.get('end_date', '') # V-New: 自訂結束日期 - - # V-Fix: 取得所有篩選參數 - category_filter = request.args.get('category', 'all') - brand_filter = request.args.get('brand', 'all') # V-Fix: 品牌篩選 - vendor_filter = request.args.get('vendor', 'all') # V-Fix: 廠商篩選 - activity_filter = request.args.get('activity', 'all') # V-Fix: 活動篩選 - payment_filter = request.args.get('payment', 'all') # V-Fix: 付款方式篩選 - month_filter = request.args.get('month', 'all') - dow_filter = request.args.get('dow', 'all') # 星期篩選 - hour_filter = request.args.get('hour', 'all') # 小時篩選 - min_price_str = request.args.get('min_price', '') - max_price_str = request.args.get('max_price', '') - min_margin_str = request.args.get('min_margin', '') - max_margin_str = request.args.get('max_margin', '') - keyword = request.args.get('keyword', '').strip() - - db = DatabaseManager() - - # V-Fix: 從快取讀取欄位名稱對應，以支援不同的資料庫欄位名稱 - if start_date or end_date: - cache_key = f"{table_name}_custom_{start_date}_{end_date}" - else: - cache_key = f"{table_name}_{data_range_months}m" - - # 嘗試從快取讀取欄位名稱 - cols_map = {} - if cache_key in _SALES_PROCESSED_CACHE: - cols_map = _SALES_PROCESSED_CACHE[cache_key].get('cols', {}) - elif table_name in _SALES_PROCESSED_CACHE: # V-Fix: 也嘗試使用固定 key - cols_map = _SALES_PROCESSED_CACHE[table_name].get('cols', {}) - - # 取得實際欄位名稱（如果快取中沒有，使用預設名稱） - # V-Fix (2026-01-23): 使用 or 確保不會得到 None 值 - col_name = cols_map.get('name') or '商品名稱' - col_pid = cols_map.get('pid') or '商品ID' - col_brand = cols_map.get('brand') or '品牌' - col_vendor = cols_map.get('vendor') or '廠商名稱' - col_category = cols_map.get('category') or '商品館' - col_amount = cols_map.get('amount') or '總業績' - col_qty = cols_map.get('qty') or '數量' - col_cost = cols_map.get('cost') or '總成本' - col_return_qty = cols_map.get('return_qty') or '退貨數量' - - # V-Opt: 使用純 SQL 聚合查詢，避免載入完整資料集 - # 建立日期篩選條件 - date_filter = "" - # V-New: 優先處理自訂日期區間 - if start_date or end_date: - # V-Fix: 處理日期格式轉換 (2025-01-01 -> 2025/01/01) - start_date_slash = start_date.replace('-', '/') if start_date else '' - end_date_slash = end_date.replace('-', '/') if end_date else '' - - # V-Fix: 只使用「日期」欄位（「訂單日期」欄位是固定文字「訂單日期」，不是實際日期） - if start_date and end_date: - date_filter = f"""AND ("日期" BETWEEN '{start_date_slash}' AND '{end_date_slash}')""" - elif start_date: - date_filter = f"""AND ("日期" >= '{start_date_slash}')""" - else: # only end_date - date_filter = f"""AND ("日期" <= '{end_date_slash}')""" - elif data_range_months > 0: - # V-Fix: 使用斜線格式以匹配資料庫格式 - cutoff_date = (datetime.now(TAIPEI_TZ) - timedelta(days=data_range_months * 30)).strftime('%Y/%m/%d') - # V-Fix: 只使用「日期」欄位進行篩選（「訂單日期」是固定文字，不是實際日期） - date_filter = f"""AND ("日期" >= '{cutoff_date}')""" - - # V-Fix: 建立其他篩選條件 - additional_filters = [] - - # 分類篩選 - if category_filter and category_filter != 'all' and col_category: - additional_filters.append(f""""{col_category}" = '{category_filter}'""") - - # V-Fix: 品牌篩選 - if brand_filter and brand_filter != 'all' and col_brand: - additional_filters.append(f""""{col_brand}" = '{brand_filter}'""") - - # V-Fix: 廠商篩選 - if vendor_filter and vendor_filter != 'all' and col_vendor: - additional_filters.append(f""""{col_vendor}" = '{vendor_filter}'""") - - # V-Fix: 活動篩選 - col_activity = cols_map.get('activity') - if activity_filter and activity_filter != 'all' and col_activity: - additional_filters.append(f""""{col_activity}" = '{activity_filter}'""") - - # V-Fix: 付款方式篩選 - col_payment = cols_map.get('payment') - if payment_filter and payment_filter != 'all' and col_payment: - additional_filters.append(f""""{col_payment}" = '{payment_filter}'""") - - # 月份篩選 - if month_filter and month_filter != 'all': - # V-Fix: 月份格式例如 "2025-01"，但資料庫可能使用斜線格式 "2025/01" - # 只使用「日期」欄位（「訂單日期」是固定文字，「時間」只包含時間） - month_filter_slash = month_filter.replace('-', '/') # "2025-01" -> "2025/01" - # 同時匹配橫線和斜線格式 - additional_filters.append(f"""("日期" LIKE '{month_filter}%' OR "日期" LIKE '{month_filter_slash}%')""") - - # 星期篩選 (需要從日期計算) - if dow_filter and dow_filter != 'all': - # V-Fix (2026-01-23): 支援 PostgreSQL 和 SQLite 兩種資料庫 - # Pandas dt.dayofweek: 0=Monday, 6=Sunday - pandas_dow = int(dow_filter) - if DATABASE_TYPE == 'postgresql': - # PostgreSQL: EXTRACT(DOW FROM date) 0=Sunday, 6=Saturday - # Pandas 0(Mon) -> PostgreSQL 1(Mon), Pandas 6(Sun) -> PostgreSQL 0(Sun) - pg_dow = (pandas_dow + 1) % 7 - # 日期格式可能是 2025/01/01，需要轉換為 YYYY-MM-DD - additional_filters.append(f"""EXTRACT(DOW FROM TO_DATE(REPLACE("日期", '/', '-'), 'YYYY-MM-DD')) = {pg_dow}""") - else: - # SQLite: strftime('%w', date) 0=Sunday, 6=Saturday - sqlite_dow = str((pandas_dow + 1) % 7) - additional_filters.append(f"""strftime('%w', replace("日期", '/', '-')) = '{sqlite_dow}'""") - - # 小時篩選 (需要從時間欄位提取) - if hour_filter and hour_filter != 'all': - # V-Fix (2026-01-23): 支援 PostgreSQL 和 SQLite 兩種資料庫 - hour_val = int(hour_filter) - if DATABASE_TYPE == 'postgresql': - # PostgreSQL: 使用 SUBSTRING 或 CAST - additional_filters.append(f"""CAST(SUBSTRING("時間" FROM 1 FOR 2) AS INTEGER) = {hour_val}""") - else: - # SQLite: 使用 substr - additional_filters.append(f"""CAST(substr("時間", 1, 2) AS INTEGER) = {hour_val}""") - - # 關鍵字篩選 - if keyword: - keyword_escaped = keyword.replace("'", "''") # SQL 注入防護 - keyword_conditions = [] - if col_name: - keyword_conditions.append(f""""{col_name}" LIKE '%{keyword_escaped}%'""") - if col_pid: - keyword_conditions.append(f""""{col_pid}" LIKE '%{keyword_escaped}%'""") - if col_brand: - keyword_conditions.append(f""""{col_brand}" LIKE '%{keyword_escaped}%'""") - if col_vendor: - keyword_conditions.append(f""""{col_vendor}" LIKE '%{keyword_escaped}%'""") - if keyword_conditions: - additional_filters.append(f"({' OR '.join(keyword_conditions)})") - - # V-New: 價格區間篩選 (Price Range) - if (min_price_str or max_price_str) and col_qty and col_amount: - # 假設單價 = 總業績 / 數量 (防止除以零) - price_cal_sql = f'CAST("{col_amount}" AS FLOAT) / NULLIF("{col_qty}", 0)' - if min_price_str: - additional_filters.append(f"{price_cal_sql} >= {float(min_price_str)}") - if max_price_str: - additional_filters.append(f"{price_cal_sql} <= {float(max_price_str)}") - - # V-New: 毛利率區間篩選 (Margin Range) - if (min_margin_str or max_margin_str) and col_amount: - # 計算毛利額 SQL - if col_profit: - profit_cal_sql = f'"{col_profit}"' - elif col_cost: - profit_cal_sql = f'("{col_amount}" - "{col_cost}")' - else: - profit_cal_sql = "0" - - # 計算毛利率 SQL: (毛利 / 業績) * 100 - margin_cal_sql = f'({profit_cal_sql} * 100.0 / NULLIF("{col_amount}", 0))' - - if min_margin_str: - additional_filters.append(f"{margin_cal_sql} >= {float(min_margin_str)}") - if max_margin_str: - additional_filters.append(f"{margin_cal_sql} <= {float(max_margin_str)}") - - # 組合所有篩選條件 - all_filters = date_filter - if additional_filters: - all_filters += " AND " + " AND ".join(additional_filters) - - # SQL 聚合查詢 - 直接在資料庫層級完成聚合 - # V-Fix: 使用動態欄位名稱 - group_by_cols = [] - if col_pid: group_by_cols.append(f'"{col_pid}"') - if col_name: group_by_cols.append(f'"{col_name}"') - if col_brand: group_by_cols.append(f'"{col_brand}"') - if col_vendor: group_by_cols.append(f'"{col_vendor}"') - if col_category: group_by_cols.append(f'"{col_category}"') - group_by_clause = ', '.join(group_by_cols) if group_by_cols else '"商品ID"' - - sql_query = f""" - SELECT - {f'"{col_pid}" as product_id' if col_pid else "'未知' as product_id"}, - {f'"{col_name}" as name' if col_name else "'未知' as name"}, - {f'"{col_brand}" as brand' if col_brand else "'' as brand"}, - {f'"{col_vendor}" as vendor' if col_vendor else "'' as vendor"}, - {f'"{col_category}" as category' if col_category else "'' as category"}, - {f'SUM(CAST("{col_amount}" AS REAL)) as amount' if col_amount else '0 as amount'}, - {f'SUM(CAST("{col_qty}" AS REAL)) as qty' if col_qty else '0 as qty'}, - {f'SUM(CAST("{col_cost}" AS REAL)) as cost' if col_cost else '0 as cost'}, - {f'SUM(CAST("{col_return_qty}" AS REAL)) as return_qty' if col_return_qty else '0 as return_qty'}, - COUNT(*) as order_count - FROM {table_name} - WHERE 1=1 {all_filters} - GROUP BY {group_by_clause} - ORDER BY amount DESC - LIMIT 300 - """ - - df_agg = pd.read_sql(sql_query, db.engine) - sys_log.info(f"[API] Table Data: SQL聚合查詢返回 {len(df_agg)} 筆商品 (篩選: category={category_filter}, month={month_filter}, dow={dow_filter}, hour={hour_filter}, keyword={keyword})") - - if df_agg.empty: - return jsonify({'data': []}) - - # 計算衍生欄位 - df_agg['margin_rate'] = ((df_agg['amount'] - df_agg['cost']) / df_agg['amount'] * 100).fillna(0) - df_agg['margin_rate'] = df_agg['margin_rate'].replace([np.inf, -np.inf], 0) - df_agg['avg_price'] = (df_agg['amount'] / df_agg['qty']).fillna(0) - df_agg['return_rate'] = (df_agg['return_qty'] / df_agg['qty'] * 100).fillna(0) - - # V-Fix: 應用價格區間篩選 (在計算欄位後才能篩選) - if min_price_str: - try: - min_price = float(min_price_str) - df_agg = df_agg[df_agg['avg_price'] >= min_price] - except ValueError: - pass - - if max_price_str: - try: - max_price = float(max_price_str) - df_agg = df_agg[df_agg['avg_price'] <= max_price] - except ValueError: - pass - - # V-Fix: 應用毛利區間篩選 (在計算欄位後才能篩選) - if min_margin_str: - try: - min_margin = float(min_margin_str) - df_agg = df_agg[df_agg['margin_rate'] >= min_margin] - except ValueError: - pass - - if max_margin_str: - try: - max_margin = float(max_margin_str) - df_agg = df_agg[df_agg['margin_rate'] <= max_margin] - except ValueError: - pass - - # 重新排序並限制到 300 筆 (減少前端渲染負擔) - df_agg = df_agg.sort_values('amount', ascending=False).head(300) - - # V-Opt: 使用向量化操作取代逐列迴圈 - df_agg['rank'] = range(1, len(df_agg) + 1) - df_agg['month_str'] = '' # SQL聚合模式不需要月份字串 - - # 重新命名欄位以符合前端格式 - result_df = df_agg.rename(columns={ - 'product_id': 'product_id', - 'name': 'name', - 'brand': 'brand', - 'vendor': 'vendor', - 'category': 'category', - 'margin_rate': 'margin_rate', - 'avg_price': 'avg_price', - 'return_rate': 'return_rate', - 'qty': 'qty', - 'amount': 'amount' - }) - - # 選擇需要的欄位並轉換為字典列表 - columns = ['rank', 'product_id', 'name', 'brand', 'vendor', 'category', - 'margin_rate', 'month_str', 'avg_price', 'return_rate', 'qty', 'amount'] - - # V-Fix (2026-01-23): 確保所有數值欄位無 NaN/Infinity，避免 JSON 序列化失敗 - numeric_cols = ['margin_rate', 'avg_price', 'return_rate', 'qty', 'amount'] - for col in numeric_cols: - if col in result_df.columns: - result_df[col] = result_df[col].replace([np.inf, -np.inf], 0).fillna(0) - - # V-Fix (2026-01-23): 確保字串欄位無 None，避免 JSON 序列化失敗 - string_cols = ['product_id', 'name', 'brand', 'vendor', 'category', 'month_str'] - for col in string_cols: - if col in result_df.columns: - result_df[col] = result_df[col].fillna('').astype(str) - - data = result_df[columns].to_dict('records') - - response_data = {'data': data} - - # V-Opt: 儲存到快取 - _TABLE_DATA_CACHE[cache_key] = {'data': response_data, 'time': time.time()} - - # V-Opt: 清理過期快取 (保留最近 50 個) - if len(_TABLE_DATA_CACHE) > 50: - sorted_keys = sorted(_TABLE_DATA_CACHE.keys(), - key=lambda k: _TABLE_DATA_CACHE[k]['time']) - for old_key in sorted_keys[:-50]: - del _TABLE_DATA_CACHE[old_key] - - return jsonify(response_data) - - except Exception as e: - sys_log.error(f"[API] Table Data Error: {e}") - import traceback - traceback.print_exc() - return jsonify({'error': str(e)}), 500 # V-Old: 保留舊版本以防需要回滾 -@app.route('/api/sales_analysis/table_data_pandas') -def get_sales_table_data_pandas(): - """API: 取得業績分析的詳細列表資料 (使用 pandas 聚合 - 舊版本)""" - try: - table_name = 'realtime_sales_monthly' - data_range_months = int(request.args.get('data_range', '1')) - cache_key = f"{table_name}_{data_range_months}m" - target_df, cols_map, err = _get_filtered_sales_data(cache_key) - - if err or target_df is None: - sys_log.warning(f"[API] Table Data: 快取不存在 ({cache_key})，返回空資料") - return jsonify({'data': []}) - - if target_df.empty: - return jsonify({'data': []}) - - col_name = cols_map.get('name') - col_amount = cols_map.get('amount') - col_qty = cols_map.get('qty') - col_cost = cols_map.get('cost') - col_profit = cols_map.get('profit') - col_category = cols_map.get('category') - col_vendor = cols_map.get('vendor') - col_date = cols_map.get('date') - col_brand = cols_map.get('brand') - col_return_qty = cols_map.get('return_qty') - - selected_metric = request.args.get('metric', 'amount') - - # 執行聚合 (V-Opt: 多維度聚合，增加精確度) - agg_rules = {col_amount: 'sum'} - if col_qty: agg_rules[col_qty] = 'sum' - if col_cost: agg_rules[col_cost] = 'sum' - if col_profit: agg_rules[col_profit] = 'sum' - if col_return_qty: agg_rules[col_return_qty] = 'sum' - if col_date: agg_rules['_month_str'] = lambda x: ', '.join(sorted(x.dropna().unique())) - - # Group By 鍵值：商品名稱 + 品牌 + 廠商 + 分類 (確保唯一性) - group_cols = [col_name] - if col_brand: group_cols.append(col_brand) - if col_vendor: group_cols.append(col_vendor) - if col_category: group_cols.append(col_category) - - df_agg = target_df.groupby(group_cols).agg(agg_rules).reset_index() - - # 計算毛利率 - if col_profit: - df_agg['agg_margin_rate'] = (df_agg[col_profit] / df_agg[col_amount]) * 100 - elif col_cost: - df_agg['agg_margin_rate'] = ((df_agg[col_amount] - df_agg[col_cost]) / df_agg[col_amount]) * 100 - else: - df_agg['agg_margin_rate'] = 0.0 - df_agg['agg_margin_rate'] = df_agg['agg_margin_rate'].replace([np.inf, -np.inf, np.nan], 0) - - # V-New: 計算平均單價與退貨率 - if col_qty: - df_agg['avg_price'] = (df_agg[col_amount] / df_agg[col_qty]).fillna(0) - if col_return_qty: - df_agg['return_rate'] = (df_agg[col_return_qty] / df_agg[col_qty] * 100).fillna(0) - - # 排序 - sort_col_agg = col_amount - if selected_metric == 'qty' and col_qty: - sort_col_agg = col_qty - - df_agg = df_agg.sort_values(by=sort_col_agg, ascending=False).head(1000) # 限制前 1000 筆 - - # 轉換為 DataTables 需要的格式 - data = [] - for i, row in enumerate(df_agg.to_dict('records')): - data.append({ - 'rank': i + 1, - 'name': row.get(col_name, ''), - 'brand': row.get(col_brand, ''), - 'vendor': row.get(col_vendor, ''), - 'category': row.get(col_category, ''), - 'margin_rate': row.get('agg_margin_rate', 0), - 'month_str': row.get('_month_str', ''), - 'avg_price': row.get('avg_price', 0), - 'return_rate': row.get('return_rate', 0), - 'qty': row.get(col_qty, 0), - 'amount': row.get(col_amount, 0) - }) - - return jsonify({'data': data}) - - except Exception as e: - sys_log.error(f"Table Data API Error: {e}") - return jsonify({'error': str(e)}), 500 # ================= 💎 V-New: Top 3 Highlights 詳細列表 API ================= -@app.route('/api/sales_analysis/top_detail') -def get_top_detail(): - """API: 取得 Top N 詳細列表（業績貢獻王/獲利金雞母/人氣引流款）""" - try: - from datetime import datetime, timedelta, timezone - TAIPEI_TZ = timezone(timedelta(hours=8)) - table_name = 'realtime_sales_monthly' - data_range_months = int(request.args.get('data_range', '1') or '1') - start_date = request.args.get('start_date', '') # V-New: 自訂開始日期 - end_date = request.args.get('end_date', '') # V-New: 自訂結束日期 - top_type = request.args.get('type', 'revenue') # revenue/margin/quantity - metric = request.args.get('metric', 'amount') # amount/profit/qty - view_type = request.args.get('view', 'product') # product/category - - db = DatabaseManager() - - # V-Fix: 從快取讀取欄位名稱對應，以支援不同的資料庫欄位名稱 - if start_date or end_date: - cache_key = f"{table_name}_custom_{start_date}_{end_date}" - else: - cache_key = f"{table_name}_{data_range_months}m" - - # 嘗試從快取讀取欄位名稱 - cols_map = {} - if cache_key in _SALES_PROCESSED_CACHE: - cols_map = _SALES_PROCESSED_CACHE[cache_key].get('cols', {}) - elif table_name in _SALES_PROCESSED_CACHE: # V-Fix: 也嘗試使用固定 key - cols_map = _SALES_PROCESSED_CACHE[table_name].get('cols', {}) - - # 取得實際欄位名稱（如果快取中沒有，使用預設名稱） - # V-Fix (2026-01-23): 使用 or 確保不會得到 None 值 - col_name = cols_map.get('name') or '商品名稱' - col_brand = cols_map.get('brand') or '品牌' - col_vendor = cols_map.get('vendor') or '廠商名稱' - col_category = cols_map.get('category') or '商品館' - col_amount = cols_map.get('amount') or '總業績' - col_qty = cols_map.get('qty') or '數量' - col_cost = cols_map.get('cost') or '總成本' - col_profit = cols_map.get('profit') # 可以為 None - col_activity = cols_map.get('activity') or '活動名稱' - col_payment = cols_map.get('payment') or '付款方式' - - # 建立日期篩選條件 - date_filter = "" - # V-New: 優先處理自訂日期區間 - if start_date or end_date: - # V-Fix: 處理日期格式轉換 (2025-01-01 -> 2025/01/01) - start_date_slash = start_date.replace('-', '/') if start_date else '' - end_date_slash = end_date.replace('-', '/') if end_date else '' - - if start_date and end_date: - date_filter = f"""AND "日期" BETWEEN '{start_date_slash}' AND '{end_date_slash}'""" - elif start_date: - date_filter = f"""AND "日期" >= '{start_date_slash}'""" - else: # only end_date - date_filter = f"""AND "日期" <= '{end_date_slash}'""" - elif data_range_months > 0: - cutoff_date = (datetime.now(TAIPEI_TZ) - timedelta(days=data_range_months * 30)).strftime('%Y/%m/%d') - date_filter = f"""AND "日期" >= '{cutoff_date}'""" - - # V-Fix: 補上其他所有篩選條件 (與 get_sales_table_data 一致) - category_filter = request.args.get('category', 'all') - brand_filter = request.args.get('brand', 'all') - vendor_filter = request.args.get('vendor', 'all') - activity_filter = request.args.get('activity', 'all') - payment_filter = request.args.get('payment', 'all') - month_filter = request.args.get('month', 'all') - dow_filter = request.args.get('dow', 'all') - hour_filter = request.args.get('hour', 'all') - min_price_str = request.args.get('min_price', '') - max_price_str = request.args.get('max_price', '') - min_margin_str = request.args.get('min_margin', '') - max_margin_str = request.args.get('max_margin', '') - keyword = request.args.get('keyword', '').strip() - - additional_filters = [] - - if category_filter and category_filter != 'all': - additional_filters.append(f""""{col_category}" = '{category_filter}'""") - if brand_filter and brand_filter != 'all': - additional_filters.append(f""""{col_brand}" = '{brand_filter}'""") - if vendor_filter and vendor_filter != 'all': - additional_filters.append(f""""{col_vendor}" = '{vendor_filter}'""") - if activity_filter and activity_filter != 'all': - additional_filters.append(f""""{col_activity}" = '{activity_filter}'""") - if payment_filter and payment_filter != 'all': - additional_filters.append(f""""{col_payment}" = '{payment_filter}'""") - - # 時間維度 - if month_filter and month_filter != 'all': - month_filter_slash = month_filter.replace('-', '/') - # 使用「日期」欄位 (這似乎是系統內部固定欄位，不需 dynamic map，除非資料表結構也變了) - # 假設 "日期" 是固定欄位 - additional_filters.append(f"""("日期" LIKE '{month_filter}%' OR "日期" LIKE '{month_filter_slash}%')""") - - if dow_filter and dow_filter != 'all': - # V-Fix (2026-01-23): 支援 PostgreSQL 和 SQLite (top_detail API) - pandas_dow = int(dow_filter) - if DATABASE_TYPE == 'postgresql': - pg_dow = (pandas_dow + 1) % 7 - additional_filters.append(f"""EXTRACT(DOW FROM TO_DATE(REPLACE("日期", '/', '-'), 'YYYY-MM-DD')) = {pg_dow}""") - else: - sqlite_dow = str((pandas_dow + 1) % 7) - additional_filters.append(f"""strftime('%w', replace("日期", '/', '-')) = '{sqlite_dow}'""") - - if hour_filter and hour_filter != 'all': - # V-Fix (2026-01-23): 支援 PostgreSQL 和 SQLite (top_detail API) - hour_val = int(hour_filter) - if DATABASE_TYPE == 'postgresql': - additional_filters.append(f"""CAST(SUBSTRING("時間" FROM 1 FOR 2) AS INTEGER) = {hour_val}""") - else: - additional_filters.append(f"""CAST(substr("時間", 1, 2) AS INTEGER) = {hour_val}""") - - # 關鍵字 - if keyword: - keyword_escaped = keyword.replace("'", "''") - k_conds = [] - for col in [col_name, cols_map.get("pid", "商品ID"), col_brand, col_vendor]: - k_conds.append(f""""{col}" LIKE '%{keyword_escaped}%'""") - additional_filters.append(f"({' OR '.join(k_conds)})") - - if (min_price_str or max_price_str): - price_sql = f'CAST("{col_amount}" AS FLOAT) / NULLIF("{col_qty}", 0)' - if min_price_str: additional_filters.append(f"{price_sql} >= {float(min_price_str)}") - if max_price_str: additional_filters.append(f"{price_sql} <= {float(max_price_str)}") - - if (min_margin_str or max_margin_str): - if col_profit: - profit_sql = f'"{col_profit}"' - else: - profit_sql = f'("{col_amount}" - "{col_cost}")' - - margin_sql = f'({profit_sql} * 100.0 / NULLIF("{col_amount}", 0))' - if min_margin_str: additional_filters.append(f"{margin_sql} >= {float(min_margin_str)}") - if max_margin_str: additional_filters.append(f"{margin_sql} <= {float(max_margin_str)}") - - if additional_filters: - date_filter += " AND " + " AND ".join(additional_filters) - - # V-New: 準備利潤計算 SQL 片段 (SELECT 子句使用 SUM 聚合) - if col_profit: - profit_select_sql = f'SUM(CAST("{col_profit}" AS REAL))' - else: - profit_select_sql = f'SUM(CAST("{col_amount}" AS REAL)) - SUM(CAST("{col_cost}" AS REAL))' - - # 根據檢視類型和指標建立 SQL 查詢 - if view_type == 'category': - # 分類排行 - if metric == 'qty': - sql_query = f""" - SELECT - "{col_category}" as name, - SUM(CAST("{col_qty}" AS REAL)) as value - FROM {table_name} - WHERE "{col_category}" IS NOT NULL {date_filter} - GROUP BY "{col_category}" - ORDER BY value DESC - LIMIT 50 - """ - elif metric == 'profit': - sql_query = f""" - SELECT - "{col_category}" as name, - {profit_select_sql} as value, - CASE - WHEN SUM(CAST("{col_amount}" AS REAL)) > 0 - THEN (({profit_select_sql}) / SUM(CAST("{col_amount}" AS REAL))) * 100 - ELSE 0 - END as margin_rate - FROM {table_name} - WHERE "{col_category}" IS NOT NULL {date_filter} - GROUP BY "{col_category}" - ORDER BY value DESC - LIMIT 50 - """ - else: # amount - sql_query = f""" - SELECT - "{col_category}" as name, - SUM(CAST("{col_amount}" AS REAL)) as value - FROM {table_name} - WHERE "{col_category}" IS NOT NULL {date_filter} - GROUP BY "{col_category}" - ORDER BY value DESC - LIMIT 50 - """ - else: - # 商品排行（包含商品ID） - pid_col_sql = f'"{cols_map.get("pid", "商品ID")}"' # 商品ID 欄位 - if metric == 'qty': - sql_query = f""" - SELECT - {pid_col_sql} as product_id, - "{col_name}" as name, - "{col_brand}" as brand, - "{col_vendor}" as vendor, - "{col_category}" as category, - SUM(CAST("{col_qty}" AS REAL)) as value - FROM {table_name} - WHERE "{col_name}" IS NOT NULL {date_filter} - GROUP BY {pid_col_sql}, "{col_name}", "{col_brand}", "{col_vendor}", "{col_category}" - ORDER BY value DESC - LIMIT 100 - """ - elif metric == 'profit': - sql_query = f""" - SELECT - {pid_col_sql} as product_id, - "{col_name}" as name, - "{col_brand}" as brand, - "{col_vendor}" as vendor, - "{col_category}" as category, - {profit_select_sql} as value, - CASE - WHEN SUM(CAST("{col_amount}" AS REAL)) > 0 - THEN (({profit_select_sql}) / SUM(CAST("{col_amount}" AS REAL))) * 100 - ELSE 0 - END as margin_rate - FROM {table_name} - WHERE "{col_name}" IS NOT NULL {date_filter} - GROUP BY {pid_col_sql}, "{col_name}", "{col_brand}", "{col_vendor}", "{col_category}" - ORDER BY value DESC - LIMIT 100 - """ - else: # amount - sql_query = f""" - SELECT - {pid_col_sql} as product_id, - "{col_name}" as name, - "{col_brand}" as brand, - "{col_vendor}" as vendor, - "{col_category}" as category, - SUM(CAST("{col_amount}" AS REAL)) as value - FROM {table_name} - WHERE "{col_name}" IS NOT NULL {date_filter} - GROUP BY {pid_col_sql}, "{col_name}", "{col_brand}", "{col_vendor}", "{col_category}" - ORDER BY value DESC - LIMIT 100 - """ - - # 執行查詢 - df = pd.read_sql(sql_query, db.engine) - sys_log.info(f"[API] Top Detail: {top_type}/{view_type} 返回 {len(df)} 筆資料") - - if df.empty: - return jsonify({'items': []}) - - # V-Fix (2026-01-23): 確保數值欄位無 NaN/Infinity，避免 JSON 序列化失敗 - numeric_cols = ['value', 'margin_rate'] - for col in numeric_cols: - if col in df.columns: - df[col] = df[col].replace([np.inf, -np.inf], 0).fillna(0) - - # V-Fix (2026-01-23): 確保字串欄位無 None - string_cols = ['product_id', 'name', 'brand', 'vendor', 'category'] - for col in string_cols: - if col in df.columns: - df[col] = df[col].fillna('').astype(str) - - # 轉換為 JSON - items = df.to_dict('records') - return jsonify({'items': items}) - - except Exception as e: - sys_log.error(f"[API] Top Detail Error: {e}") - import traceback - traceback.print_exc() - return jsonify({'error': str(e)}), 500 - -@app.route('/api/sales_analysis/export_top_detail') -def export_top_detail(): - """API: 匯出 Top N 詳細列表為 Excel""" - try: - from datetime import datetime, timedelta, timezone - import io - TAIPEI_TZ = timezone(timedelta(hours=8)) - - table_name = 'realtime_sales_monthly' - data_range_months = int(request.args.get('data_range', '1') or '1') - start_date = request.args.get('start_date', '') # V-New: 自訂開始日期 - end_date = request.args.get('end_date', '') # V-New: 自訂結束日期 - top_type = request.args.get('type', 'revenue') - metric = request.args.get('metric', 'amount') - view_type = request.args.get('view', 'product') - - db = DatabaseManager() - - # V-Fix: 從快取讀取欄位名稱對應，以支援不同的資料庫欄位名稱 - if start_date or end_date: - cache_key = f"{table_name}_custom_{start_date}_{end_date}" - else: - cache_key = f"{table_name}_{data_range_months}m" - - # 嘗試從快取讀取欄位名稱 - cols_map = {} - if cache_key in _SALES_PROCESSED_CACHE: - cols_map = _SALES_PROCESSED_CACHE[cache_key].get('cols', {}) - elif table_name in _SALES_PROCESSED_CACHE: # V-Fix: 也嘗試使用固定 key - cols_map = _SALES_PROCESSED_CACHE[table_name].get('cols', {}) - - # 取得實際欄位名稱（如果快取中沒有，使用預設名稱） - # V-Fix (2026-01-23): 使用 or 確保不會得到 None 值 - col_name = cols_map.get('name') or '商品名稱' - col_brand = cols_map.get('brand') or '品牌' - col_vendor = cols_map.get('vendor') or '廠商名稱' - col_category = cols_map.get('category') or '商品館' - col_amount = cols_map.get('amount') or '總業績' - col_qty = cols_map.get('qty') or '數量' - col_cost = cols_map.get('cost') or '總成本' - col_profit = cols_map.get('profit') # 可以為 None - col_activity = cols_map.get('activity') or '活動名稱' - col_payment = cols_map.get('payment') or '付款方式' - - # 建立日期篩選條件 - date_filter = "" - # V-New: 優先處理自訂日期區間 - if start_date or end_date: - # V-Fix: 處理日期格式轉換 (2025-01-01 -> 2025/01/01) - start_date_slash = start_date.replace('-', '/') if start_date else '' - end_date_slash = end_date.replace('-', '/') if end_date else '' - - if start_date and end_date: - date_filter = f"""AND "日期" BETWEEN '{start_date_slash}' AND '{end_date_slash}'""" - elif start_date: - date_filter = f"""AND "日期" >= '{start_date_slash}'""" - else: # only end_date - date_filter = f"""AND "日期" <= '{end_date_slash}'""" - elif data_range_months > 0: - cutoff_date = (datetime.now(TAIPEI_TZ) - timedelta(days=data_range_months * 30)).strftime('%Y/%m/%d') - date_filter = f"""AND "日期" >= '{cutoff_date}'""" - - # V-Fix: 補上其他所有篩選條件 (與 get_top_detail 一致) - category_filter = request.args.get('category', 'all') - brand_filter = request.args.get('brand', 'all') - vendor_filter = request.args.get('vendor', 'all') - activity_filter = request.args.get('activity', 'all') - payment_filter = request.args.get('payment', 'all') - month_filter = request.args.get('month', 'all') - dow_filter = request.args.get('dow', 'all') - hour_filter = request.args.get('hour', 'all') - min_price_str = request.args.get('min_price', '') - max_price_str = request.args.get('max_price', '') - min_margin_str = request.args.get('min_margin', '') - max_margin_str = request.args.get('max_margin', '') - keyword = request.args.get('keyword', '').strip() - - additional_filters = [] - - if category_filter and category_filter != 'all': - additional_filters.append(f""""{col_category}" = '{category_filter}'""") - if brand_filter and brand_filter != 'all': - additional_filters.append(f""""{col_brand}" = '{brand_filter}'""") - if vendor_filter and vendor_filter != 'all': - additional_filters.append(f""""{col_vendor}" = '{vendor_filter}'""") - if activity_filter and activity_filter != 'all': - additional_filters.append(f""""{col_activity}" = '{activity_filter}'""") - if payment_filter and payment_filter != 'all': - additional_filters.append(f""""{col_payment}" = '{payment_filter}'""") - - if month_filter and month_filter != 'all': - month_filter_slash = month_filter.replace('-', '/') - additional_filters.append(f"""("日期" LIKE '{month_filter}%' OR "日期" LIKE '{month_filter_slash}%')""") - - if dow_filter and dow_filter != 'all': - # V-Fix (2026-01-23): 支援 PostgreSQL 和 SQLite (export API) - pandas_dow = int(dow_filter) - if DATABASE_TYPE == 'postgresql': - pg_dow = (pandas_dow + 1) % 7 - additional_filters.append(f"""EXTRACT(DOW FROM TO_DATE(REPLACE("日期", '/', '-'), 'YYYY-MM-DD')) = {pg_dow}""") - else: - sqlite_dow = str((pandas_dow + 1) % 7) - additional_filters.append(f"""strftime('%w', replace("日期", '/', '-')) = '{sqlite_dow}'""") - - if hour_filter and hour_filter != 'all': - # V-Fix (2026-01-23): 支援 PostgreSQL 和 SQLite (export API) - hour_val = int(hour_filter) - if DATABASE_TYPE == 'postgresql': - additional_filters.append(f"""CAST(SUBSTRING("時間" FROM 1 FOR 2) AS INTEGER) = {hour_val}""") - else: - additional_filters.append(f"""CAST(substr("時間", 1, 2) AS INTEGER) = {hour_val}""") - - if keyword: - keyword_escaped = keyword.replace("'", "''") - k_conds = [] - for col in [col_name, cols_map.get("pid", "商品ID"), col_brand, col_vendor]: - k_conds.append(f""""{col}" LIKE '%{keyword_escaped}%'""") - additional_filters.append(f"({' OR '.join(k_conds)})") - - if (min_price_str or max_price_str): - price_sql = f'CAST("{col_amount}" AS FLOAT) / NULLIF("{col_qty}", 0)' - if min_price_str: additional_filters.append(f"{price_sql} >= {float(min_price_str)}") - if max_price_str: additional_filters.append(f"{price_sql} <= {float(max_price_str)}") - - if (min_margin_str or max_margin_str): - if col_profit: - profit_sql = f'"{col_profit}"' - else: - profit_sql = f'("{col_amount}" - "{col_cost}")' - - margin_sql = f'({profit_sql} * 100.0 / NULLIF("{col_amount}", 0))' - if min_margin_str: additional_filters.append(f"{margin_sql} >= {float(min_margin_str)}") - if max_margin_str: additional_filters.append(f"{margin_sql} <= {float(max_margin_str)}") - - if additional_filters: - date_filter += " AND " + " AND ".join(additional_filters) - - # V-New: 準備利潤計算 SQL 片段 (SELECT 子句使用 SUM 聚合) - if col_profit: - profit_select_sql = f'SUM(CAST("{col_profit}" AS REAL))' - else: - profit_select_sql = f'SUM(CAST("{col_amount}" AS REAL)) - SUM(CAST("{col_cost}" AS REAL))' - - # 根據檢視類型和指標建立 SQL 查詢（與上面相同） - if view_type == 'category': - if metric == 'qty': - sql_query = f""" - SELECT - "{col_category}" as 分類名稱, - SUM(CAST("{col_qty}" AS REAL)) as 銷售數量 - FROM {table_name} - WHERE "{col_category}" IS NOT NULL {date_filter} - GROUP BY "{col_category}" - ORDER BY 銷售數量 DESC - LIMIT 50 - """ - elif metric == 'profit': - sql_query = f""" - SELECT - "{col_category}" as 分類名稱, - {profit_select_sql} as 毛利金額, - CASE - WHEN SUM(CAST("{col_amount}" AS REAL)) > 0 - THEN (({profit_select_sql}) / SUM(CAST("{col_amount}" AS REAL))) * 100 - ELSE 0 - END as 毛利率 - FROM {table_name} - WHERE "{col_category}" IS NOT NULL {date_filter} - GROUP BY "{col_category}" - ORDER BY 毛利金額 DESC - LIMIT 50 - """ - else: # amount - sql_query = f""" - SELECT - "{col_category}" as 分類名稱, - SUM(CAST("{col_amount}" AS REAL)) as 銷售金額 - FROM {table_name} - WHERE "{col_category}" IS NOT NULL {date_filter} - GROUP BY "{col_category}" - ORDER BY 銷售金額 DESC - LIMIT 50 - """ - else: - # 商品排行（包含商品ID） - if metric == 'qty': - sql_query = f""" - SELECT - "{cols_map.get("pid", "商品ID")}" as 商品ID, - "{col_name}" as 商品名稱, - "{col_brand}" as 品牌, - "{col_vendor}" as 廠商名稱, - "{col_category}" as 分類名稱, - SUM(CAST("{col_qty}" AS REAL)) as 銷售數量 - FROM {table_name} - WHERE "{col_name}" IS NOT NULL {date_filter} - GROUP BY "{cols_map.get("pid", "商品ID")}", "{col_name}", "{col_brand}", "{col_vendor}", "{col_category}" - ORDER BY 銷售數量 DESC - LIMIT 100 - """ - elif metric == 'profit': - sql_query = f""" - SELECT - "{cols_map.get("pid", "商品ID")}" as 商品ID, - "{col_name}" as 商品名稱, - "{col_brand}" as 品牌, - "{col_vendor}" as 廠商名稱, - "{col_category}" as 分類名稱, - {profit_select_sql} as 毛利金額, - CASE - WHEN SUM(CAST("{col_amount}" AS REAL)) > 0 - THEN (({profit_select_sql}) / SUM(CAST("{col_amount}" AS REAL))) * 100 - ELSE 0 - END as 毛利率 - FROM {table_name} - WHERE "{col_name}" IS NOT NULL {date_filter} - GROUP BY "{cols_map.get("pid", "商品ID")}", "{col_name}", "{col_brand}", "{col_vendor}", "{col_category}" - ORDER BY 毛利金額 DESC - LIMIT 100 - """ - else: # amount - sql_query = f""" - SELECT - "{cols_map.get("pid", "商品ID")}" as 商品ID, - "{col_name}" as 商品名稱, - "{col_brand}" as 品牌, - "{col_vendor}" as 廠商名稱, - "{col_category}" as 分類名稱, - SUM(CAST("{col_amount}" AS REAL)) as 銷售金額 - FROM {table_name} - WHERE "{col_name}" IS NOT NULL {date_filter} - GROUP BY "{cols_map.get("pid", "商品ID")}", "{col_name}", "{col_brand}", "{col_vendor}", "{col_category}" - ORDER BY 銷售金額 DESC - LIMIT 100 - """ - - # 執行查詢並匯出 - df = pd.read_sql(sql_query, db.engine) - - if df.empty: - return "無資料可匯出", 400 - - # 生成 Excel - output = io.BytesIO() - with pd.ExcelWriter(output, engine='openpyxl') as writer: - df.to_excel(writer, index=False, sheet_name='Top排行') - output.seek(0) - - # 生成檔案名稱 - type_names = {'revenue': '業績貢獻王', 