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- 建立 Gitea Actions CD pipeline (.gitea/workflows/cd.yaml) - 部署模式: rsync Python 檔案至 188 → docker restart (volume mount) - Dockerfile/requirements 變動時自動重建 Docker image - 部署通知: Telegram (開始/成功/失敗) - 健康檢查: https://mo.wooo.work/health (最多 5 次重試) - 同步最新 CLAUDE.md / ADR-008 / memory (2026-04-19) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
642 lines
25 KiB
Python
642 lines
25 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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匯出功能路由模組
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包含：各種報表匯出 API (Excel、CSV)
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"""
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import os
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import io
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from datetime import datetime, timezone, timedelta
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from flask import Blueprint, request, send_file, redirect, url_for, flash
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from auth import login_required
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from sqlalchemy import func, desc
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import pandas as pd
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import numpy as np
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from config import BASE_DIR, EXCEL_EXPORT_DIR
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from database.manager import DatabaseManager
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from database.models import Product, PriceRecord
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from services.exporter import Exporter
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from services.logger_manager import SystemLogger
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# 時區設定
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TAIPEI_TZ = timezone(timedelta(hours=8))
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# Logger
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sys_log = SystemLogger("ExportRoutes").get_logger()
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# Blueprint 定義
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export_bp = Blueprint('export', __name__)
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# ==========================================
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# 輔助函數 (使用獨立模組，避免循環依賴)
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# ==========================================
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def _get_consolidated_data():
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"""從 dashboard_routes 模組導入 get_consolidated_data 函數"""
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from routes.dashboard_routes import get_consolidated_data
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return get_consolidated_data()
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def _get_sales_cache():
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"""從 cache_service 導入業績分析快取"""
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from services.cache_service import _SALES_PROCESSED_CACHE
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return _SALES_PROCESSED_CACHE
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# ==========================================
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# 全分類匯出
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# ==========================================
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@export_bp.route('/api/export/all_categories')
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@login_required
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def export_all_categories():
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"""處理全分類報表匯出請求"""
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try:
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sys_log.info("執行全分類 CSV 數據導出...")
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# 獲取與看板一致的整合數據
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items, _ = _get_consolidated_data()
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# 呼叫匯出服務
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exporter = Exporter()
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file_path = exporter.generate_all_categories_report()
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if file_path:
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abs_file_path = os.path.abspath(file_path)
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if os.path.exists(abs_file_path):
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sys_log.info(f"報表匯出成功，準備下載: {abs_file_path}")
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return send_file(abs_file_path, as_attachment=True)
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return "匯出失敗：資料庫內尚無足夠數據", 404
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except Exception as e:
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sys_log.error(f"[Web] [Export] 全分類報表匯出異常 | Error: {e}")
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return f"匯出失敗，錯誤詳情：{e}", 500
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@export_bp.route('/api/export/excel/all')
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@login_required
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def export_excel_all():
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"""匯出所有商品 Excel"""
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try:
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items, _ = _get_consolidated_data()
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exporter = Exporter()
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file_path = exporter.generate_all_products_excel(items)
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if file_path and os.path.exists(file_path):
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return send_file(file_path, as_attachment=True)
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return "匯出失敗", 500
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except Exception as e:
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sys_log.error(f"[Web] [Export] Excel 匯出失敗 (All) | Error: {e}")
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return f"匯出失敗: {e}", 500
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# ==========================================
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# 價格變動匯出
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# ==========================================
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@export_bp.route('/api/export/excel/changes')
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@login_required
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def export_excel_changes():
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"""匯出價格變動商品 Excel (漲價/跌價)"""
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try:
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items, _ = _get_consolidated_data()
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increase = [i for i in items if i['yesterday_diff'] > 0]
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decrease = [i for i in items if i['yesterday_diff'] < 0]
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exporter = Exporter()
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file_path = exporter.generate_changes_excel(increase, decrease)
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if file_path and os.path.exists(file_path):
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return send_file(file_path, as_attachment=True)
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return "匯出失敗", 500
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except Exception as e:
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sys_log.error(f"[Web] [Export] Excel 匯出失敗 (Changes) | Error: {e}")
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return f"匯出失敗: {e}", 500
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@export_bp.route('/api/export/excel/delisted')
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@login_required