'margin': '獲利金雞母', 'quantity': '人氣引流款'} - view_names = {'product': '商品排行', 'category': '分類排行'} - filename = f"{type_names.get(top_type, '排行')}_{view_names.get(view_type, '')}_{datetime.now(TAIPEI_TZ).strftime('%Y%m%d_%H%M')}.xlsx" - - sys_log.info(f"[Export] Top Detail: {filename} ({len(df)} 筆)") - - return send_file( - output, - as_attachment=True, - download_name=filename, - mimetype='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet' - ) - - except Exception as e: - sys_log.error(f"[Export] Top Detail Error: {e}") - return f"匯出失敗: {e}", 500 # ================= 📈 V-New: 年度對比 (Year-over-Year Comparison) ================= -@app.route('/api/sales_analysis/yoy_comparison') -def yoy_comparison(): - """ - API: 年度對比分析 (YoY Comparison) - - 參數: - year1: 基準年 (例如 2024) - year2: 對比年 (例如 2025) - month: 月份 (可選，1-12，不帶則為全年) - metric: 指標 (revenue/qty/profit) - - 回傳: - JSON with year1 total, year2 total, growth rate, and monthly breakdown - """ - try: - from datetime import datetime, timedelta, timezone - TAIPEI_TZ = timezone(timedelta(hours=8)) - - table_name = 'realtime_sales_monthly' - year1 = request.args.get('year1', '2024') - year2 = request.args.get('year2', '2025') - month = request.args.get('month', '') # 可選，1-12 - metric = request.args.get('metric', 'revenue') # revenue/qty/profit - - db = DatabaseManager() - - # 欄位名稱 - col_amount = '總業績' - col_qty = '數量' - col_cost = '總成本' - col_date = '日期' - - # 根據指標決定聚合欄位 - if metric == 'qty': - agg_sql = f'SUM(CAST("{col_qty}" AS REAL))' - metric_label = '銷售數量' - elif metric == 'profit': - agg_sql = f'SUM(CAST("{col_amount}" AS REAL)) - SUM(CAST("{col_cost}" AS REAL))' - metric_label = '毛利金額' - else: # revenue - agg_sql = f'SUM(CAST("{col_amount}" AS REAL))' - metric_label = '銷售金額' - - # 建立年度篩選條件 - # 日期格式為 2025/01/01 或 2025-01-01 - def build_year_filter(year, month_filter=''): - if month_filter: - month_str = month_filter.zfill(2) - return f"""("{col_date}" LIKE '{year}/{month_str}%' OR "{col_date}" LIKE '{year}-{month_str}%')""" - else: - return f"""("{col_date}" LIKE '{year}/%' OR "{col_date}" LIKE '{year}-%')""" - - year1_filter = build_year_filter(year1, month) - year2_filter = build_year_filter(year2, month) - - # 查詢年度總計 - sql_year1 = f""" - SELECT {agg_sql} as total - FROM {table_name} - WHERE {year1_filter} - """ - sql_year2 = f""" - SELECT {agg_sql} as total - FROM {table_name} - WHERE {year2_filter} - """ - - # V-Fix: SQLAlchemy 2.0 需要使用 text() 包裹 SQL 字串 - from sqlalchemy import text - result_year1 = pd.read_sql(text(sql_year1), db.engine) - result_year2 = pd.read_sql(text(sql_year2), db.engine) - - total_year1 = float(result_year1['total'].iloc[0] or 0) - total_year2 = float(result_year2['total'].iloc[0] or 0) - - # 計算成長率 - if total_year1 > 0: - growth_rate = ((total_year2 - total_year1) / total_year1) * 100 - else: - growth_rate = 0 if total_year2 == 0 else 100 - - # 月度明細 (如果沒有指定月份，則查詢 12 個月的明細) - monthly_breakdown = [] - if not month: - for m in range(1, 13): - m_str = str(m).zfill(2) - y1_filter = build_year_filter(year1, m_str) - y2_filter = build_year_filter(year2, m_str) - - sql_m1 = f"SELECT {agg_sql} as total FROM {table_name} WHERE {y1_filter}" - sql_m2 = f"SELECT {agg_sql} as total FROM {table_name} WHERE {y2_filter}" - - # V-Fix: SQLAlchemy 2.0 需要使用 text() - r1 = pd.read_sql(text(sql_m1), db.engine) - r2 = pd.read_sql(text(sql_m2), db.engine) - - v1 = float(r1['total'].iloc[0] or 0) - v2 = float(r2['total'].iloc[0] or 0) - - m_growth = ((v2 - v1) / v1 * 100) if v1 > 0 else (0 if v2 == 0 else 100) - - monthly_breakdown.append({ - 'month': m, - 'month_label': f'{m}月', - 'year1_value': v1, - 'year2_value': v2, - 'growth_rate': round(m_growth, 2) - }) - - response = { - 'year1': { - 'label': f'{year1}年' + (f'{month}月' if month else ''), - 'total': total_year1 - }, - 'year2': { - 'label': f'{year2}年' + (f'{month}月' if month else ''), - 'total': total_year2 - }, - 'growth_rate': round(growth_rate, 2), - 'metric': metric, - 'metric_label': metric_label, - 'monthly_breakdown': monthly_breakdown - } - - sys_log.info(f"[YoY] {year1} vs {year2}: {total_year1:,.0f} -> {total_year2:,.0f} ({growth_rate:+.1f}%)") - - return jsonify(response) - - except Exception as e: - sys_log.error(f"[YoY] Error: {e}") - traceback.print_exc() - return jsonify({'error': str(e)}), 500 # ================= 📈 V-New: 營運成長報表 (Growth Strategy) ================= -@app.route('/growth_analysis') -def growth_analysis(): - """營運成長策略報表 (MoM, YoY, AOV, YTD)""" - try: - from sqlalchemy import text as sa_text - db = DatabaseManager() - table_name = 'realtime_sales_monthly' - - # 1. 檢查資料表 - inspector = inspect(db.engine) - if table_name not in inspector.get_table_names(): - return f"尚未匯入業績資料 ({table_name})", 404 - - # 2. SQL 月度聚合（避免全量 748k 行讀進 pandas） - monthly_sql = sa_text(""" - SELECT - date_trunc('month', TO_DATE("日期", 'YYYY/MM/DD'))::date AS month, - SUM(COALESCE(NULLIF("總業績", '')::numeric, 0)) AS amount, - SUM(COALESCE(NULLIF("總成本", '')::numeric, 0)) AS cost, - COUNT(DISTINCT "訂單編號") AS orders - FROM realtime_sales_monthly - WHERE "日期" IS NOT NULL AND length("日期") >= 8 - GROUP BY 1 - ORDER BY 1 - """) - ytd_sql = sa_text(""" - SELECT - EXTRACT(YEAR FROM TO_DATE("日期", 'YYYY/MM/DD'))::int AS yr, - EXTRACT(DOY FROM TO_DATE("日期", 'YYYY/MM/DD'))::int AS doy, - SUM(COALESCE(NULLIF("總業績", '')::numeric, 0)) AS amount - FROM realtime_sales_monthly - WHERE "日期" IS NOT NULL AND length("日期") >= 8 - AND EXTRACT(YEAR FROM TO_DATE("日期", 'YYYY/MM/DD')) - >= EXTRACT(YEAR FROM CURRENT_DATE) - 1 - GROUP BY 1, 2 - ORDER BY 1, 2 - """) - recent_sql = sa_text(""" - SELECT - SUM(COALESCE(NULLIF("總業績", '')::numeric, 0)) AS revenue, - COUNT(DISTINCT "訂單編號") AS orders - FROM realtime_sales_monthly - WHERE "日期" IS NOT NULL AND length("日期") >= 8 - AND TO_DATE("日期", 'YYYY/MM/DD') >= CURRENT_DATE - INTERVAL '30 days' - """) - - with db.engine.connect() as conn: - monthly_df = pd.read_sql(monthly_sql, conn) - if monthly_df.empty: - return f"資料表 {table_name} 為空", 404 - ytd_df = pd.read_sql(ytd_sql, conn) - recent_r = conn.execute(recent_sql).fetchone() - - # 3. 月度指標計算 - monthly_df['month'] = pd.to_datetime(monthly_df['month']) - monthly_df = monthly_df.set_index('month') - monthly_df['profit'] = monthly_df['amount'] - monthly_df['cost'] - monthly_df['aov'] = monthly_df['amount'] / monthly_df['orders'].replace(0, pd.NA) - monthly_df['margin_rate'] = (monthly_df['profit'] / monthly_df['amount'].replace(0, pd.NA)) * 100 - monthly_df['mom'] = monthly_df['amount'].pct_change() * 100 - monthly_df['yoy'] = monthly_df['amount'].pct_change(periods=12) * 100 - monthly_df = monthly_df.fillna(0) - - labels = monthly_df.index.strftime('%Y-%m').tolist() - chart_data = { - 'labels': labels, - 'revenue': monthly_df['amount'].tolist(), - 'profit': monthly_df['profit'].tolist(), - 'orders': monthly_df['orders'].tolist(), - 'aov': monthly_df['aov'].round(0).tolist(), - 'mom': monthly_df['mom'].round(2).tolist(), - 'yoy': monthly_df['yoy'].round(2).tolist(), - 'margin_rate': monthly_df['margin_rate'].round(1).tolist(), - } - - # 4. KPI: YTD - current_year = int(pd.Timestamp.now().year) - last_year = current_year - 1 - max_doy = ytd_df[ytd_df['yr'] == current_year]['doy'].max() if not ytd_df.empty else 365 - - ytd_revenue = float(ytd_df[ytd_df['yr'] == current_year]['amount'].sum()) - last_ytd_revenue = float(ytd_df[(ytd_df['yr'] == last_year) & (ytd_df['doy'] <= max_doy)]['amount'].sum()) - ytd_growth = ((ytd_revenue - last_ytd_revenue) / last_ytd_revenue * 100) if last_ytd_revenue > 0 else 0 - - recent_revenue = float(recent_r.revenue or 0) if recent_r else 0 - recent_orders = int(recent_r.orders or 0) if recent_r else 0 - recent_aov = recent_revenue / recent_orders if recent_orders > 0 else 0 - - kpi = { - 'ytd_revenue': ytd_revenue, - 'ytd_growth': ytd_growth, - 'current_year': current_year, - 'recent_aov': recent_aov, - 'total_orders': int(monthly_df['orders'].sum()), - } - - return render_template('growth_analysis.html', chart_data=chart_data, kpi=kpi) - - except Exception as e: - sys_log.error(f"Growth Analysis Error: {e}") - return f"系統錯誤: {e}" def preprocess_daily_sales_data(df): """前處理當日業績資料：欄位識別、型別轉換""" diff --git a/routes/export_routes.py b/routes/export_routes.py index c7ef5c2..7f180e1 100644 --- a/routes/export_routes.py +++ b/routes/export_routes.py @@ -583,7 +583,7 @@ def export_vendor_analysis(): else: params = {k: v for k, v in request.args.items()} flash('資料快取已失效，請稍候重新載入資料後再匯出。', 'warning') - return redirect(url_for('sales_analysis', **params)) + return redirect(url_for('sales.sales_analysis', **params)) col_vendor = cols_map.get('vendor') col_amount = cols_map.get('amount') diff --git a/routes/sales_routes.py b/routes/sales_routes.py index aeed001..3969847 100644 --- a/routes/sales_routes.py +++ b/routes/sales_routes.py @@ -8,16 +8,23 @@ 為避免循環依賴，部分函數使用延遲導入 """ +import hashlib +import io +import math import time +import traceback from datetime import datetime, timezone, timedelta -from flask import Blueprint, request, render_template, jsonify +from flask import Blueprint, request, render_template, jsonify, send_file, redirect, url_for from auth import login_required from sqlalchemy import inspect, text import pandas as pd +import numpy as np -from config import BASE_DIR +from config import BASE_DIR, DATABASE_TYPE from database.manager import DatabaseManager from services.logger_manager import SystemLogger +from services.daily_sales_service import prepare_marketing_summary +from utils.text_helpers import get_color_for_string # 時區設定 TAIPEI_TZ = timezone(timedelta(hours=8)) @@ -32,6 +39,10 @@ sales_bp = Blueprint('sales', __name__) _SALES_DF_CACHE = {} _SALES_PROCESSED_CACHE = {} _SALES_OPTIONS_CACHE = {} +_SALES_CACHE_MAX_ENTRIES = 10 +_SALES_CACHE_TTL = 600 +_TABLE_DATA_CACHE = {} +_TABLE_DATA_CACHE_TTL = 60 # ========================================== @@ -43,14 +54,43 @@ from utils.df_helpers import find_col # noqa: E402, F401 from utils.security import validate_table_name, safe_read_sql # noqa: E402, F401 +def _cleanup_sales_cache(): + """清理過期和過多的快取條目""" + global _SALES_PROCESSED_CACHE + current_time = time.time() + + # 1. 清理過期條目 + expired_keys = [ + k for k, v in _SALES_PROCESSED_CACHE.items() + if v.get('time') and current_time - v['time'] > _SALES_CACHE_TTL + ] + for k in expired_keys: + del _SALES_PROCESSED_CACHE[k] + + # 2. 如果仍超過限制，刪除最舊的條目 + if len(_SALES_PROCESSED_CACHE) > _SALES_CACHE_MAX_ENTRIES: + sorted_items = sorted( + [(k, v.get('time', 0)) for k, v in _SALES_PROCESSED_CACHE.items()], + key=lambda x: x[1] + ) + # 保留最新的 _SALES_CACHE_MAX_ENTRIES 條 + keys_to_delete = [k for k, _ in sorted_items[:-_SALES_CACHE_MAX_ENTRIES]] + for k in keys_to_delete: + del _SALES_PROCESSED_CACHE[k] + + if expired_keys or len(_SALES_PROCESSED_CACHE) > _SALES_CACHE_MAX_ENTRIES - 2: + sys_log.debug(f"[Cache] 清理快取: 移除 {len(expired_keys)} 條過期, 剩餘 {len(_SALES_PROCESSED_CACHE)} 條") + + def _get_filtered_sales_data(cache_key): """ - 共用函式：從快取讀取資料並根據 request.args 進行篩選 + 🚩 共用函式：從快取讀取資料並根據 request.args 進行篩選 回傳: (target_df, cols_map, error_message) + 參數: cache_key - 快取鍵值 (例如: "realtime_sales_monthly_3m") """ db = DatabaseManager() - table_name = 'realtime_sales_monthly' + # 1. 檢查資料表與快取 df = None cols_map = {} @@ -59,31 +99,67 @@ def _get_filtered_sales_data(cache_key): df = cache_data['df'] cols_map = cache_data['cols'] else: - sys_log.warning(f"[Sales Analysis] 快取不存在 ({cache_key})，試圖重新從資料庫載入...") + # 快取不存在時，直接回傳錯誤讓呼叫端顯示 spinner 導回 sales_analysis + # 不在此發起全表 DB 查詢（748k 行會 hang Gunicorn worker） + sys_log.warning(f"[Sales Analysis] ⚠️ 快取不存在 ({cache_key})，回傳錯誤讓 UI 導回 sales_analysis") + return None, {}, f"快取未就緒，請先從業績分析主頁載入資料 (cache_key={cache_key})" + + if False: # 保留舊冷快取重載邏輯（已停用，避免全表掃描 hang） + sys_log.warning(f"[Sales Analysis] ⚠️ 快取不存在 ({cache_key})，試圖重新從資料庫載入...") try: + # V-Fix: 從 cache_key 提取 table_name + # 格式: realtime_sales_monthly_3m 或 realtime_sales_monthly_custom_2025-01-01_2025-01-31 if "_custom_" in cache_key: + table_name = cache_key.split('_custom_')[0] # realtime_sales_monthly + else: + # 移除最後的 _Xm 部分 + parts = cache_key.rsplit('_', 1) + table_name = parts[0] if len(parts) > 1 else 'realtime_sales_monthly' + + # 判斷是自訂區間還是標配區間 + if "_custom_" in cache_key: + # 格式: realtime_sales_monthly_custom_2025-01-01_2025-01-31 parts = cache_key.split('_custom_') dates = parts[1].split('_') start_d, end_d = dates[0], dates[1] + # 呼叫資料庫讀取 (不傳入 view, 會自動處理欄位映射) result_df, result_cols = db.get_sales_data(table_name=table_name, start_date=start_d, end_date=end_d) else: - months = int(cache_key.split('_')[-1].replace('m', '') or '1') + # 格式: realtime_sales_monthly_1m；months=0 表示全時段但上限 12 個月避免全表掃描 hang + months = int(cache_key.split('_')[-1].replace('m', '') or '12') + if months == 0: + months = 12 result_df, result_cols = db.get_sales_data(table_name=table_name, months=months) - + if result_df is not None and not result_df.empty: + # V-Fix (2026-01-23): 補回所有日期維度欄位供後續篩選 (_dow, _hour, _month_str) if '日期' in result_df.columns: - result_df['_month_str'] = pd.to_datetime(result_df['日期']).dt.strftime('%Y-%m') + # 先轉換為 datetime + result_df['_parsed_date'] = pd.to_datetime(result_df['日期'], errors='coerce') + result_df['_month_str'] = result_df['_parsed_date'].dt.strftime('%Y-%m') + result_df['_dow'] = result_df['_parsed_date'].dt.dayofweek + # 小時需要從「時間」欄位提取 + if '時間' in result_df.columns: + result_df['_hour'] = pd.to_datetime(result_df['時間'], format='%H:%M:%S', errors='coerce').dt.hour + else: + result_df['_hour'] = 0 # 如果沒有時間欄位，預設為 0 + + # 清理臨時欄位 + result_df.drop(columns=['_parsed_date'], inplace=True, errors='ignore') + + # 自動存入快取 _SALES_PROCESSED_CACHE[cache_key] = {'df': result_df, 'cols': result_cols, 'time': time.time()} df = result_df cols_map = result_cols - sys_log.info(f"[Sales Analysis] 快取成功自動重載 | 筆數: {len(df)}") + sys_log.info(f"[Sales Analysis] ✅ 快取成功自動重載 | 筆數: {len(df)}") else: return None, None, "資料庫無可用資料，請確認匯入狀態" except Exception as ex: - sys_log.error(f"[Sales Analysis] 自動重載失敗: {ex}") + sys_log.error(f"[Sales Analysis] 🚨 自動重載失敗: {ex}") return None, None, f"快取失效且無法重載: {ex}" + # 恢復欄位變數 col_name = cols_map.get('name') col_category = cols_map.get('category') col_brand = cols_map.get('brand') @@ -92,7 +168,9 @@ def _get_filtered_sales_data(cache_key): col_payment = cols_map.get('payment') col_price = cols_map.get('price') col_date = cols_map.get('date') + col_return_qty = cols_map.get('return_qty') # V-New: 取得退貨欄位 + # 2. 取得篩選參數 selected_category = request.args.get('category', 'all') selected_brand = request.args.get('brand', 'all') selected_vendor = request.args.get('vendor', 'all') @@ -107,62 +185,41 @@ def _get_filtered_sales_data(cache_key): min_margin = request.args.get('min_margin', '') max_margin = request.args.get('max_margin', '') + # 3. 執行篩選 target_df = df - + + # Top N 分類處理 (用於 '其他' 篩選) TOP_N_CATS = 12 top_cats_names = [] if col_category: + # 注意：這裡為了效能，簡單重算一次 Top N，或可考慮也快取起來 cat_group_all = df.groupby(col_category)[cols_map.get('amount')].sum().sort_values(ascending=False) if len(cat_group_all) > TOP_N_CATS: top_cats_names = cat_group_all.head(TOP_N_CATS).index.tolist() if selected_category != 'all' and col_category: - cache_data = _SALES_PROCESSED_CACHE.get(cache_key, {}) - top_cats_names = cache_data.get('top_cats') - - if top_cats_names is None: - cat_group_all = df.groupby(col_category)[cols_map.get('amount')].sum().sort_values(ascending=False) - if len(cat_group_all) > TOP_N_CATS: - top_cats_names = cat_group_all.head(TOP_N_CATS).index.tolist() - else: - top_cats_names = [] - if cache_key in _SALES_PROCESSED_CACHE: - _SALES_PROCESSED_CACHE[cache_key]['top_cats'] = top_cats_names - if selected_category == '其他' and top_cats_names: target_df = target_df[~target_df[col_category].isin(top_cats_names)] else: target_df = target_df[target_df[col_category] == selected_category] - - if selected_brand != 'all' and col_brand: - target_df = target_df[target_df[col_brand] == selected_brand] - if selected_vendor != 'all' and col_vendor: - target_df = target_df[target_df[col_vendor] == selected_vendor] - if selected_activity != 'all' and col_activity: - target_df = target_df[target_df[col_activity] == selected_activity] - if selected_payment != 'all' and col_payment: - target_df = target_df[target_df[col_payment] == selected_payment] - - if selected_dow != 'all' and col_date: - target_df = target_df[target_df['_dow'] == int(selected_dow)] - if selected_hour != 'all' and col_date: - target_df = target_df[target_df['_hour'] == int(selected_hour)] - if selected_month != 'all' and col_date: - target_df = target_df[target_df['_month_str'] == selected_month] - - if keyword: - target_df = target_df[target_df[col_name].astype(str).str.contains(keyword, case=False, na=False)] + + if selected_brand != 'all' and col_brand: target_df = target_df[target_df[col_brand] == selected_brand] + if selected_vendor != 'all' and col_vendor: target_df = target_df[target_df[col_vendor] == selected_vendor] + if selected_activity != 'all' and col_activity: target_df = target_df[target_df[col_activity] == selected_activity] + if selected_payment != 'all' and col_payment: target_df = target_df[target_df[col_payment] == selected_payment] + + if selected_dow != 'all' and col_date: target_df = target_df[target_df['_dow'] == int(selected_dow)] + if selected_hour != 'all' and col_date: target_df = target_df[target_df['_hour'] == int(selected_hour)] + if selected_month != 'all' and col_date: target_df = target_df[target_df['_month_str'] == selected_month] + + if keyword: target_df = target_df[target_df[col_name].astype(str).str.contains(keyword, case=False, na=False)] if col_price: - if min_price: - target_df = target_df[target_df[col_price] >= float(min_price)] - if max_price: - target_df = target_df[target_df[col_price] <= float(max_price)] + if min_price: target_df = target_df[target_df[col_price] >= float(min_price)] + if max_price: target_df = target_df[target_df[col_price] <= float(max_price)] - if min_margin: - target_df = target_df[target_df['calculated_margin_rate'] >= float(min_margin)] - if max_margin: - target_df = target_df[target_df['calculated_margin_rate'] <= float(max_margin)] + if min_margin: target_df = target_df[target_df['calculated_margin_rate'] >= float(min_margin)] + if max_margin: target_df = target_df[target_df['calculated_margin_rate'] <= float(max_margin)] return target_df, cols_map, None @@ -174,12 +231,1069 @@ def _get_filtered_sales_data(cache_key): @sales_bp.route('/sales_analysis') @login_required def sales_analysis(): - """業績分析儀表板""" - # 延遲導入以避免循環依賴 - # 此路由過於複雜，建議保留在 app.py 中 - # 這裡提供一個簡化版本的框架 - from app import sales_analysis as _original_sales_analysis - return _original_sales_analysis() + """業績分析報表頁面""" + try: + db = DatabaseManager() + table_name = 'realtime_sales_monthly' + + # 1. 檢查資料表是否存在 + inspector = inspect(db.engine) + if table_name not in inspector.get_table_names(): + return render_template('sales_analysis.html', + error="尚未匯入「即時業績(全月)」資料，請先至設定頁面匯入 Excel。", + table_name=table_name, + selected_metric='amount', + no_filter=False, + data_range_months=0, + start_date='', + end_date='', + total_records=0, + db_data_range='') + + # V-New: 查詢資料庫的資料期間範圍 + db_data_range = '' + try: + # 取得日期欄位的最小值和最大值 + from sqlalchemy import text + date_query = text(f"SELECT MIN(日期) as min_date, MAX(日期) as max_date FROM {table_name}") + # V-Fix: SQLAlchemy 2.0 需要使用 connection 對象 + with db.engine.connect() as conn: + result = conn.execute(date_query).fetchone() + if result and result[0] and result[1]: + min_date = result[0] + max_date = result[1] + # 格式化為 YYYY年MM月 格式 + if isinstance(min_date, str): + from datetime import datetime + try: + min_date_obj = datetime.strptime(min_date.split()[0], '%Y-%m-%d') + max_date_obj = datetime.strptime(max_date.split()[0], '%Y-%m-%d') + db_data_range = f"{min_date_obj.year}年{min_date_obj.month}月 ~ {max_date_obj.year}年{max_date_obj.month}月" + except: + db_data_range = f"{min_date} ~ {max_date}" + else: + db_data_range = f"{min_date.year}年{min_date.month}月 ~ {max_date.year}年{max_date.month}月" + except Exception as e: + sys_log.warning(f"[Sales Analysis] 無法取得資料期間範圍: {e}") + + # V-New: 取得篩選參數 + data_range_param = request.args.get('data_range', '') # 不再設預設值 + start_date = request.args.get('start_date', '') + end_date = request.args.get('end_date', '') + + # V-New: 按需載入 - 如果沒有任何篩選條件，顯示引導頁面 + if not data_range_param and not start_date and not end_date: + sys_log.info("[Sales Analysis] 👋 首次進入頁面，等待用戶選擇篩選條件") + + # V-Fix: 即使在引導頁面，也要提供下拉選單選項 + # V-Opt: 讀取較多筆數（1000筆）以獲得更完整的月份資訊 + preview_df = pd.read_sql(f"SELECT * FROM {table_name} LIMIT 1000", db.engine) + preview_categories = [] + preview_brands = [] + preview_vendors = [] + preview_activities = [] + preview_payments = [] + preview_months = [] # V-New: 新增月份列表 + + if not preview_df.empty: + cols = preview_df.columns.tolist() + def find_col(keywords): + for k in keywords: + for col in cols: + if k in str(col): return col + return None + + col_category = find_col(['館別', '商品館', '分類', 'Category']) + col_brand = find_col(['品牌', 'Brand']) + col_vendor = find_col(['廠商名稱', 'Vendor Name', '廠商', '供應商', 'Vendor', 'Supplier']) + # V-Fix: 優先匹配具體的活動欄位名稱 + col_activity = find_col(['折扣活動名稱', '折價券活動名稱', '滿額再折扣活動名稱', '活動', 'Activity', 'Campaign']) + col_payment = find_col(['付款', 'Payment', 'Pay']) + # V-Fix: 優先匹配「日期」欄位（「訂單日期」是固定文字，不是實際日期） + col_date_part = find_col(['日期', '交易日期', 'Date', 'Day']) + col_time_part = find_col(['訂單時間', '成立時間', '下單時間', '購買時間', '時間', 'Time', 'Created']) + + # V-Fix: 篩選掉空字串，只保留有效數據 + if col_category: + preview_categories = sorted([x for x in preview_df[col_category].dropna().astype(str).unique().tolist() if x and x.strip()]) + if col_brand: + preview_brands = sorted([x for x in preview_df[col_brand].dropna().astype(str).unique().tolist() if x and x.strip()]) + if col_vendor: + preview_vendors = sorted([x for x in preview_df[col_vendor].dropna().astype(str).unique().tolist() if x and x.strip()]) + if col_activity: + preview_activities = sorted([x for x in preview_df[col_activity].dropna().astype(str).unique().tolist() if x and x.strip()]) + if col_payment: + preview_payments = sorted([x for x in preview_df[col_payment].dropna().astype(str).unique().tolist() if x and x.strip()]) + + # V-Fix: 從數據庫直接查詢所有月份（而不是從預覽數據提取，避免只獲得部分月份） + if col_date_part: + try: + from sqlalchemy import text + with db.engine.connect() as conn: + result = conn.execute(text(f""" + SELECT DISTINCT replace(substr("{col_date_part}", 1, 7), '/', '-') as month + FROM {table_name} + WHERE "{col_date_part}" IS NOT NULL AND "{col_date_part}" != '' + ORDER BY month + """)).fetchall() + preview_months = [row[0] for row in result if row[0] and '-' in str(row[0])] + sys_log.info(f"[Sales Analysis] 預覽模式從「{col_date_part}」欄位提取到 {len(preview_months)} 個月份") + except Exception as e: + sys_log.warning(f"[Sales Analysis] 無法從「{col_date_part}」欄位提取月份: {e}") + pass + + # 傳遞必要的變數以避免模板錯誤 + selected_metric = request.args.get('metric', 'amount') + # 建立空的數據結構 + empty_data = {'labels': [], 'chart_values': [], 'values': [], 'metric_label': ''} + return render_template('sales_analysis.html', + no_filter=True, + table_name=table_name, + selected_metric=selected_metric, + total_records=0, + items=[], + kpi={'revenue': 0, 'qty': 0, 'count': 0, 'cost': 0, 'gross_margin': 0, 'gross_margin_rate': 0, 'avg_price': 0}, + insights={}, + abc_stats={}, + vendor_stats=[], + seasonality_data={'datasets': [], 'yLabels': [], 'xLabels': []}, + bar_data=empty_data, + cat_data=empty_data, + price_dist_data=empty_data, + scatter_data=[], + bcg_data=[], + dow_data=empty_data, + hourly_data=empty_data, + monthly_data=empty_data, + weekly_data=empty_data, + heatmap_data=[], + treemap_data=[], + cols={'name': True, 'amount': True, 'qty': True, 'cat': True, 'date': True, + 'cost': True, 'profit': True, 'vendor': True, 'brand': True, + 'return_qty': True, 'pid': True}, + all_categories=preview_categories, all_brands=preview_brands, all_vendors=preview_vendors, + all_activities=preview_activities, all_payments=preview_payments, all_months=preview_months, + selected_category='all', selected_brand='all', selected_vendor='all', + selected_activity='all', selected_payment='all', selected_dow='all', + selected_hour='all', selected_month='all', + keyword='', min_price='', max_price='', min_margin='', max_margin='', + data_range_months=0, start_date='', end_date='', + db_data_range=db_data_range, + marketing_data=None) + + # 解析 data_range_months（有篩選時才處理） + data_range_months = int(data_range_param or '0') + + # V-New: 如果有自訂日期區間，則優先使用 + if start_date or end_date: + cache_key = f"{table_name}_custom_{start_date}_{end_date}" + else: + cache_key = f"{table_name}_{data_range_months}m" + + # 2. 讀取與處理資料 (V-Opt: 使用二級快取機制 Raw -> Processed) + df = None + cols_map = {} + + # A. 優先檢查是否已有處理好的快取 (最快) + if cache_key in _SALES_PROCESSED_CACHE: + cache_data = _SALES_PROCESSED_CACHE[cache_key] + df = cache_data['df'] + cols_map = cache_data['cols'] + + # 恢復欄位變數 + col_name = cols_map.get('name') + col_date = cols_map.get('date') + col_amount = cols_map.get('amount') + col_qty = cols_map.get('qty') + col_category = cols_map.get('category') + col_brand = cols_map.get('brand') + col_vendor = cols_map.get('vendor') + col_activity = cols_map.get('activity') + col_payment = cols_map.get('payment') + col_pid = cols_map.get('pid') # V-New: 取得 PID 欄位 + col_price = cols_map.get('price') + col_cost = cols_map.get('cost') + col_profit = cols_map.get('profit') + col_return_qty = cols_map.get('return_qty') + + cached_pie_data = cache_data.get('pie_data', {'labels': [], 'chart_values': []}) # V-Opt: 讀取圓餅圖快取 + else: + # B. 若無處理後快取，則從 Raw Cache 或 DB 讀取並處理 + # V-Opt: 加入日期範圍篩選以減少記憶體使用 + # (data_range_months 已在上方定義) + + # 先讀取小樣本以識別日期欄位 + sample_df = pd.read_sql(f"SELECT * FROM {table_name} LIMIT 100", db.engine) + if sample_df.empty: + return render_template('sales_analysis.html', + error="資料表為空，請重新匯入。", + table_name=table_name, + selected_metric=request.args.get('metric', 'amount'), + no_filter=False, + data_range_months=data_range_months, + start_date=start_date, + end_date=end_date, + total_records=0, + db_data_range=db_data_range, + marketing_data=None) + + # 自動識別日期欄位（V-Fix: 優先匹配「日期」，因為「訂單日期」可能是固定文字） + sample_cols = sample_df.columns.tolist() + date_col_name = None + for col in sample_cols: + if any(keyword in str(col) for keyword in ['日期', '交易日期', 'Date', '訂單時間', '成立時間', '下單時間', '購買時間', '時間', 'Time', 'Created']): + date_col_name = col + break + + # 根據是否有日期欄位決定查詢方式 + if date_col_name: + from datetime import datetime, timedelta, timezone + TAIPEI_TZ = timezone(timedelta(hours=8)) + + # V-New: 優先處理自訂日期區間 + if start_date or end_date: + # V-Fix: 處理日期格式轉換 (2025-01-01 -> 2025/01/01) + start_date_slash = start_date.replace('-', '/') if start_date else '' + end_date_slash = end_date.replace('-', '/') if end_date else '' + + # 有自訂日期區間 - 使用 BETWEEN 或單邊範圍 + if start_date and end_date: + sql_query = f"SELECT * FROM {table_name} WHERE \"{date_col_name}\" BETWEEN '{start_date_slash}' AND '{end_date_slash}'" + sys_log.info(f"[Sales Analysis] 📅 使用自訂日期範圍: {start_date} ~ {end_date} (DB格式: {start_date_slash} ~ {end_date_slash})") + elif start_date: + sql_query = f"SELECT * FROM {table_name} WHERE \"{date_col_name}\" >= '{start_date_slash}'" + sys_log.info(f"[Sales Analysis] 📅 使用自訂開始日期: >= {start_date} (DB格式: {start_date_slash})") + else: # only end_date + sql_query = f"SELECT * FROM {table_name} WHERE \"{date_col_name}\" <= '{end_date_slash}'" + sys_log.info(f"[Sales Analysis] 📅 使用自訂結束日期: <= {end_date} (DB格式: {end_date_slash})") + elif data_range_months > 0: + # 使用相對日期範圍（最近N個月） + # V-Fix: 使用斜線格式以匹配資料庫格式 + cutoff_date = (datetime.now(TAIPEI_TZ) - timedelta(days=data_range_months * 30)).strftime('%Y/%m/%d') + sql_query = f"SELECT * FROM {table_name} WHERE \"{date_col_name}\" >= '{cutoff_date}'" + sys_log.info(f"[Sales Analysis] 📊 使用日期範圍篩選: 最近 {data_range_months} 個月 (>= {cutoff_date})") + else: + # data_range_months == 0，載入全部資料 + sql_query = f"SELECT * FROM {table_name}" + sys_log.info(f"[Sales Analysis] 📊 載入全部資料（用戶選擇）") + else: + # 無日期欄位 - 載入全部 + sql_query = f"SELECT * FROM {table_name}" + sys_log.info(f"[Sales Analysis] ⚠️ 未找到日期欄位，載入全部資料") + + # V-Opt (2026-01-23): 優先使用 PostgreSQL 聚合視圖 (mv_sales_summary) + # 聚合視圖已預先計算：資料量 -20%, 大小 -73%, 欄位類型已轉換 + from sqlalchemy import text as sql_text + from config import DATABASE_TYPE + + use_materialized_view = False + if DATABASE_TYPE == 'postgresql': + # 檢查聚合視圖是否存在 + try: + with db.engine.connect() as conn: + check_mv = conn.execute(sql_text( + "SELECT EXISTS (SELECT 1 FROM pg_matviews WHERE matviewname = 'mv_sales_summary')" + )).fetchone() + use_materialized_view = check_mv[0] if check_mv else False + except: + use_materialized_view = False + + if use_materialized_view: + # 使用聚合視圖 - 欄位已標準化為英文 + sys_log.info(f"[Sales Analysis] 📊 使用 PostgreSQL 聚合視圖 (mv_sales_summary)") + + # 構建日期篩選條件 + mv_where = "" + if start_date or end_date: + if start_date and end_date: + mv_where = f"WHERE sale_date BETWEEN '{start_date}' AND '{end_date}'" + elif start_date: + mv_where = f"WHERE sale_date >= '{start_date}'" + else: + mv_where = f"WHERE sale_date <= '{end_date}'" + elif data_range_months > 0: + from datetime import datetime, timedelta, timezone + TAIPEI_TZ = timezone(timedelta(hours=8)) + cutoff = (datetime.now(TAIPEI_TZ) - timedelta(days=data_range_months * 30)).strftime('%Y-%m-%d') + mv_where = f"WHERE sale_date >= '{cutoff}'" + + mv_query = f""" + SELECT + sale_date as "日期", + product_id as "商品ID", + product_name as "商品名稱", + category as "商品館", + brand as "品牌", + vendor_name as "廠商名稱", + payment as "付款", + total_revenue as "總業績", + total_qty as "數量", + total_cost as "總成本", + order_count + FROM mv_sales_summary + {mv_where} + """ + df = pd.read_sql(mv_query, db.engine) + sys_log.info(f"[Sales Analysis] 📊 聚合視圖載入完成: {len(df):,} 筆記錄") + else: + # 原始邏輯：使用原始表 + sys_log.info(f"[Sales Analysis] 📊 使用原始表載入...") + df = pd.read_sql(sql_query, db.engine) + sys_log.info(f"[Sales Analysis] 📊 載入完成: {len(df):,} 筆記錄") + + # 聚合模式標記 + is_aggregated_mode = use_materialized_view + + # V-Opt: 不再快取完整 DataFrame 到 _SALES_DF_CACHE (避免記憶體累積) + # 改用輕量級處理後快取 (_SALES_PROCESSED_CACHE) + + if df.empty: + return render_template('sales_analysis.html', + error="資料表為空，請重新匯入。", + table_name=table_name, + selected_metric=request.args.get('metric', 'amount'), + no_filter=False, + data_range_months=data_range_months, + start_date=start_date, + end_date=end_date, + total_records=0, + db_data_range=db_data_range, + marketing_data=None) + + # 3. 自動識別關鍵欄位 (模糊比對) + cols = df.columns.tolist() + def find_col(keywords): + # V-Opt: 改為優先遍歷關鍵字，確保優先匹配更精確的名稱 (例如 '廠商名稱' 優於 '廠商') + for k in keywords: + for col in cols: + if k in str(col): return col + return None + + col_name = find_col(['商品名稱', '品名', 'Name', 'Product']) + col_pid = find_col(['商品ID', 'Product ID', 'ID', 'i_code', 'Item Code']) # V-New: 偵測商品ID欄位 + + # V-Fix: 優先匹配「日期」欄位（「訂單日期」是固定文字，不是實際日期） + col_date_part = find_col(['日期', '交易日期', 'Date', 'Day']) + col_time_part = find_col(['訂單時間', '成立時間', '下單時間', '購買時間', '時間', 'Time', 'Created']) + + col_brand = find_col(['品牌', 'Brand']) # V-New: 品牌欄位 + col_vendor = find_col(['廠商名稱', 'Vendor Name', '廠商', '供應商', 'Vendor', 'Supplier']) # V-Opt: 優先抓取名稱 + col_activity = find_col(['活動', '折扣', 'Activity', 'Campaign', 'Promotion', '專案']) # V-New: 活動欄位 + col_payment = find_col(['付款方式', 'Payment', 'Pay']) # V-New: 付款方式欄位 + col_price = find_col(['單價', 'Price', '價格', 'Avg Price']) # V-New: 嘗試尋找單價欄位 + col_cost = find_col(['成本', 'Cost', '進價', 'Cost Price', 'Wholesale']) # V-New: 成本欄位 + col_profit = find_col(['毛利', 'Profit', '利潤']) # V-New: 直接尋找毛利欄位 (若有) + col_return_qty = find_col(['退貨數量', 'Return Qty', '退貨']) # V-New: 退貨欄位 + col_amount = find_col(['銷售金額', '業績', '金額', 'Amount', 'Sales', 'Total']) + col_qty = find_col(['銷售數量', '銷量', '數量', 'Qty', 'Quantity']) + col_category = find_col(['館別', '分類', 'Category']) + + if not col_name or not col_amount: + return render_template('sales_analysis.html', + error=f"無法自動識別關鍵欄位 (需包含 '名稱' 與 '金額')。偵測到的欄位: {cols}", + table_name=table_name, + selected_metric=request.args.get('metric', 'amount'), + no_filter=False, + data_range_months=data_range_months, + start_date=start_date, + end_date=end_date, + total_records=0, + db_data_range=db_data_range, + marketing_data=None) + + # 4. 資料處理 (Heavy Lifting - 只在快取建立時執行一次) + # 確保金額與數量是數字 + df[col_amount] = pd.to_numeric(df[col_amount], errors='coerce').fillna(0) + if col_qty: + df[col_qty] = pd.to_numeric(df[col_qty], errors='coerce').fillna(0) + + if col_cost: + df[col_cost] = pd.to_numeric(df[col_cost], errors='coerce').fillna(0) + + if col_profit: + df[col_profit] = pd.to_numeric(df[col_profit], errors='coerce').fillna(0) + + if col_return_qty: + df[col_return_qty] = pd.to_numeric(df[col_return_qty], errors='coerce').fillna(0) + + # V-Fix: 智慧日期時間合併邏輯 (聚合模式下跳過) + col_date = None + if not is_aggregated_mode: + if col_date_part and col_time_part: + # 兩者都有，嘗試合併 + try: + df['combined_dt'] = pd.to_datetime(df[col_date_part].astype(str) + ' ' + df[col_time_part].astype(str), errors='coerce') + col_date = 'combined_dt' + except: + # 合併失敗，退回使用時間欄位 (假設包含日期) 或日期欄位 + col_date = col_time_part or col_date_part + elif col_time_part: + # 只有時間欄位 (可能包含日期) + df[col_time_part] = pd.to_datetime(df[col_time_part], errors='coerce') + col_date = col_time_part + elif col_date_part: + # 只有日期欄位 + df[col_date_part] = pd.to_datetime(df[col_date_part], errors='coerce') + col_date = col_date_part + + # V-New: 若無明確單價欄位，則自動計算 (金額 / 數量) + if not col_price and col_amount and col_qty: + col_price = 'calculated_price' + # V-Opt: 使用 numpy 向量化運算加速 (取代 apply) + df[col_price] = np.where(df[col_qty] > 0, df[col_amount] / df[col_qty], 0) + + if col_price: + df[col_price] = pd.to_numeric(df[col_price], errors='coerce').fillna(0) + + # V-New: 預先計算毛利率 (Margin Rate) 用於篩選 + # 邏輯: (毛利 / 金額) * 100 + col_margin_rate = 'calculated_margin_rate' + with np.errstate(divide='ignore', invalid='ignore'): + if col_profit: + df[col_margin_rate] = (df[col_profit] / df[col_amount]) * 100 + elif col_cost: + df[col_margin_rate] = ((df[col_amount] - df[col_cost]) / df[col_amount]) * 100 + else: + df[col_margin_rate] = 0.0 + # 處理無限大與 NaN (轉為 0) + df[col_margin_rate] = df[col_margin_rate].replace([np.inf, -np.inf, np.nan], 0) + + # === V-Opt: 效能優化預計算 (V9.98) === + # 1. 日期維度 (加速篩選與聚合，避免重複呼叫 .dt 存取器) + # 聚合模式下跳過日期維度計算 + if col_date and not is_aggregated_mode: + df['_dow'] = df[col_date].dt.dayofweek + df['_hour'] = df[col_date].dt.hour + df['_week'] = df[col_date].dt.strftime('%G-W%V') + df['_month_str'] = df[col_date].dt.strftime('%Y-%m') # V-New: 月份維度 (YYYY-MM) + + # 2. 毛利額 (加速 Top 3 分析，避免 runtime 計算) + if col_profit: + df['calculated_profit'] = df[col_profit] + elif col_cost: + df['calculated_profit'] = df[col_amount] - df[col_cost] + else: + df['calculated_profit'] = 0.0 + + # 3. 全站分類圓餅圖 (已移至下方使用 target_df 計算) + + + # 建立/更新處理後快取 + cache_entry = { + 'df': df, + 'cols': { + 'name': col_name, 'date': col_date, 'amount': col_amount, + 'qty': col_qty, 'category': col_category, 'brand': col_brand, + 'vendor': col_vendor, 'activity': col_activity, 'payment': col_payment, + 'price': col_price, 'cost': col_cost, 'profit': col_profit, + 'return_qty': col_return_qty, + 'pid': col_pid # V-New: 儲存商品ID欄位 + }, + 'pid': col_pid # V-New: 儲存商品ID欄位 + } + _SALES_PROCESSED_CACHE[cache_key] = cache_entry + # V-Fix: 同時使用固定的 table_name 作為 key 儲存副本，供其他路由使用 + _SALES_PROCESSED_CACHE[table_name] = cache_entry + # V-Opt (2026-01-23): 定期清理過期快取 + _cleanup_sales_cache() + + # 🚩 V-Opt: 使用共用篩選函式 + target_df, cols_map, err = _get_filtered_sales_data(cache_key) + if err: + # V-Fix: 若快取失效，重新導向自己以觸發重新讀取（保留所有查詢參數） + params = {k: v for k, v in request.args.items()} + return redirect(url_for('sales.sales_analysis', **params)) + + # 重新取得變數 (因為 _get_filtered_sales_data 內部使用了 cols_map) + col_name = cols_map.get('name') + col_amount = cols_map.get('amount') + col_qty = cols_map.get('qty') + col_category = cols_map.get('category') + col_brand = cols_map.get('brand') + col_vendor = cols_map.get('vendor') + col_activity = cols_map.get('activity') + col_payment = cols_map.get('payment') + col_price = cols_map.get('price') + col_cost = cols_map.get('cost') + col_profit = cols_map.get('profit') + col_return_qty = cols_map.get('return_qty') + col_date = cols_map.get('date') + col_pid = cols_map.get('pid') + + # V-Fix: 準備前端需要的下拉選單資料 + # V-Opt: 從數據庫直接查詢所有可用選項，而不是只從篩選後的快取中讀取 + # 這樣可以確保下拉選單顯示完整的可選項，即使當前篩選範圍很小 + all_categories = [] + all_brands = [] + all_vendors = [] + all_activities = [] + all_payments = [] + all_months = [] + + try: + from sqlalchemy import text + # V-Fix: SQLAlchemy 2.0 需要使用 connection 對象 + with db.engine.connect() as conn: + # 讀取完整資料表的所有可用選項（使用 DISTINCT 以提升效能） + # V-Fix: 使用單引號空字串，兼容 PostgreSQL + if col_category: + sql = f"SELECT DISTINCT \"{col_category}\" FROM {table_name} WHERE \"{col_category}\" IS NOT NULL AND \"{col_category}\" <> ''" + result = conn.execute(text(sql)).fetchall() + all_categories = sorted([str(row[0]) for row in result if row[0]]) + + if col_brand: + sql = f"SELECT DISTINCT \"{col_brand}\" FROM {table_name} WHERE \"{col_brand}\" IS NOT NULL AND \"{col_brand}\" <> ''" + result = conn.execute(text(sql)).fetchall() + all_brands = sorted([str(row[0]) for row in result if row[0]]) + + if col_vendor: + sql = f"SELECT DISTINCT \"{col_vendor}\" FROM {table_name} WHERE \"{col_vendor}\" IS NOT NULL AND \"{col_vendor}\" <> ''" + result = conn.execute(text(sql)).fetchall() + all_vendors = sorted([str(row[0]) for row in result if row[0]]) + + if col_activity: + sql = f"SELECT DISTINCT \"{col_activity}\" FROM {table_name} WHERE \"{col_activity}\" IS NOT NULL AND \"{col_activity}\" <> ''" + result = conn.execute(text(sql)).fetchall() + all_activities = sorted([str(row[0]) for row in result if row[0]]) + + if col_payment: + sql = f"SELECT DISTINCT \"{col_payment}\" FROM {table_name} WHERE \"{col_payment}\" IS NOT NULL AND \"{col_payment}\" <> ''" + result = conn.execute(text(sql)).fetchall() + all_payments = sorted([str(row[0]) for row in result if row[0]]) + + # V-Fix: 從數據庫提取所有月份（格式：YYYY-MM） + if col_date: + # 從日期欄位提取月份（支援多種日期欄位名稱） + date_fields = ['日期', '訂單日期', '時間'] + for field in date_fields: + try: + # V-Fix: 使用 substr 提取年月部分，並將斜線替換為橫線 + # 數據庫格式: "2025/07/01" -> 提取前7個字符 "2025/07" -> 替換斜線 "2025-07" + result = conn.execute(text(f""" + SELECT DISTINCT replace(substr(\"{field}\", 1, 7), '/', '-') as month + FROM {table_name} + WHERE \"{field}\" IS NOT NULL AND \"{field}\" != '' + ORDER BY month + """)).fetchall() + if result and len(result) > 0: + all_months = [row[0] for row in result if row[0] and '-' in str(row[0])] + if all_months: # 如果成功提取到月份，就使用這個欄位 + sys_log.info(f"[Sales Analysis] 從欄位 {field} 提取到 {len(all_months)} 個月份: {all_months}") + break + except Exception as ex: + sys_log.warning(f"[Sales Analysis] 從欄位 {field} 提取月份失敗: {ex}") + continue + except Exception as e: + sys_log.warning(f"[Sales Analysis] 從數據庫查詢下拉選項失敗: {e}") + # 如果查詢失敗，回退到從快取讀取 + if cache_key in _SALES_PROCESSED_CACHE: + original_df = _SALES_PROCESSED_CACHE[cache_key]['df'] + elif table_name in _SALES_PROCESSED_CACHE: + original_df = _SALES_PROCESSED_CACHE[table_name]['df'] + else: + original_df = pd.DataFrame() + + if not original_df.empty: + all_categories = sorted(original_df[col_category].dropna().astype(str).unique().tolist()) if col_category else [] + all_brands = sorted(original_df[col_brand].dropna().astype(str).unique().tolist()) if col_brand else [] + all_vendors = sorted(original_df[col_vendor].dropna().astype(str).unique().tolist()) if col_vendor else [] + all_activities = sorted(original_df[col_activity].dropna().astype(str).unique().tolist()) if col_activity else [] + all_payments = sorted(original_df[col_payment].dropna().astype(str).unique().tolist()) if col_payment else [] + all_months = sorted(original_df['_month_str'].dropna().unique().tolist()) if col_date and '_month_str' in original_df.columns else [] + + # 取得前端參數供模板回填 + selected_category = request.args.get('category', 'all') + selected_metric = request.args.get('metric', 'amount') + selected_brand = request.args.get('brand', 'all') + selected_vendor = request.args.get('vendor', 'all') + selected_activity = request.args.get('activity', 'all') + selected_payment = request.args.get('payment', 'all') + selected_dow = request.args.get('dow', 'all') + selected_hour = request.args.get('hour', 'all') + selected_month = request.args.get('month', 'all') + keyword = request.args.get('keyword', '').strip() + min_price = request.args.get('min_price', '') + max_price = request.args.get('max_price', '') + min_margin = request.args.get('min_margin', '') + max_margin = request.args.get('max_margin', '') + + # 決定排序欄位 + sort_col = col_amount + if selected_metric == 'qty' and col_qty: + sort_col = col_qty + + target_df = target_df.sort_values(by=sort_col, ascending=False) + + # 📊 KPI 計算 (針對篩選後的資料) + total_revenue = float(target_df[col_amount].sum()) + total_qty = float(target_df[col_qty].sum()) if col_qty else 0 + total_count = int(len(target_df)) # 訂單筆數 + # V-Fix 2026-01-15: SKU 數應計算唯一商品數，而非記錄筆數 + sku_count = int(target_df[col_name].nunique()) if col_name else total_count + + # V-New: 成本與毛利計算 + total_cost = float(target_df[col_cost].sum()) if col_cost else 0 + if col_profit: + gross_margin = float(target_df[col_profit].sum()) + else: + gross_margin = total_revenue - total_cost + + gross_margin_rate = (gross_margin / total_revenue * 100) if total_revenue > 0 else 0 + + avg_price = total_revenue / total_qty if total_qty > 0 else 0 + + # 📊 V-New: 商業洞察 (Top 3 Analysis) + insights = { + 'rev_cats': [], 'rev_prods': [], + 'margin_cats': [], 'margin_prods': [], + 'qty_cats': [], 'qty_prods': [] + } + + # Helper function to get top 3 + # Helper function to get top 3 + def get_top_3(groupby_col, metric_col, is_margin=False, is_qty=False): + if not groupby_col or not metric_col: return [] + + # V-Opt: 直接使用 target_df 與預計算欄位，避免 copy() 與 assign() + target_metric = metric_col + if is_margin: + target_metric = 'calculated_profit' + + try: + # 直接聚合並取前3名 + # V-Fix 2026-01-15: 若 groupby_col 是 list (例如 [PID, Name])，結果 index 會是 MultiIndex + grouped = target_df.groupby(groupby_col)[target_metric].sum() + + def get_name(k): + # 如果是 Tuple (MultiIndex)，通常最後一個是 Name，取之 + return str(k[-1]) if isinstance(k, tuple) else str(k) + + return [{'name': get_name(k), 'value': float(v)} for k, v in grouped.nlargest(3).items() if v > 0] + except Exception: + return [] + + insights['rev_cats'] = get_top_3(col_category, col_amount) + # V-Fix: 商品聚合改用 [PID, Name] 避免同名不同ID商品被合併 + product_groupby = [col_pid, col_name] if col_pid else col_name + insights['rev_prods'] = get_top_3(product_groupby, col_amount) + insights['qty_cats'] = get_top_3(col_category, col_qty, is_qty=True) + insights['qty_prods'] = get_top_3(product_groupby, col_qty, is_qty=True) + + if col_cost or col_profit: + insights['margin_cats'] = get_top_3(col_category, col_amount, is_margin=True) + insights['margin_prods'] = get_top_3(product_groupby, col_amount, is_margin=True) + + # 📊 V-Opt: 改為橫向長條圖數據 (Top 20) + top_chart = target_df.head(20) + bar_data = { + 'labels': [str(n)[:20] + '...' if len(str(n)) > 20 else str(n) for n in top_chart[col_name]], # 稍微放寬長度限制 + 'chart_values': [float(x) for x in top_chart[sort_col]], + 'metric_label': '銷售金額 ($)' if selected_metric == 'amount' else '銷售數量' + } + + # 📋 V-Opt: 列表資料改為 AJAX 載入，這裡只傳空列表以加快初始渲染 + table_items = [] + + # 準備類別圓餅圖資料 + # V-Fix: 使用 target_df (篩選後資料) 動態計算 + cat_data = {'labels': [], 'chart_values': []} + if col_category and not target_df.empty: + cat_group_all = target_df.groupby(col_category)[col_amount].sum().sort_values(ascending=False) + TOP_N_CATS = 12 + if len(cat_group_all) > TOP_N_CATS: + top_cats = cat_group_all.head(TOP_N_CATS) + other_val = cat_group_all.iloc[TOP_N_CATS:].sum() + cat_data['labels'] = [str(x) for x in top_cats.index.tolist()] + ['其他'] + cat_data['chart_values'] = [float(x) for x in top_cats.tolist()] + [float(other_val)] + else: + cat_data['labels'] = [str(x) for x in cat_group_all.index.tolist()] + cat_data['chart_values'] = [float(x) for x in cat_group_all.tolist()] + + # 📊 V-New: 價格帶分析 (Price Range Analysis) + price_dist_data = {'labels': [], 'chart_values': []} + if col_price and not target_df.empty: + # 定義價格區間 (0-500, 500-1000, 1000-2000, 2000-5000, 5000-10000, 10000+) + bins = [0, 500, 1000, 2000, 5000, 10000, float('inf')] + labels = ['0-499', '500-999', '1,000-1,999', '2,000-4,999', '5,000-9,999', '10,000+'] + + # V-Opt: 使用 pd.cut 進行分組，但不修改 target_df (避免污染快取) + # right=False 表示包含左邊界，例如 500 在 500-999 這一組 + price_bins = pd.cut(target_df[col_price], bins=bins, labels=labels, right=False) + + # 統計各區間的「銷售金額」貢獻 (直接使用外部 Series 進行 groupby) + range_group = target_df.groupby(price_bins, observed=False)[col_amount].sum() + + price_dist_data['labels'] = labels + price_dist_data['chart_values'] = [float(range_group.get(l, 0)) for l in labels] + + # 📊 V-New: 價格 vs 銷量 散佈圖 (Scatter Plot) + scatter_data = [] + if col_price and col_qty and not target_df.empty: + # 取前 300 筆主要商品，避免圖表過於密集導致瀏覽器卡頓 + scatter_source = target_df.head(300) + for _, row in scatter_source.iterrows(): + # V-Fix (2026-01-23): 處理 NaN 值 + price_val = row[col_price] if pd.notna(row[col_price]) else 0 + qty_val = row[col_qty] if pd.notna(row[col_qty]) else 0 + amt_val = row[col_amount] if pd.notna(row[col_amount]) else 0 + scatter_data.append({ + 'x': float(price_val), + 'y': float(qty_val), + 'name': str(row[col_name]) if pd.notna(row[col_name]) else '', + 'amt': float(amt_val) # 用於 tooltip 顯示金額 + }) + + # 📊 V-New: BCG 矩陣分析 (BCG Matrix) + # X軸: 銷量 (Qty), Y軸: 毛利率 (Margin %) + bcg_data = {'datasets': [], 'thresholds': {'x': 0, 'y': 0}} + # V-Fix: 確保 calculated_margin_rate 欄位存在 + if col_qty and (col_cost or col_profit) and not target_df.empty and 'calculated_margin_rate' in target_df.columns: + # 1. 計算閾值 (使用中位數，避免極端值影響) + # 過濾掉銷量為 0 的商品，避免干擾閾值計算 + active_products = target_df[target_df[col_qty] > 0] + if not active_products.empty and 'calculated_margin_rate' in active_products.columns: + median_qty = active_products[col_qty].median() + median_margin = active_products['calculated_margin_rate'].median() + + # 若中位數為 0 (例如大部分商品沒銷量)，則給一個預設值以利顯示 + if median_qty == 0: median_qty = 1 + + bcg_data['thresholds'] = {'x': float(median_qty), 'y': float(median_margin)} + + # 2. 分類商品 (四象限) + # Stars (明星): High Qty, High Margin + stars = active_products[(active_products[col_qty] >= median_qty) & (active_products['calculated_margin_rate'] >= median_margin)] + # Cows (金牛): High Qty, Low Margin + cows = active_products[(active_products[col_qty] >= median_qty) & (active_products['calculated_margin_rate'] < median_margin)] + # Questions (問題): Low Qty, High Margin + questions = active_products[(active_products[col_qty] < median_qty) & (active_products['calculated_margin_rate'] >= median_margin)] + # Dogs (瘦狗): Low Qty, Low Margin + dogs = active_products[(active_products[col_qty] < median_qty) & (active_products['calculated_margin_rate'] < median_margin)] + + def format_bcg_points(df_segment): + # 限制點數，避免前端卡頓 (各象限最多 100 點) + return [{'x': float(row[col_qty]), 'y': float(row['calculated_margin_rate']), 'name': str(row[col_name]), 'amt': float(row[col_amount])} for _, row in df_segment.head(100).iterrows()] + + bcg_data['datasets'] = [ + {'label': '明星商品 (Stars)', 'data': format_bcg_points(stars), 'backgroundColor': 'rgba(255, 206, 86, 0.8)', 'borderColor': 'rgba(255, 206, 86, 1)'}, # Yellow + {'label': '金牛商品 (Cows)', 'data': format_bcg_points(cows), 'backgroundColor': 'rgba(75, 192, 192, 0.8)', 'borderColor': 'rgba(75, 192, 192, 1)'}, # Green + {'label': '問題商品 (Questions)', 'data': format_bcg_points(questions), 'backgroundColor': 'rgba(54, 162, 235, 0.8)', 'borderColor': 'rgba(54, 162, 235, 1)'}, # Blue + {'label': '瘦狗商品 (Dogs)', 'data': format_bcg_points(dogs), 'backgroundColor': 'rgba(201, 203, 207, 0.8)', 'borderColor': 'rgba(201, 203, 207, 1)'} # Grey + ] + + # 📊 V-New: 時間維度分析 (Time Analysis) + dow_data = {'labels': ['週一', '週二', '週三', '週四', '週五', '週六', '週日'], 'chart_values': [0]*7} + hourly_data = {'labels': [f"{i:02d}:00" for i in range(24)], 'chart_values': [0]*24} + weekly_data = {'labels': [], 'chart_values': []} # V-New: 每週趨勢 + monthly_data = {'labels': [], 'chart_values': []} # V-New: 每月趨勢 + heatmap_data = [] # V-New: 多維度熱力圖 (Day x Hour) + treemap_data = [] # V-New: 板塊圖數據 + + if col_date: + # 過濾掉日期無效的資料 + # V-Opt: 使用預計算欄位進行分組，速度更快 + if not target_df.empty: + # 1. 星期分析 (Day of Week) + dow_group = target_df.groupby('_dow')[col_amount].sum() + for day, val in dow_group.items(): + if not np.isnan(day): + dow_data['chart_values'][int(day)] = float(val) + + # 2. 小時分析 (Hourly) + hour_group = target_df.groupby('_hour')[col_amount].sum() + for hour, val in hour_group.items(): + if not np.isnan(hour): + hourly_data['chart_values'][int(hour)] = float(val) + + # 3. 每月趨勢 (Monthly Trend) - V-New + month_group = target_df.groupby('_month_str')[col_amount].sum().sort_index() + monthly_data['labels'] = month_group.index.tolist() + # V-Fix (2026-01-23): 處理 NaN 值避免 JSON 序列化失敗 + monthly_data['chart_values'] = [float(x) if not np.isnan(x) else 0 for x in month_group.tolist()] + + # 3. 每週趨勢 (Weekly Trend) - V-New + week_group = target_df.groupby('_week')[col_amount].sum().sort_index() + # V-Opt: 解除 12 週限制，顯示完整年度趨勢 (因應一年份數據需求) + weekly_data['labels'] = week_group.index.tolist() + # V-Fix (2026-01-23): 處理 NaN 值避免 JSON 序列化失敗 + weekly_data['chart_values'] = [float(x) if not np.isnan(x) else 0 for x in week_group.tolist()] + + # 4. 多維度熱力圖 (Day x Hour) - V-Fix: 確保數據完整性 + dh_group = target_df.groupby(['_dow', '_hour'])[col_amount].sum() + # V-Opt: 正規化氣泡大小 (Normalize Bubble Size) 以提升可讀性 + max_val = dh_group.max() if not dh_group.empty else 1 + + for (day, hour), val in dh_group.items(): + # V-Fix (2026-01-23): 處理 NaN 值 + if np.isnan(val): + val = 0 + # 將數值映射到 3~25px 的半徑範圍，確保視覺可辨識 + radius = 3 + (math.sqrt(val) / math.sqrt(max_val)) * 22 if val > 0 else 0 + heatmap_data.append({ + 'x': int(hour), # X軸: 小時 (0-23) + 'y': int(day), # Y軸: 星期 (0-6) + 'r': float(radius) if not np.isnan(radius) else 0, # V-Adj: 正規化後半徑 + 'v': float(val) # 實際數值 (用於 Tooltip) + }) + + # 📊 V-New: 板塊圖 (Treemap) 數據準備 + # 結構: Root -> Category -> Product (Top 5 per cat) + if col_category and col_name and col_amount and not target_df.empty: + # V-Opt: 優化聚合邏輯，先聚合再篩選，避免在迴圈中重複過濾大表 + # 1. 先聚合 Category + Product (大幅減少資料量) + cat_prod_group = target_df.groupby([col_category, col_name])[col_amount].sum().reset_index() + + # 2. 找出前 10 大分類 + top_cats = cat_prod_group.groupby(col_category)[col_amount].sum().nlargest(10).index.tolist() + + # 3. 針對前 10 大分類，各取前 5 大商品 + for cat in top_cats: + if not cat: continue + # 在縮減後的資料中篩選，速度極快 + cat_subset = cat_prod_group[cat_prod_group[col_category] == cat] + top_prods = cat_subset.nlargest(5, col_amount) + + for _, row in top_prods.iterrows(): + # V-Fix (2026-01-23): 處理 NaN 值 + amount_val = row[col_amount] + if pd.isna(amount_val): + amount_val = 0 + treemap_data.append({ + 'category': str(cat), + 'product': str(row[col_name]) if pd.notna(row[col_name]) else '', + 'value': float(amount_val), + 'color': get_color_for_string(str(cat)) # V-Fix: 增加顏色參數，確保與分類顏色一致且清晰 + }) + + # 📊 V-New: ABC 分析 (Pareto Analysis) - TODO #8 + # A類: 累積營收 0-80% (核心商品) + # B類: 累積營收 80-95% (次要商品) + # C類: 累積營收 95-100% (長尾商品) + abc_stats = {'A': {'count': 0, 'revenue': 0, 'pct_rev': 0, 'pct_sku': 0}, + 'B': {'count': 0, 'revenue': 0, 'pct_rev': 0, 'pct_sku': 0}, + 'C': {'count': 0, 'revenue': 0, 'pct_rev': 0, 'pct_sku': 0}} + + if not target_df.empty and col_amount: + # 使用 numpy 加速累積計算 + sorted_rev = target_df[col_amount].values # 已在上方排序過 + cumsum_rev = np.cumsum(sorted_rev) + total_rev_abc = cumsum_rev[-1] if len(cumsum_rev) > 0 else 0 + + if total_rev_abc > 0: + pct_cumsum = cumsum_rev / total_rev_abc * 100 + + # 找出分界點索引 + idx_a = np.searchsorted(pct_cumsum, 80) + idx_b = np.searchsorted(pct_cumsum, 95) + + # A類: 0 ~ idx_a + count_a = idx_a + 1 + rev_a = cumsum_rev[idx_a] if idx_a < len(cumsum_rev) else total_rev_abc + + # B類: idx_a+1 ~ idx_b + count_b = max(0, idx_b - idx_a) + rev_b = (cumsum_rev[idx_b] - cumsum_rev[idx_a]) if idx_b < len(cumsum_rev) else (total_rev_abc - cumsum_rev[idx_a]) + + # C類: idx_b+1 ~ end + count_c = max(0, len(cumsum_rev) - 1 - idx_b) + rev_c = total_rev_abc - cumsum_rev[idx_b] if idx_b < len(cumsum_rev) else 0 + + abc_stats['A'] = {'count': int(count_a), 'revenue': float(rev_a), 'pct_rev': float(rev_a/total_rev_abc*100), 'pct_sku': float(count_a/total_count*100)} + abc_stats['B'] = {'count': int(count_b), 'revenue': float(rev_b), 'pct_rev': float(rev_b/total_rev_abc*100), 'pct_sku': float(count_b/total_count*100)} + abc_stats['C'] = {'count': int(count_c), 'revenue': float(rev_c), 'pct_rev': float(rev_c/total_rev_abc*100), 'pct_sku': float(count_c/total_count*100)} + + # 📊 V-New: 廠商獲利能力排行 (Vendor Profitability) - TODO #9 + vendor_stats = [] + if col_vendor and col_amount and not target_df.empty: + # Group by vendor + agg_dict = {col_amount: 'sum', col_name: 'nunique'} # nunique 計算不重複商品數 (SKU) + if col_qty: agg_dict[col_qty] = 'sum' # V-New: 累加銷量 + if col_profit: + agg_dict[col_profit] = 'sum' + elif col_cost: + agg_dict[col_cost] = 'sum' + + # 使用 groupby 聚合 + vendor_group = target_df.groupby(col_vendor).agg(agg_dict).reset_index() + + # 計算毛利與毛利率 + if col_profit: + vendor_group['total_profit'] = vendor_group[col_profit] + elif col_cost: + vendor_group['total_profit'] = vendor_group[col_amount] - vendor_group[col_cost] + else: + vendor_group['total_profit'] = 0 + + # 計算營收佔比 (Share %) + total_vendor_revenue = vendor_group[col_amount].sum() + if total_vendor_revenue > 0: + vendor_group['revenue_share'] = (vendor_group[col_amount] / total_vendor_revenue * 100) + else: + vendor_group['revenue_share'] = 0.0 + + # 避免除以零 + vendor_group['margin_rate'] = np.where(vendor_group[col_amount] > 0, (vendor_group['total_profit'] / vendor_group[col_amount] * 100), 0) + + # 計算平均客單價 (ASP) + if col_qty: + vendor_group['asp'] = np.where(vendor_group[col_qty] > 0, vendor_group[col_amount] / vendor_group[col_qty], 0) + + # 排序：預設按總業績降序 + vendor_group = vendor_group.sort_values(by=col_amount, ascending=False) + + # 格式化輸出 (Top 100) + for _, row in vendor_group.head(100).iterrows(): + vendor_stats.append({ + 'name': str(row[col_vendor]), + 'revenue': float(row[col_amount]), + 'share': float(row['revenue_share']), # V-New + 'qty': float(row[col_qty]) if col_qty else 0, # V-New + 'asp': float(row.get('asp', 0)), # V-New + 'profit': float(row['total_profit']), + 'margin_rate': float(row['margin_rate']), + 'sku_count': int(row[col_name]) + }) + + # 📊 V-New: 淡旺季熱力圖 (Seasonality Analysis) - TODO #10 + seasonality_data = None + if col_date and col_category and col_amount and not target_df.empty: + # 1. 取得前 10 大分類 (避免圖表過大) + # 使用 target_df (受篩選影響)，這樣可以看特定品牌下的分類季節性 + top_cats_season = target_df.groupby(col_category)[col_amount].sum().nlargest(10).index.tolist() + + # 2. 聚合數據 (Month x Category) + season_group = target_df[target_df[col_category].isin(top_cats_season)].groupby(['_month_str', col_category])[col_amount].sum().reset_index() + + # 3. 轉換為 Bubble Chart 格式 + # X軸: 月份 (需解析 _month_str 取得順序) + # Y軸: 分類 (使用 top_cats_season 的索引) + + # 取得所有月份並排序 + all_months_sorted = sorted(target_df['_month_str'].unique()) + month_map = {m: i for i, m in enumerate(all_months_sorted)} + cat_map = {c: i for i, c in enumerate(top_cats_season)} + + points = [] + max_val_season = season_group[col_amount].max() if not season_group.empty else 1 + + for _, row in season_group.iterrows(): + m_str = row['_month_str'] + cat = row[col_category] + val = row[col_amount] + + if m_str in month_map and cat in cat_map: + # 正規化大小 (3~25px) + radius = 3 + (math.sqrt(val) / math.sqrt(max_val_season)) * 25 if val > 0 else 0 + points.append({ + 'x': month_map[m_str], + 'y': cat_map[cat], + 'r': radius, + 'v': float(val), + 'm': m_str, + 'c': cat + }) + + seasonality_data = { + 'datasets': [{ + 'label': '淡旺季熱點', + 'data': points, + # 顏色將在前端動態生成 + }], + 'yLabels': top_cats_season, + 'xLabels': all_months_sorted + } + + # 📊 V-New 2026-01-15: 行銷活動業績貢獻 (Marketing Campaign Contribution) + marketing_data = None + if not target_df.empty: + marketing_data = prepare_marketing_summary(target_df, sort_by=selected_metric) + + return render_template('sales_analysis.html', + marketing_data=marketing_data, # V-New: 傳遞行銷活動數據 + items=table_items, + kpi={ + 'revenue': total_revenue, + 'qty': total_qty, + 'count': total_count, + 'sku_count': sku_count, # V-Fix 2026-01-15: 唯一商品數 + 'cost': total_cost, + 'gross_margin': gross_margin, + 'gross_margin_rate': gross_margin_rate, + 'avg_price': avg_price + }, + insights=insights, + abc_stats=abc_stats, # V-New: 傳遞 ABC 分析數據 + vendor_stats=vendor_stats, # V-New: 傳遞廠商排行數據 + seasonality_data=seasonality_data, # V-New: 傳遞淡旺季數據 + bar_data=bar_data, + cat_data=cat_data, + price_dist_data=price_dist_data, + scatter_data=scatter_data, + bcg_data=bcg_data, # V-New: 傳遞 BCG 數據 + dow_data=dow_data, + hourly_data=hourly_data, + monthly_data=monthly_data, + weekly_data=weekly_data, + heatmap_data=heatmap_data, + treemap_data=treemap_data, + all_categories=all_categories, + all_brands=all_brands, all_vendors=all_vendors, all_activities=all_activities, all_payments=all_payments, + all_months=all_months, # V-New: 傳遞月份列表 + selected_category=selected_category, + selected_brand=selected_brand, selected_vendor=selected_vendor, + selected_activity=selected_activity, selected_payment=selected_payment, + selected_dow=selected_dow, selected_hour=selected_hour, + selected_month=selected_month, + selected_metric=selected_metric, + keyword=keyword, min_price=min_price, max_price=max_price, + min_margin=min_margin, max_margin=max_margin, + cols={'name': col_name, 'amount': col_amount, 'qty': col_qty, 'cat': col_category, 'date': col_date, 'cost': col_cost, 'profit': col_profit, 'vendor': col_vendor, 'brand': col_brand, 'return_qty': col_return_qty, 'pid': col_pid}, + table_name=table_name, + data_range_months=data_range_months, + start_date=start_date, # V-New: 傳遞自訂開始日期 + end_date=end_date, # V-New: 傳遞自訂結束日期 + total_records=len(df), + db_data_range=db_data_range) # V-New: 傳遞資料庫資料期間 + + except Exception as e: + sys_log.error(f"Sales Analysis Error: {e}") + import traceback + traceback.print_exc() + # 提供完整的變數以避免模板錯誤 + return render_template('sales_analysis.html', + error=f"系統發生錯誤: {str(e)}", + marketing_data=None, + insights=None, + abc_stats=None, + vendor_stats=None, + seasonality_data=None, + scatter_data=None, + bcg_data=None, + dow_data=None, + hourly_data=None, + monthly_data=None, + weekly_data=None, + heatmap_data=None, + treemap_data=None, + all_categories=[], + all_brands=[], all_vendors=[], all_activities=[], all_payments=[], + all_months=[], + selected_category='all', + selected_brand='all', selected_vendor='all', + selected_activity='all', selected_payment='all', + selected_dow='all', selected_hour='all', + selected_month='all', + selected_metric=request.args.get('metric', 'amount'), + keyword='', min_price='', max_price='', + min_margin='', max_margin='', + cols={}, + table_name='realtime_sales_monthly', + no_filter=False, + data_range_months=int(request.args.get('data_range', '0') or '0'), + start_date=request.args.get('start_date', ''), + end_date=request.args.get('end_date', ''), + total_records=0, + db_data_range='') @sales_bp.route('/growth_analysis') @@ -306,43 +1420,1093 @@ def growth_analysis(): @sales_bp.route('/api/sales_analysis/table_data') @login_required def api_sales_table_data(): - """API: 取得業績分析表格數據""" - # 延遲導入 (使用 app.py 中的原始函數名稱) - from app import get_sales_table_data as _original_func - return _original_func() + """API: 取得業績分析的詳細列表資料 (Server-side AJAX) - 使用 SQL 聚合優化""" + try: + import hashlib + from datetime import datetime, timedelta, timezone + TAIPEI_TZ = timezone(timedelta(hours=8)) + + # V-Opt: 產生查詢快取 key (根據所有篩選條件) + cache_params = request.args.to_dict() + cache_key = hashlib.md5(str(sorted(cache_params.items())).encode(), usedforsecurity=False).hexdigest() + + # V-Opt: 檢查快取 + if cache_key in _TABLE_DATA_CACHE: + cached = _TABLE_DATA_CACHE[cache_key] + if time.time() - cached['time'] < _TABLE_DATA_CACHE_TTL: + sys_log.debug(f"[API] Table Data: 使用快取 (key={cache_key[:8]})") + return jsonify(cached['data']) + + table_name = 'realtime_sales_monthly' + data_range_months = int(request.args.get('data_range', '1') or '1') + start_date = request.args.get('start_date', '') # V-New: 自訂開始日期 + end_date = request.args.get('end_date', '') # V-New: 自訂結束日期 + + # V-Fix: 取得所有篩選參數 + category_filter = request.args.get('category', 'all') + brand_filter = request.args.get('brand', 'all') # V-Fix: 品牌篩選 + vendor_filter = request.args.get('vendor', 'all') # V-Fix: 廠商篩選 + activity_filter = request.args.get('activity', 'all') # V-Fix: 活動篩選 + payment_filter = request.args.get('payment', 'all') # V-Fix: 付款方式篩選 + month_filter = request.args.get('month', 'all') + dow_filter = request.args.get('dow', 'all') # 星期篩選 + hour_filter = request.args.get('hour', 'all') # 小時篩選 + min_price_str = request.args.get('min_price', '') + max_price_str = request.args.get('max_price', '') + min_margin_str = request.args.get('min_margin', '') + max_margin_str = request.args.get('max_margin', '') + keyword = request.args.get('keyword', '').strip() + + db = DatabaseManager() + + # V-Fix: 從快取讀取欄位名稱對應，以支援不同的資料庫欄位名稱 + if start_date or end_date: + cache_key = f"{table_name}_custom_{start_date}_{end_date}" + else: + cache_key = f"{table_name}_{data_range_months}m" + + # 嘗試從快取讀取欄位名稱 + cols_map = {} + if cache_key in _SALES_PROCESSED_CACHE: + cols_map = _SALES_PROCESSED_CACHE[cache_key].get('cols', {}) + elif table_name in _SALES_PROCESSED_CACHE: # V-Fix: 也嘗試使用固定 key + cols_map = _SALES_PROCESSED_CACHE[table_name].get('cols', {}) + + # 取得實際欄位名稱（如果快取中沒有，使用預設名稱） + # V-Fix (2026-01-23): 使用 or 確保不會得到 None 值 + col_name = cols_map.get('name') or '商品名稱' + col_pid = cols_map.get('pid') or '商品ID' + col_brand = cols_map.get('brand') or '品牌' + col_vendor = cols_map.get('vendor') or '廠商名稱' + col_category = cols_map.get('category') or '商品館' + col_amount = cols_map.get('amount') or '總業績' + col_qty = cols_map.get('qty') or '數量' + col_cost = cols_map.get('cost') or '總成本' + col_profit = cols_map.get('profit') or '毛利' + col_return_qty = cols_map.get('return_qty') or '退貨數量' + + # V-Opt: 使用純 SQL 聚合查詢，避免載入完整資料集 + # 建立日期篩選條件 + date_filter = "" + # V-New: 優先處理自訂日期區間 + if start_date or end_date: + # V-Fix: 處理日期格式轉換 (2025-01-01 -> 2025/01/01) + start_date_slash = start_date.replace('-', '/') if start_date else '' + end_date_slash = end_date.replace('-', '/') if end_date else '' + + # V-Fix: 只使用「日期」欄位（「訂單日期」欄位是固定文字「訂單日期」，不是實際日期） + if start_date and end_date: + date_filter = f"""AND ("日期" BETWEEN '{start_date_slash}' AND '{end_date_slash}')""" + elif start_date: + date_filter = f"""AND ("日期" >= '{start_date_slash}')""" + else: # only end_date + date_filter = f"""AND ("日期" <= '{end_date_slash}')""" + elif data_range_months > 0: + # V-Fix: 使用斜線格式以匹配資料庫格式 + cutoff_date = (datetime.now(TAIPEI_TZ) - timedelta(days=data_range_months * 30)).strftime('%Y/%m/%d') + # V-Fix: 只使用「日期」欄位進行篩選（「訂單日期」是固定文字，不是實際日期） + date_filter = f"""AND ("日期" >= '{cutoff_date}')""" + + # V-Fix: 建立其他篩選條件 + additional_filters = [] + + # 分類篩選 + if category_filter and category_filter != 'all' and col_category: + additional_filters.append(f""""{col_category}" = '{category_filter}'""") + + # V-Fix: 品牌篩選 + if brand_filter and brand_filter != 'all' and col_brand: + additional_filters.append(f""""{col_brand}" = '{brand_filter}'""") + + # V-Fix: 廠商篩選 + if vendor_filter and vendor_filter != 'all' and col_vendor: + additional_filters.append(f""""{col_vendor}" = '{vendor_filter}'""") + + # V-Fix: 活動篩選 + col_activity = cols_map.get('activity') + if activity_filter and activity_filter != 'all' and col_activity: + additional_filters.append(f""""{col_activity}" = '{activity_filter}'""") + + # V-Fix: 付款方式篩選 + col_payment = cols_map.get('payment') + if payment_filter and payment_filter != 'all' and col_payment: + additional_filters.append(f""""{col_payment}" = '{payment_filter}'""") + + # 月份篩選 + if month_filter and month_filter != 'all': + # V-Fix: 月份格式例如 "2025-01"，但資料庫可能使用斜線格式 "2025/01" + # 只使用「日期」欄位（「訂單日期」是固定文字，「時間」只包含時間） + month_filter_slash = month_filter.replace('-', '/') # "2025-01" -> "2025/01" + # 同時匹配橫線和斜線格式 + additional_filters.append(f"""("日期" LIKE '{month_filter}%' OR "日期" LIKE '{month_filter_slash}%')""") + + # 星期篩選 (需要從日期計算) + if dow_filter and dow_filter != 'all': + # V-Fix (2026-01-23): 支援 PostgreSQL 和 SQLite 兩種資料庫 + # Pandas dt.dayofweek: 0=Monday, 6=Sunday + pandas_dow = int(dow_filter) + if DATABASE_TYPE == 'postgresql': + # PostgreSQL: EXTRACT(DOW FROM date) 0=Sunday, 6=Saturday + # Pandas 0(Mon) -> PostgreSQL 1(Mon), Pandas 6(Sun) -> PostgreSQL 0(Sun) + pg_dow = (pandas_dow + 1) % 7 + # 日期格式可能是 2025/01/01，需要轉換為 YYYY-MM-DD + additional_filters.append(f"""EXTRACT(DOW FROM TO_DATE(REPLACE("日期", '/', '-'), 'YYYY-MM-DD')) = {pg_dow}""") + else: + # SQLite: strftime('%w', date) 0=Sunday, 6=Saturday + sqlite_dow = str((pandas_dow + 1) % 7) + additional_filters.append(f"""strftime('%w', replace("日期", '/', '-')) = '{sqlite_dow}'""") + + # 小時篩選 (需要從時間欄位提取) + if hour_filter and hour_filter != 'all': + # V-Fix (2026-01-23): 支援 PostgreSQL 和 SQLite 兩種資料庫 + hour_val = int(hour_filter) + if DATABASE_TYPE == 'postgresql': + # PostgreSQL: 使用 SUBSTRING 或 CAST + additional_filters.append(f"""CAST(SUBSTRING("時間" FROM 1 FOR 2) AS INTEGER) = {hour_val}""") + else: + # SQLite: 使用 substr + additional_filters.append(f"""CAST(substr("時間", 1, 2) AS INTEGER) = {hour_val}""") + + # 關鍵字篩選 + if keyword: + keyword_escaped = keyword.replace("'", "''") # SQL 注入防護 + keyword_conditions = [] + if col_name: + keyword_conditions.append(f""""{col_name}" LIKE '%{keyword_escaped}%'""") + if col_pid: + keyword_conditions.append(f""""{col_pid}" LIKE '%{keyword_escaped}%'""") + if col_brand: + keyword_conditions.append(f""""{col_brand}" LIKE '%{keyword_escaped}%'""") + if col_vendor: + keyword_conditions.append(f""""{col_vendor}" LIKE '%{keyword_escaped}%'""") + if keyword_conditions: + additional_filters.append(f"({' OR '.join(keyword_conditions)})") + + # V-New: 價格區間篩選 (Price Range) + if (min_price_str or max_price_str) and col_qty and col_amount: + # 假設單價 = 總業績 / 數量 (防止除以零) + price_cal_sql = f'CAST("{col_amount}" AS FLOAT) / NULLIF("{col_qty}", 0)' + if min_price_str: + additional_filters.append(f"{price_cal_sql} >= {float(min_price_str)}") + if max_price_str: + additional_filters.append(f"{price_cal_sql} <= {float(max_price_str)}") + + # V-New: 毛利率區間篩選 (Margin Range) + if (min_margin_str or max_margin_str) and col_amount: + # 計算毛利額 SQL + if col_profit: + profit_cal_sql = f'"{col_profit}"' + elif col_cost: + profit_cal_sql = f'("{col_amount}" - "{col_cost}")' + else: + profit_cal_sql = "0" + + # 計算毛利率 SQL: (毛利 / 業績) * 100 + margin_cal_sql = f'({profit_cal_sql} * 100.0 / NULLIF("{col_amount}", 0))' + + if min_margin_str: + additional_filters.append(f"{margin_cal_sql} >= {float(min_margin_str)}") + if max_margin_str: + additional_filters.append(f"{margin_cal_sql} <= {float(max_margin_str)}") + + # 組合所有篩選條件 + all_filters = date_filter + if additional_filters: + all_filters += " AND " + " AND ".join(additional_filters) + + # SQL 聚合查詢 - 直接在資料庫層級完成聚合 + # V-Fix: 使用動態欄位名稱 + group_by_cols = [] + if col_pid: group_by_cols.append(f'"{col_pid}"') + if col_name: group_by_cols.append(f'"{col_name}"') + if col_brand: group_by_cols.append(f'"{col_brand}"') + if col_vendor: group_by_cols.append(f'"{col_vendor}"') + if col_category: group_by_cols.append(f'"{col_category}"') + group_by_clause = ', '.join(group_by_cols) if group_by_cols else '"商品ID"' + + sql_query = f""" + SELECT + {f'"{col_pid}" as product_id' if col_pid else "'未知' as product_id"}, + {f'"{col_name}" as name' if col_name else "'未知' as name"}, + {f'"{col_brand}" as brand' if col_brand else "'' as brand"}, + {f'"{col_vendor}" as vendor' if col_vendor else "'' as vendor"}, + {f'"{col_category}" as category' if col_category else "'' as category"}, + {f'SUM(CAST("{col_amount}" AS REAL)) as amount' if col_amount else '0 as amount'}, + {f'SUM(CAST("{col_qty}" AS REAL)) as qty' if col_qty else '0 as qty'}, + {f'SUM(CAST("{col_cost}" AS REAL)) as cost' if col_cost else '0 as cost'}, + {f'SUM(CAST("{col_return_qty}" AS REAL)) as return_qty' if col_return_qty else '0 as return_qty'}, + COUNT(*) as order_count + FROM {table_name} + WHERE 1=1 {all_filters} + GROUP BY {group_by_clause} + ORDER BY amount DESC + LIMIT 300 + """ + + df_agg = pd.read_sql(sql_query, db.engine) + sys_log.info(f"[API] Table Data: SQL聚合查詢返回 {len(df_agg)} 筆商品 (篩選: category={category_filter}, month={month_filter}, dow={dow_filter}, hour={hour_filter}, keyword={keyword})") + + if df_agg.empty: + return jsonify({'data': []}) + + # 計算衍生欄位 + df_agg['margin_rate'] = ((df_agg['amount'] - df_agg['cost']) / df_agg['amount'] * 100).fillna(0) + df_agg['margin_rate'] = df_agg['margin_rate'].replace([np.inf, -np.inf], 0) + df_agg['avg_price'] = (df_agg['amount'] / df_agg['qty']).fillna(0) + df_agg['return_rate'] = (df_agg['return_qty'] / df_agg['qty'] * 100).fillna(0) + + # V-Fix: 應用價格區間篩選 (在計算欄位後才能篩選) + if min_price_str: + try: + min_price = float(min_price_str) + df_agg = df_agg[df_agg['avg_price'] >= min_price] + except ValueError: + pass + + if max_price_str: + try: + max_price = float(max_price_str) + df_agg = df_agg[df_agg['avg_price'] <= max_price] + except ValueError: + pass + + # V-Fix: 應用毛利區間篩選 (在計算欄位後才能篩選) + if min_margin_str: + try: + min_margin = float(min_margin_str) + df_agg = df_agg[df_agg['margin_rate'] >= min_margin] + except ValueError: + pass + + if max_margin_str: + try: + max_margin = float(max_margin_str) + df_agg = df_agg[df_agg['margin_rate'] <= max_margin] + except ValueError: + pass + + # 重新排序並限制到 300 筆 (減少前端渲染負擔) + df_agg = df_agg.sort_values('amount', ascending=False).head(300) + + # V-Opt: 使用向量化操作取代逐列迴圈 + df_agg['rank'] = range(1, len(df_agg) + 1) + df_agg['month_str'] = '' # SQL聚合模式不需要月份字串 + + # 重新命名欄位以符合前端格式 + result_df = df_agg.rename(columns={ + 'product_id': 'product_id', + 'name': 'name', + 'brand': 'brand', + 'vendor': 'vendor', + 'category': 'category', + 'margin_rate': 'margin_rate', + 'avg_price': 'avg_price', + 'return_rate': 'return_rate', + 'qty': 'qty', + 'amount': 'amount' + }) + + # 選擇需要的欄位並轉換為字典列表 + columns = ['rank', 'product_id', 'name', 'brand', 'vendor', 'category', + 'margin_rate', 'month_str', 'avg_price', 'return_rate', 'qty', 'amount'] + + # V-Fix (2026-01-23): 確保所有數值欄位無 NaN/Infinity，避免 JSON 序列化失敗 + numeric_cols = ['margin_rate', 'avg_price', 'return_rate', 'qty', 'amount'] + for col in numeric_cols: + if col in result_df.columns: + result_df[col] = result_df[col].replace([np.inf, -np.inf], 0).fillna(0) + + # V-Fix (2026-01-23): 確保字串欄位無 None，避免 JSON 序列化失敗 + string_cols = ['product_id', 'name', 'brand', 'vendor', 'category', 'month_str'] + for col in string_cols: + if col in result_df.columns: + result_df[col] = result_df[col].fillna('').astype(str) + + data = result_df[columns].to_dict('records') + + response_data = {'data': data} + + # V-Opt: 儲存到快取 + _TABLE_DATA_CACHE[cache_key] = {'data': response_data, 'time': time.time()} + + # V-Opt: 清理過期快取 (保留最近 50 個) + if len(_TABLE_DATA_CACHE) > 50: + sorted_keys = sorted(_TABLE_DATA_CACHE.keys(), + key=lambda k: _TABLE_DATA_CACHE[k]['time']) + for old_key in sorted_keys[:-50]: + del _TABLE_DATA_CACHE[old_key] + + return jsonify(response_data) + + except Exception as e: + sys_log.error(f"[API] Table Data Error: {e}") + import traceback + traceback.print_exc() + return jsonify({'error': str(e)}), 500 @sales_bp.route('/api/sales_analysis/table_data_pandas') @login_required def api_sales_table_data_pandas(): - """API: 使用 Pandas 進行分頁和排序的業績分析表格數據""" - # 延遲導入 (使用 app.py 中的原始函數名稱) - from app import get_sales_table_data_pandas as _original_func - return _original_func() + """API: 取得業績分析的詳細列表資料 (使用 pandas 聚合 - 舊版本)""" + try: + table_name = 'realtime_sales_monthly' + data_range_months = int(request.args.get('data_range', '1')) + cache_key = f"{table_name}_{data_range_months}m" + target_df, cols_map, err = _get_filtered_sales_data(cache_key) + + if err or target_df is None: + sys_log.warning(f"[API] Table Data: 快取不存在 ({cache_key})，返回空資料") + return jsonify({'data': []}) + + if target_df.empty: + return jsonify({'data': []}) + + col_name = cols_map.get('name') + col_amount = cols_map.get('amount') + col_qty = cols_map.get('qty') + col_cost = cols_map.get('cost') + col_profit = cols_map.get('profit') + col_category = cols_map.get('category') + col_vendor = cols_map.get('vendor') + col_date = cols_map.get('date') + col_brand = cols_map.get('brand') + col_return_qty = cols_map.get('return_qty') + + selected_metric = request.args.get('metric', 'amount') + + # 執行聚合 (V-Opt: 多維度聚合，增加精確度) + agg_rules = {col_amount: 'sum'} + if col_qty: agg_rules[col_qty] = 'sum' + if col_cost: agg_rules[col_cost] = 'sum' + if col_profit: agg_rules[col_profit] = 'sum' + if col_return_qty: agg_rules[col_return_qty] = 'sum' + if col_date: agg_rules['_month_str'] = lambda x: ', '.join(sorted(x.dropna().unique())) + + # Group By 鍵值：商品名稱 + 品牌 + 廠商 + 分類 (確保唯一性) + group_cols = [col_name] + if col_brand: group_cols.append(col_brand) + if col_vendor: group_cols.append(col_vendor) + if col_category: group_cols.append(col_category) + + df_agg = target_df.groupby(group_cols).agg(agg_rules).reset_index() + + # 計算毛利率 + if col_profit: + df_agg['agg_margin_rate'] = (df_agg[col_profit] / df_agg[col_amount]) * 100 + elif col_cost: + df_agg['agg_margin_rate'] = ((df_agg[col_amount] - df_agg[col_cost]) / df_agg[col_amount]) * 100 + else: + df_agg['agg_margin_rate'] = 0.0 + df_agg['agg_margin_rate'] = df_agg['agg_margin_rate'].replace([np.inf, -np.inf, np.nan], 0) + + # V-New: 計算平均單價與退貨率 + if col_qty: + df_agg['avg_price'] = (df_agg[col_amount] / df_agg[col_qty]).fillna(0) + if col_return_qty: + df_agg['return_rate'] = (df_agg[col_return_qty] / df_agg[col_qty] * 100).fillna(0) + + # 排序 + sort_col_agg = col_amount + if selected_metric == 'qty' and col_qty: + sort_col_agg = col_qty + + df_agg = df_agg.sort_values(by=sort_col_agg, ascending=False).head(1000) # 限制前 1000 筆 + + # 轉換為 DataTables 需要的格式 + data = [] + for i, row in enumerate(df_agg.to_dict('records')): + data.append({ + 'rank': i + 1, + 'name': row.get(col_name, ''), + 'brand': row.get(col_brand, ''), + 'vendor': row.get(col_vendor, ''), + 'category': row.get(col_category, ''), + 'margin_rate': row.get('agg_margin_rate', 0), + 'month_str': row.get('_month_str', ''), + 'avg_price': row.get('avg_price', 0), + 'return_rate': row.get('return_rate', 0), + 'qty': row.get(col_qty, 0), + 'amount': row.get(col_amount, 0) + }) + + return jsonify({'data': data}) + + except Exception as e: + sys_log.error(f"Table Data API Error: {e}") + return jsonify({'error': str(e)}), 500 @sales_bp.route('/api/sales_analysis/top_detail') @login_required def api_sales_top_detail(): - """API: 取得 Top N 商品/分類/廠商的詳細資料""" - # 延遲導入 (使用 app.py 中的原始函數名稱) - from app import get_top_detail as _original_func - return _original_func() + """API: 取得 Top N 詳細列表（業績貢獻王/獲利金雞母/人氣引流款）""" + try: + from datetime import datetime, timedelta, timezone + TAIPEI_TZ = timezone(timedelta(hours=8)) + + table_name = 'realtime_sales_monthly' + data_range_months = int(request.args.get('data_range', '1') or '1') + start_date = request.args.get('start_date', '') # V-New: 自訂開始日期 + end_date = request.args.get('end_date', '') # V-New: 自訂結束日期 + top_type = request.args.get('type', 'revenue') # revenue/margin/quantity + metric = request.args.get('metric', 'amount') # amount/profit/qty + view_type = request.args.get('view', 'product') # product/category + + db = DatabaseManager() + + # V-Fix: 從快取讀取欄位名稱對應，以支援不同的資料庫欄位名稱 + if start_date or end_date: + cache_key = f"{table_name}_custom_{start_date}_{end_date}" + else: + cache_key = f"{table_name}_{data_range_months}m" + + # 嘗試從快取讀取欄位名稱 + cols_map = {} + if cache_key in _SALES_PROCESSED_CACHE: + cols_map = _SALES_PROCESSED_CACHE[cache_key].get('cols', {}) + elif table_name in _SALES_PROCESSED_CACHE: # V-Fix: 也嘗試使用固定 key + cols_map = _SALES_PROCESSED_CACHE[table_name].get('cols', {}) + + # 取得實際欄位名稱（如果快取中沒有，使用預設名稱） + # V-Fix (2026-01-23): 使用 or 確保不會得到 None 值 + col_name = cols_map.get('name') or '商品名稱' + col_brand = cols_map.get('brand') or '品牌' + col_vendor = cols_map.get('vendor') or '廠商名稱' + col_category = cols_map.get('category') or '商品館' + col_amount = cols_map.get('amount') or '總業績' + col_qty = cols_map.get('qty') or '數量' + col_cost = cols_map.get('cost') or '總成本' + col_profit = cols_map.get('profit') # 可以為 None + col_activity = cols_map.get('activity') or '活動名稱' + col_payment = cols_map.get('payment') or '付款方式' + + # 建立日期篩選條件 + date_filter = "" + # V-New: 優先處理自訂日期區間 + if start_date or end_date: + # V-Fix: 處理日期格式轉換 (2025-01-01 -> 2025/01/01) + start_date_slash = start_date.replace('-', '/') if start_date else '' + end_date_slash = end_date.replace('-', '/') if end_date else '' + + if start_date and end_date: + date_filter = f"""AND "日期" BETWEEN '{start_date_slash}' AND '{end_date_slash}'""" + elif start_date: + date_filter = f"""AND "日期" >= '{start_date_slash}'""" + else: # only end_date + date_filter = f"""AND "日期" <= '{end_date_slash}'""" + elif data_range_months > 0: + cutoff_date = (datetime.now(TAIPEI_TZ) - timedelta(days=data_range_months * 30)).strftime('%Y/%m/%d') + date_filter = f"""AND "日期" >= '{cutoff_date}'""" + + # V-Fix: 補上其他所有篩選條件 (與 get_sales_table_data 一致) + category_filter = request.args.get('category', 'all') + brand_filter = request.args.get('brand', 'all') + vendor_filter = request.args.get('vendor', 'all') + activity_filter = request.args.get('activity', 'all') + payment_filter = request.args.get('payment', 'all') + month_filter = request.args.get('month', 'all') + dow_filter = request.args.get('dow', 'all') + hour_filter = request.args.get('hour', 'all') + min_price_str = request.args.get('min_price', '') + max_price_str = request.args.get('max_price', '') + min_margin_str = request.args.get('min_margin', '') + max_margin_str = request.args.get('max_margin', '') + keyword = request.args.get('keyword', '').strip() + + additional_filters = [] + + if category_filter and category_filter != 'all': + additional_filters.append(f""""{col_category}" = '{category_filter}'""") + if brand_filter and brand_filter != 'all': + additional_filters.append(f""""{col_brand}" = '{brand_filter}'""") + if vendor_filter and vendor_filter != 'all': + additional_filters.append(f""""{col_vendor}" = '{vendor_filter}'""") + if activity_filter and activity_filter != 'all': + additional_filters.append(f""""{col_activity}" = '{activity_filter}'""") + if payment_filter and payment_filter != 'all': + additional_filters.append(f""""{col_payment}" = '{payment_filter}'""") + + # 時間維度 + if month_filter and month_filter != 'all': + month_filter_slash = month_filter.replace('-', '/') + # 使用「日期」欄位 (這似乎是系統內部固定欄位，不需 dynamic map，除非資料表結構也變了) + # 假設 "日期" 是固定欄位 + additional_filters.append(f"""("日期" LIKE '{month_filter}%' OR "日期" LIKE '{month_filter_slash}%')""") + + if dow_filter and dow_filter != 'all': + # V-Fix (2026-01-23): 支援 PostgreSQL 和 SQLite (top_detail API) + pandas_dow = int(dow_filter) + if DATABASE_TYPE == 'postgresql': + pg_dow = (pandas_dow + 1) % 7 + additional_filters.append(f"""EXTRACT(DOW FROM TO_DATE(REPLACE("日期", '/', '-'), 'YYYY-MM-DD')) = {pg_dow}""") + else: + sqlite_dow = str((pandas_dow + 1) % 7) + additional_filters.append(f"""strftime('%w', replace("日期", '/', '-')) = '{sqlite_dow}'""") + + if hour_filter and hour_filter != 'all': + # V-Fix (2026-01-23): 支援 PostgreSQL 和 SQLite (top_detail API) + hour_val = int(hour_filter) + if DATABASE_TYPE == 'postgresql': + additional_filters.append(f"""CAST(SUBSTRING("時間" FROM 1 FOR 2) AS INTEGER) = {hour_val}""") + else: + additional_filters.append(f"""CAST(substr("時間", 1, 2) AS INTEGER) = {hour_val}""") + + # 關鍵字 + if keyword: + keyword_escaped = keyword.replace("'", "''") + k_conds = [] + for col in [col_name, cols_map.get("pid", "商品ID"), col_brand, col_vendor]: + k_conds.append(f""""{col}" LIKE '%{keyword_escaped}%'""") + additional_filters.append(f"({' OR '.join(k_conds)})") + + if (min_price_str or max_price_str): + price_sql = f'CAST("{col_amount}" AS FLOAT) / NULLIF("{col_qty}", 0)' + if min_price_str: additional_filters.append(f"{price_sql} >= {float(min_price_str)}") + if max_price_str: additional_filters.append(f"{price_sql} <= {float(max_price_str)}") + + if (min_margin_str or max_margin_str): + if col_profit: + profit_sql = f'"{col_profit}"' + else: + profit_sql = f'("{col_amount}" - "{col_cost}")' + + margin_sql = f'({profit_sql} * 100.0 / NULLIF("{col_amount}", 0))' + if min_margin_str: additional_filters.append(f"{margin_sql} >= {float(min_margin_str)}") + if max_margin_str: additional_filters.append(f"{margin_sql} <= {float(max_margin_str)}") + + if additional_filters: + date_filter += " AND " + " AND ".join(additional_filters) + + # V-New: 準備利潤計算 SQL 片段 (SELECT 子句使用 SUM 聚合) + if col_profit: + profit_select_sql = f'SUM(CAST("{col_profit}" AS REAL))' + else: + profit_select_sql = f'SUM(CAST("{col_amount}" AS REAL)) - SUM(CAST("{col_cost}" AS REAL))' + + # 根據檢視類型和指標建立 SQL 查詢 + if view_type == 'category': + # 分類排行 + if metric == 'qty': + sql_query = f""" + SELECT + "{col_category}" as name, + SUM(CAST("{col_qty}" AS REAL)) as value + FROM {table_name} + WHERE "{col_category}" IS NOT NULL {date_filter} + GROUP BY "{col_category}" + ORDER BY value DESC + LIMIT 50 + """ + elif metric == 'profit': + sql_query = f""" + SELECT + "{col_category}" as name, + {profit_select_sql} as value, + CASE + WHEN SUM(CAST("{col_amount}" AS REAL)) > 0 + THEN (({profit_select_sql}) / SUM(CAST("{col_amount}" AS REAL))) * 100 + ELSE 0 + END as margin_rate + FROM {table_name} + WHERE "{col_category}" IS NOT NULL {date_filter} + GROUP BY "{col_category}" + ORDER BY value DESC + LIMIT 50 + """ + else: # amount + sql_query = f""" + SELECT + "{col_category}" as name, + SUM(CAST("{col_amount}" AS REAL)) as value + FROM {table_name} + WHERE "{col_category}" IS NOT NULL {date_filter} + GROUP BY "{col_category}" + ORDER BY value DESC + LIMIT 50 + """ + else: + # 商品排行（包含商品ID） + pid_col_sql = f'"{cols_map.get("pid", "商品ID")}"' # 商品ID 欄位 + if metric == 'qty': + sql_query = f""" + SELECT + {pid_col_sql} as product_id, + "{col_name}" as name, + "{col_brand}" as brand, + "{col_vendor}" as vendor, + "{col_category}" as category, + SUM(CAST("{col_qty}" AS REAL)) as value + FROM {table_name} + WHERE "{col_name}" IS NOT NULL {date_filter} + GROUP BY {pid_col_sql}, "{col_name}", "{col_brand}", "{col_vendor}", "{col_category}" + ORDER BY value DESC + LIMIT 100 + """ + elif metric == 'profit': + sql_query = f""" + SELECT + {pid_col_sql} as product_id, + "{col_name}" as name, + "{col_brand}" as brand, + "{col_vendor}" as vendor, + "{col_category}" as category, + {profit_select_sql} as value, + CASE + WHEN SUM(CAST("{col_amount}" AS REAL)) > 0 + THEN (({profit_select_sql}) / SUM(CAST("{col_amount}" AS REAL))) * 100 + ELSE 0 + END as margin_rate + FROM {table_name} + WHERE "{col_name}" IS NOT NULL {date_filter} + GROUP BY {pid_col_sql}, "{col_name}", "{col_brand}", "{col_vendor}", "{col_category}" + ORDER BY value DESC + LIMIT 100 + """ + else: # amount + sql_query = f""" + SELECT + {pid_col_sql} as product_id, + "{col_name}" as name, + "{col_brand}" as brand, + "{col_vendor}" as vendor, + "{col_category}" as category, + SUM(CAST("{col_amount}" AS REAL)) as value + FROM {table_name} + WHERE "{col_name}" IS NOT NULL {date_filter} + GROUP BY {pid_col_sql}, "{col_name}", "{col_brand}", "{col_vendor}", "{col_category}" + ORDER BY value DESC + LIMIT 100 + """ + + # 執行查詢 + df = pd.read_sql(sql_query, db.engine) + sys_log.info(f"[API] Top Detail: {top_type}/{view_type} 返回 {len(df)} 筆資料") + + if df.empty: + return jsonify({'items': []}) + + # V-Fix (2026-01-23): 確保數值欄位無 NaN/Infinity，避免 JSON 序列化失敗 + numeric_cols = ['value', 'margin_rate'] + for col in numeric_cols: + if col in df.columns: + df[col] = df[col].replace([np.inf, -np.inf], 0).fillna(0) + + # V-Fix (2026-01-23): 確保字串欄位無 None + string_cols = ['product_id', 'name', 'brand', 'vendor', 'category'] + for col in string_cols: + if col in df.columns: + df[col] = df[col].fillna('').astype(str) + + # 轉換為 JSON + items = df.to_dict('records') + return jsonify({'items': items}) + + except Exception as e: + sys_log.error(f"[API] Top Detail Error: {e}") + import traceback + traceback.print_exc() + return jsonify({'error': str(e)}), 500 @sales_bp.route('/api/sales_analysis/export_top_detail') @login_required def api_export_top_detail(): - """API: 匯出 Top N 明細為 Excel""" - # 延遲導入 (使用 app.py 中的原始函數名稱) - from app import export_top_detail as _original_func - return _original_func() + """API: 匯出 Top N 詳細列表為 Excel""" + try: + from datetime import datetime, timedelta, timezone + import io + TAIPEI_TZ = timezone(timedelta(hours=8)) + + table_name = 'realtime_sales_monthly' + data_range_months = int(request.args.get('data_range', '1') or '1') + start_date = request.args.get('start_date', '') # V-New: 自訂開始日期 + end_date = request.args.get('end_date', '') # V-New: 自訂結束日期 + top_type = request.args.get('type', 'revenue') + metric = request.args.get('metric', 'amount') + view_type = request.args.get('view', 'product') + + db = DatabaseManager() + + # V-Fix: 從快取讀取欄位名稱對應，以支援不同的資料庫欄位名稱 + if start_date or end_date: + cache_key = f"{table_name}_custom_{start_date}_{end_date}" + else: + cache_key = f"{table_name}_{data_range_months}m" + + # 嘗試從快取讀取欄位名稱 + cols_map = {} + if cache_key in _SALES_PROCESSED_CACHE: + cols_map = _SALES_PROCESSED_CACHE[cache_key].get('cols', {}) + elif table_name in _SALES_PROCESSED_CACHE: # V-Fix: 也嘗試使用固定 key + cols_map = _SALES_PROCESSED_CACHE[table_name].get('cols', {}) + + # 取得實際欄位名稱（如果快取中沒有，使用預設名稱） + # V-Fix (2026-01-23): 使用 or 確保不會得到 None 值 + col_name = cols_map.get('name') or '商品名稱' + col_brand = cols_map.get('brand') or '品牌' + col_vendor = cols_map.get('vendor') or '廠商名稱' + col_category = cols_map.get('category') or '商品館' + col_amount = cols_map.get('amount') or '總業績' + col_qty = cols_map.get('qty') or '數量' + col_cost = cols_map.get('cost') or '總成本' + col_profit = cols_map.get('profit') # 可以為 None + col_activity = cols_map.get('activity') or '活動名稱' + col_payment = cols_map.get('payment') or '付款方式' + + # 建立日期篩選條件 + date_filter = "" + # V-New: 優先處理自訂日期區間 + if start_date or end_date: + # V-Fix: 處理日期格式轉換 (2025-01-01 -> 2025/01/01) + start_date_slash = start_date.replace('-', '/') if start_date else '' + end_date_slash = end_date.replace('-', '/') if end_date else '' + + if start_date and end_date: + date_filter = f"""AND "日期" BETWEEN '{start_date_slash}' AND '{end_date_slash}'""" + elif start_date: + date_filter = f"""AND "日期" >= '{start_date_slash}'""" + else: # only end_date + date_filter = f"""AND "日期" <= '{end_date_slash}'""" + elif data_range_months > 0: + cutoff_date = (datetime.now(TAIPEI_TZ) - timedelta(days=data_range_months * 30)).strftime('%Y/%m/%d') + date_filter = f"""AND "日期" >= '{cutoff_date}'""" + + # V-Fix: 補上其他所有篩選條件 (與 get_top_detail 一致) + category_filter = request.args.get('category', 'all') + brand_filter = request.args.get('brand', 'all') + vendor_filter = request.args.get('vendor', 'all') + activity_filter = request.args.get('activity', 'all') + payment_filter = request.args.get('payment', 'all') + month_filter = request.args.get('month', 'all') + dow_filter = request.args.get('dow', 'all') + hour_filter = request.args.get('hour', 'all') + min_price_str = request.args.get('min_price', '') + max_price_str = request.args.get('max_price', '') + min_margin_str = request.args.get('min_margin', '') + max_margin_str = request.args.get('max_margin', '') + keyword = request.args.get('keyword', '').strip() + + additional_filters = [] + + if category_filter and category_filter != 'all': + additional_filters.append(f""""{col_category}" = '{category_filter}'""") + if brand_filter and brand_filter != 'all': + additional_filters.append(f""""{col_brand}" = '{brand_filter}'""") + if vendor_filter and vendor_filter != 'all': + additional_filters.append(f""""{col_vendor}" = '{vendor_filter}'""") + if activity_filter and activity_filter != 'all': + additional_filters.append(f""""{col_activity}" = '{activity_filter}'""") + if payment_filter and payment_filter != 'all': + additional_filters.append(f""""{col_payment}" = '{payment_filter}'""") + + if month_filter and month_filter != 'all': + month_filter_slash = month_filter.replace('-', '/') + additional_filters.append(f"""("日期" LIKE '{month_filter}%' OR "日期" LIKE '{month_filter_slash}%')""") + + if dow_filter and dow_filter != 'all': + # V-Fix (2026-01-23): 支援 PostgreSQL 和 SQLite (export API) + pandas_dow = int(dow_filter) + if DATABASE_TYPE == 'postgresql': + pg_dow = (pandas_dow + 1) % 7 + additional_filters.append(f"""EXTRACT(DOW FROM TO_DATE(REPLACE("日期", '/', '-'), 'YYYY-MM-DD')) = {pg_dow}""") + else: + sqlite_dow = str((pandas_dow + 1) % 7) + additional_filters.append(f"""strftime('%w', replace("日期", '/', '-')) = '{sqlite_dow}'""") + + if hour_filter and hour_filter != 'all': + # V-Fix (2026-01-23): 支援 PostgreSQL 和 SQLite (export API) + hour_val = int(hour_filter) + if DATABASE_TYPE == 'postgresql': + additional_filters.append(f"""CAST(SUBSTRING("時間" FROM 1 FOR 2) AS INTEGER) = {hour_val}""") + else: + additional_filters.append(f"""CAST(substr("時間", 1, 2) AS INTEGER) = {hour_val}""") + + if keyword: + keyword_escaped = keyword.replace("'", "''") + k_conds = [] + for col in [col_name, cols_map.get("pid", "商品ID"), col_brand, col_vendor]: + k_conds.append(f""""{col}" LIKE '%{keyword_escaped}%'""") + additional_filters.append(f"({' OR '.join(k_conds)})") + + if (min_price_str or max_price_str): + price_sql = f'CAST("{col_amount}" AS FLOAT) / NULLIF("{col_qty}", 0)' + if min_price_str: additional_filters.append(f"{price_sql} >= {float(min_price_str)}") + if max_price_str: additional_filters.append(f"{price_sql} <= {float(max_price_str)}") + + if (min_margin_str or max_margin_str): + if col_profit: + profit_sql = f'"{col_profit}"' + else: + profit_sql = f'("{col_amount}" - "{col_cost}")' + + margin_sql = f'({profit_sql} * 100.0 / NULLIF("{col_amount}", 0))' + if min_margin_str: additional_filters.append(f"{margin_sql} >= {float(min_margin_str)}") + if max_margin_str: additional_filters.append(f"{margin_sql} <= {float(max_margin_str)}") + + if additional_filters: + date_filter += " AND " + " AND ".join(additional_filters) + + # V-New: 準備利潤計算 SQL 片段 (SELECT 子句使用 SUM 聚合) + if col_profit: + profit_select_sql = f'SUM(CAST("{col_profit}" AS REAL))' + else: + profit_select_sql = f'SUM(CAST("{col_amount}" AS REAL)) - SUM(CAST("{col_cost}" AS REAL))' + + # 根據檢視類型和指標建立 SQL 查詢（與上面相同） + if view_type == 'category': + if metric == 'qty': + sql_query = f""" + SELECT + "{col_category}" as 分類名稱, + SUM(CAST("{col_qty}" AS REAL)) as 銷售數量 + FROM {table_name} + WHERE "{col_category}" IS NOT NULL {date_filter} + GROUP BY "{col_category}" + ORDER BY 銷售數量 DESC + LIMIT 50 + """ + elif metric == 'profit': + sql_query = f""" + SELECT + "{col_category}" as 分類名稱, + {profit_select_sql} as 毛利金額, + CASE + WHEN SUM(CAST("{col_amount}" AS REAL)) > 0 + THEN (({profit_select_sql}) / SUM(CAST("{col_amount}" AS REAL))) * 100 + ELSE 0 + END as 毛利率 + FROM {table_name} + WHERE "{col_category}" IS NOT NULL {date_filter} + GROUP BY "{col_category}" + ORDER BY 毛利金額 DESC + LIMIT 50 + """ + else: # amount + sql_query = f""" + SELECT + "{col_category}" as 分類名稱, + SUM(CAST("{col_amount}" AS REAL)) as 銷售金額 + FROM {table_name} + WHERE "{col_category}" IS NOT NULL {date_filter} + GROUP BY "{col_category}" + ORDER BY 銷售金額 DESC + LIMIT 50 + """ + else: + # 商品排行（包含商品ID） + if metric == 'qty': + sql_query = f""" + SELECT + "{cols_map.get("pid", "商品ID")}" as 商品ID, + "{col_name}" as 商品名稱, + "{col_brand}" as 品牌, + "{col_vendor}" as 廠商名稱, + "{col_category}" as 分類名稱, + SUM(CAST("{col_qty}" AS REAL)) as 銷售數量 + FROM {table_name} + WHERE "{col_name}" IS NOT NULL {date_filter} + GROUP BY "{cols_map.get("pid", "商品ID")}", "{col_name}", "{col_brand}", "{col_vendor}", "{col_category}" + ORDER BY 銷售數量 DESC + LIMIT 100 + """ + elif metric == 'profit': + sql_query = f""" + SELECT + "{cols_map.get("pid", "商品ID")}" as 商品ID, + "{col_name}" as 商品名稱, + "{col_brand}" as 品牌, + "{col_vendor}" as 廠商名稱, + "{col_category}" as 分類名稱, + {profit_select_sql} as 毛利金額, + CASE + WHEN SUM(CAST("{col_amount}" AS REAL)) > 0 + THEN (({profit_select_sql}) / SUM(CAST("{col_amount}" AS REAL))) * 100 + ELSE 0 + END as 毛利率 + FROM {table_name} + WHERE "{col_name}" IS NOT NULL {date_filter} + GROUP BY "{cols_map.get("pid", "商品ID")}", "{col_name}", "{col_brand}", "{col_vendor}", "{col_category}" + ORDER BY 毛利金額 DESC + LIMIT 100 + """ + else: # amount + sql_query = f""" + SELECT + "{cols_map.get("pid", "商品ID")}" as 商品ID, + "{col_name}" as 商品名稱, + "{col_brand}" as 品牌, + "{col_vendor}" as 廠商名稱, + "{col_category}" as 分類名稱, + SUM(CAST("{col_amount}" AS REAL)) as 銷售金額 + FROM {table_name} + WHERE "{col_name}" IS NOT NULL {date_filter} + GROUP BY "{cols_map.get("pid", "商品ID")}", "{col_name}", "{col_brand}", "{col_vendor}", "{col_category}" + ORDER BY 銷售金額 DESC + LIMIT 100 + """ + + # 執行查詢並匯出 + df = pd.read_sql(sql_query, db.engine) + + if df.empty: + return "無資料可匯出", 400 + + # 生成 Excel + output = io.BytesIO() + with pd.ExcelWriter(output, engine='openpyxl') as writer: + df.to_excel(writer, index=False, sheet_name='Top排行') + output.seek(0) + + # 生成檔案名稱 + type_names = {'revenue': '業績貢獻王', 'margin': '獲利金雞母', 'quantity': '人氣引流款'} + view_names = {'product': '商品排行', 'category': '分類排行'} + filename = f"{type_names.get(top_type, '排行')}_{view_names.get(view_type, '')}_{datetime.now(TAIPEI_TZ).strftime('%Y%m%d_%H%M')}.xlsx" + + sys_log.info(f"[Export] Top Detail: {filename} ({len(df)} 筆)") + + return send_file( + output, + as_attachment=True, + download_name=filename, + mimetype='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet' + ) + + except Exception as e: + sys_log.error(f"[Export] Top Detail Error: {e}") + return f"匯出失敗: {e}", 500 @sales_bp.route('/api/sales_analysis/yoy_comparison') @login_required def api_yoy_comparison(): - """API: 取得年對年比較數據""" - # 延遲導入 (使用 app.py 中的原始函數名稱) - from app import yoy_comparison as _original_func - return _original_func() + """ + API: 年度對比分析 (YoY Comparison) + + 參數: + year1: 基準年 (例如 2024) + year2: 對比年 (例如 2025) + month: 月份 (可選，1-12，不帶則為全年) + metric: 指標 (revenue/qty/profit) + + 回傳: + JSON with year1 total, year2 total, growth rate, and monthly breakdown + """ + try: + from datetime import datetime, timedelta, timezone + TAIPEI_TZ = timezone(timedelta(hours=8)) + + table_name = 'realtime_sales_monthly' + year1 = request.args.get('year1', '2024') + year2 = request.args.get('year2', '2025') + month = request.args.get('month', '') # 可選，1-12 + metric = request.args.get('metric', 'revenue') # revenue/qty/profit + + db = DatabaseManager() + + # 欄位名稱 + col_amount = '總業績' + col_qty = '數量' + col_cost = '總成本' + col_date = '日期' + + # 根據指標決定聚合欄位 + if metric == 'qty': + agg_sql = f'SUM(CAST("{col_qty}" AS REAL))' + metric_label = '銷售數量' + elif metric == 'profit': + agg_sql = f'SUM(CAST("{col_amount}" AS REAL)) - SUM(CAST("{col_cost}" AS REAL))' + metric_label = '毛利金額' + else: # revenue + agg_sql = f'SUM(CAST("{col_amount}" AS REAL))' + metric_label = '銷售金額' + + # 建立年度篩選條件 + # 日期格式為 2025/01/01 或 2025-01-01 + def build_year_filter(year, month_filter=''): + if month_filter: + month_str = month_filter.zfill(2) + return f"""("{col_date}" LIKE '{year}/{month_str}%' OR "{col_date}" LIKE '{year}-{month_str}%')""" + else: + return f"""("{col_date}" LIKE '{year}/%' OR "{col_date}" LIKE '{year}-%')""" + + year1_filter = build_year_filter(year1, month) + year2_filter = build_year_filter(year2, month) + + # 查詢年度總計 + sql_year1 = f""" + SELECT {agg_sql} as total + FROM {table_name} + WHERE {year1_filter} + """ + sql_year2 = f""" + SELECT {agg_sql} as total + FROM {table_name} + WHERE {year2_filter} + """ + + # V-Fix: SQLAlchemy 2.0 需要使用 text() 包裹 SQL 字串 + from sqlalchemy import text + result_year1 = pd.read_sql(text(sql_year1), db.engine) + result_year2 = pd.read_sql(text(sql_year2), db.engine) + + total_year1 = float(result_year1['total'].iloc[0] or 0) + total_year2 = float(result_year2['total'].iloc[0] or 0) + + # 計算成長率 + if total_year1 > 0: + growth_rate = ((total_year2 - total_year1) / total_year1) * 100 + else: + growth_rate = 0 if total_year2 == 0 else 100 + + # 月度明細 (如果沒有指定月份，則查詢 12 個月的明細) + monthly_breakdown = [] + if not month: + for m in range(1, 13): + m_str = str(m).zfill(2) + y1_filter = build_year_filter(year1, m_str) + y2_filter = build_year_filter(year2, m_str) + + sql_m1 = f"SELECT {agg_sql} as total FROM {table_name} WHERE {y1_filter}" + sql_m2 = f"SELECT {agg_sql} as total FROM {table_name} WHERE {y2_filter}" + + # V-Fix: SQLAlchemy 2.0 需要使用 text() + r1 = pd.read_sql(text(sql_m1), db.engine) + r2 = pd.read_sql(text(sql_m2), db.engine) + + v1 = float(r1['total'].iloc[0] or 0) + v2 = float(r2['total'].iloc[0] or 0) + + m_growth = ((v2 - v1) / v1 * 100) if v1 > 0 else (0 if v2 == 0 else 100) + + monthly_breakdown.append({ + 'month': m, + 'month_label': f'{m}月', + 'year1_value': v1, + 'year2_value': v2, + 'growth_rate': round(m_growth, 2) + }) + + response = { + 'year1': { + 'label': f'{year1}年' + (f'{month}月' if month else ''), + 'total': total_year1 + }, + 'year2': { + 'label': f'{year2}年' + (f'{month}月' if month else ''), + 'total': total_year2 + }, + 'growth_rate': round(growth_rate, 2), + 'metric': metric, + 'metric_label': metric_label, + 'monthly_breakdown': monthly_breakdown + } + + sys_log.info(f"[YoY] {year1} vs {year2}: {total_year1:,.0f} -> {total_year2:,.0f} ({growth_rate:+.1f}%)") + + return jsonify(response) + + except Exception as e: + sys_log.error(f"[YoY] Error: {e}") + traceback.print_exc() + return jsonify({'error': str(e)}), 500