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def export_excel_delisted():
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"""匯出下架商品 Excel"""
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db = DatabaseManager()
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session = db.get_session()
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try:
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_, today_start = _get_consolidated_data()
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today_delisted_query = session.query(Product).filter(
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Product.status == 'INACTIVE',
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Product.updated_at >= today_start # 保持台北時區
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)
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raw_items = today_delisted_query.all()
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delisted_items = [{
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'product': p,
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'last_price': (session.query(PriceRecord).filter_by(product_id=p.id)
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.order_by(desc(PriceRecord.timestamp)).first().price
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if session.query(PriceRecord).filter_by(product_id=p.id).first() else 0)
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} for p in raw_items]
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exporter = Exporter()
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file_path = exporter.generate_delisted_excel(delisted_items)
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return send_file(file_path, as_attachment=True)
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except Exception as e:
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sys_log.error(f"[Web] [Export] Excel 匯出失敗 (Delisted) | Error: {e}")
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return f"匯出失敗: {e}", 500
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finally:
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session.close()
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@export_bp.route('/api/export/price_changes')
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@login_required
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def export_price_changes():
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"""匯出今日價格異動明細 (支援篩選)"""
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import openpyxl
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from openpyxl.styles import Font, Alignment, PatternFill
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filter_type = request.args.get('type', '')
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filter_category = request.args.get('category', '')
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try:
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db = DatabaseManager()
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session = db.get_session()
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now_taipei = datetime.now(TAIPEI_TZ)
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today_start = now_taipei.replace(hour=0, minute=0, second=0, microsecond=0, tzinfo=None)
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# 基礎查詢：取得所有商品的最新記錄
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latest_records_subq = session.query(
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func.max(PriceRecord.id).label('max_id')
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).group_by(PriceRecord.product_id).subquery()
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query = session.query(PriceRecord, Product).join(
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latest_records_subq,
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PriceRecord.id == latest_records_subq.c.max_id
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).join(Product, PriceRecord.product_id == Product.id)
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# 查詢所有商品的「今日之前最後價格」
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product_ids = [r[0] for r in session.query(PriceRecord.product_id).join(
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latest_records_subq, PriceRecord.id == latest_records_subq.c.max_id
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).all()]
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yesterday_prices_subq = session.query(
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PriceRecord.product_id,
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func.max(PriceRecord.id).label('max_id')
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).filter(
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PriceRecord.product_id.in_(product_ids),
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PriceRecord.timestamp < today_start
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).group_by(PriceRecord.product_id).subquery()
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yesterday_prices_q = session.query(
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PriceRecord.product_id, PriceRecord.price
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).join(
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yesterday_prices_subq,
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PriceRecord.id == yesterday_prices_subq.c.max_id
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)
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yesterday_prices_map = {pid: price for pid, price in yesterday_prices_q}
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products = []
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# 根據 filter_type 篩選
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if filter_type == 'increase':
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for record, product in query.all():
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old_price = yesterday_prices_map.get(product.id)
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if old_price is not None and record.price > old_price:
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products.append((product, record, old_price))
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elif filter_type == 'decrease':
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for record, product in query.all():
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old_price = yesterday_prices_map.get(product.id)
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if old_price is not None and record.price < old_price:
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products.append((product, record, old_price))
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elif filter_type == 'delisted':
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today_delisted = session.query(Product).filter(
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Product.status == 'INACTIVE',
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Product.updated_at >= today_start
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).all()
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for product in today_delisted:
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last_record = session.query(PriceRecord).filter(
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PriceRecord.product_id == product.id
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).order_by(PriceRecord.timestamp.desc()).first()
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if last_record:
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products.append((product, last_record, last_record.price))
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elif filter_type == 'active':
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for record, product in query.all():
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old_price = yesterday_prices_map.get(product.id)
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if old_price is not None and record.price != old_price:
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products.append((product, record, old_price))
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elif filter_type == 'category' and filter_category:
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for record, product in query.filter(Product.category == filter_category).all():
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old_price = yesterday_prices_map.get(product.id)
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if old_price is not None and record.price != old_price:
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products.append((product, record, old_price))
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else:
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# 預設：所有變動商品
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for record, product in query.all():
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old_price = yesterday_prices_map.get(product.id)
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if old_price is not None and record.price != old_price:
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products.append((product, record, old_price))
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session.close()
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if not products:
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return "無符合條件的商品資料", 404
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# 建立 Excel
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wb = openpyxl.Workbook()
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ws = wb.active
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ws.title = "價格變動明細"
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# 標題列
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headers = ['商品ID', '商品名稱', '分類', '原價格', '現價格', '變動金額', '變動百分比', '更新時間', '商品網址']
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ws.append(headers)
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# 設定標題列樣式
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header_fill = PatternFill(start_color='4472C4', end_color='4472C4', fill_type='solid')
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header_font = Font(bold=True, color='FFFFFF')
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for cell in ws[1]:
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cell.fill = header_fill
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cell.font = header_font
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cell.alignment = Alignment(horizontal='center', vertical='center')
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# 填充資料
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for product, record, old_price in products:
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change = record.price - old_price
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change_pct = (change / old_price * 100) if old_price > 0 else 0
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ws.append([
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product.i_code,
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product.name,
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product.category or '未分類',
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old_price,
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record.price,
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change,
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f"{change_pct:.2f}%",
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record.timestamp.strftime('%Y-%m-%d %H:%M'),
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product.url
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])
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# 調整欄寬
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ws.column_dimensions['A'].width = 12
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ws.column_dimensions['B'].width = 40
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ws.column_dimensions['C'].width = 15
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ws.column_dimensions['D'].width = 12
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ws.column_dimensions['E'].width = 12
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ws.column_dimensions['F'].width = 12
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ws.column_dimensions['G'].width = 12
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ws.column_dimensions['H'].width = 18
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ws.column_dimensions['I'].width = 50
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# 儲存檔案
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timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
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filename = f"價格變動明細_{filter_type or 'all'}_{timestamp}.xlsx"
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filepath = os.path.join(EXCEL_EXPORT_DIR, filename)
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os.makedirs(EXCEL_EXPORT_DIR, exist_ok=True)
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wb.save(filepath)
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return send_file(filepath, as_attachment=True, download_name=filename)
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except Exception as e:
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sys_log.error(f"[Web] [Export] 異動報表匯出失敗 | Type: {filter_type} | Error: {e}")
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return f"匯出失敗: {e}", 500
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# ==========================================
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# 其他匯出功能
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# ==========================================
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@export_bp.route('/api/export/low_prices')
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@login_required
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def export_low_prices():
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"""匯出歷史低價商品"""
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try:
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exporter = Exporter()
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file_path = exporter.generate_low_price_report()
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if file_path and os.path.exists(file_path):
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return send_file(file_path, as_attachment=True)
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return "目前無歷史低價商品", 404
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except Exception as e:
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sys_log.error(f"[Web] [Export] 低價報表匯出失敗 | Error: {e}")
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return f"匯出失敗: {e}", 500
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@export_bp.route('/api/export/changes')
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@login_required
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def export_changes():
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"""匯出篩選後的資料 (漲/跌/下架)"""
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filter_type = request.args.get('type')
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exporter = Exporter()
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file_path = None
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try:
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unique_items, today_start = _get_consolidated_data()
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if filter_type == 'increase':
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target_items = [i for i in unique_items if i['yesterday_diff'] > 0]
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file_path = exporter.generate_custom_report(target_items, "今日漲價商品")
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elif filter_type == 'decrease':
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target_items = [i for i in unique_items if i['yesterday_diff'] < 0]
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file_path = exporter.generate_custom_report(target_items, "今日跌價商品")
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elif filter_type == 'delisted':
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db = DatabaseManager()
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session = db.get_session()
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try:
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today_delisted_query = session.query(Product).filter(
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Product.status == 'INACTIVE',
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Product.updated_at >= today_start # 保持台北時區
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)
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raw_delisted_items = today_delisted_query.all()
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delisted_items_with_price = []
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for p in raw_delisted_items:
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last_rec = session.query(PriceRecord).filter_by(product_id=p.id).order_by(
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desc(PriceRecord.timestamp)).first()
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price = last_rec.price if last_rec else 0
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delisted_items_with_price.append({'product': p, 'last_price': price})
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file_path = exporter.generate_delisted_report(delisted_items_with_price, "今日下架商品")
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finally:
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session.close()
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if file_path and os.path.exists(file_path):
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return send_file(file_path, as_attachment=True)
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return "無資料可匯出", 404
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except Exception as e:
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sys_log.error(f"[Web] [Export] 篩選匯出失敗 | Type: {filter_type} | Error: {e}")
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return f"匯出失敗: {e}", 500
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@export_bp.route('/api/export/excel/abc')
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@login_required
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def export_abc_analysis():
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"""匯出 ABC 分析報表 (Excel)"""
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try:
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table_name = 'realtime_sales_monthly'
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_SALES_PROCESSED_CACHE = _get_sales_cache()
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# 嘗試從快取讀取資料
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df = None
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cols_map = {}
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if table_name in _SALES_PROCESSED_CACHE:
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cache_data = _SALES_PROCESSED_CACHE[table_name]
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df = cache_data['df']
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cols_map = cache_data['cols']
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else:
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return "請先瀏覽「業績分析」頁面以載入資料與快取。", 400
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# 恢復欄位變數
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col_name = cols_map.get('name')
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col_amount = cols_map.get('amount')
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col_qty = cols_map.get('qty')
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col_category = cols_map.get('category')
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col_brand = cols_map.get('brand')
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col_vendor = cols_map.get('vendor')
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col_cost = cols_map.get('cost')
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col_profit = cols_map.get('profit')
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col_date = cols_map.get('date')
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col_pid = cols_map.get('pid')
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# 篩選資料
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selected_category = request.args.get('category', 'all')
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selected_brand = request.args.get('brand', 'all')
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selected_vendor = request.args.get('vendor', 'all')
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keyword = request.args.get('keyword', '').strip()
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min_price = request.args.get('min_price', '')
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max_price = request.args.get('max_price', '')
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min_margin = request.args.get('min_margin', '')
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max_margin = request.args.get('max_margin', '')
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target_df = df.copy()
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# 重新計算 Top N 分類
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TOP_N_CATS = 12
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top_cats_names = []
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if col_category:
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cat_group_all = df.groupby(col_category)[col_amount].sum().sort_values(ascending=False)
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if len(cat_group_all) > TOP_N_CATS:
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top_cats_names = cat_group_all.head(TOP_N_CATS).index.tolist()
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if selected_category != 'all' and col_category:
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if selected_category == '其他' and top_cats_names:
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target_df = target_df[~target_df[col_category].isin(top_cats_names)]
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else:
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target_df = target_df[target_df[col_category] == selected_category]
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if selected_brand != 'all' and col_brand:
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target_df = target_df[target_df[col_brand] == selected_brand]
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if selected_vendor != 'all' and col_vendor:
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target_df = target_df[target_df[col_vendor] == selected_vendor]
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if keyword:
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target_df = target_df[target_df[col_name].astype(str).str.contains(keyword, case=False, na=False)]
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if min_margin:
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target_df = target_df[target_df['calculated_margin_rate'] >= float(min_margin)]
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if max_margin:
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target_df = target_df[target_df['calculated_margin_rate'] <= float(max_margin)]
|
|
|
|
# 執行 ABC 分析與匯出
|
|
if col_amount and not target_df.empty:
|
|
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_category:
|
|
agg_rules[col_category] = 'first'
|
|
if col_vendor:
|
|
agg_rules[col_vendor] = 'first'
|
|
if col_brand:
|
|
agg_rules[col_brand] = 'first'
|
|
if col_pid:
|
|
agg_rules[col_pid] = 'first'
|
|
|
|
df_agg = target_df.groupby(col_name).agg(agg_rules).reset_index()
|
|
|
|
# 計算毛利率
|
|
if col_profit:
|
|
df_agg['calculated_margin_rate'] = (df_agg[col_profit] / df_agg[col_amount]) * 100
|
|
elif col_cost:
|
|
df_agg['calculated_margin_rate'] = ((df_agg[col_amount] - df_agg[col_cost]) / df_agg[col_amount]) * 100
|
|
else:
|
|
df_agg['calculated_margin_rate'] = 0.0
|
|
df_agg['calculated_margin_rate'] = df_agg['calculated_margin_rate'].replace([np.inf, -np.inf, np.nan], 0)
|
|
|
|
# 排序與 ABC 分類
|
|
target_df = df_agg.sort_values(by=col_amount, ascending=False)
|
|
target_df['cumulative_revenue'] = target_df[col_amount].cumsum()
|
|
total_revenue = target_df[col_amount].sum()
|
|
target_df['cumulative_pct'] = (target_df['cumulative_revenue'] / total_revenue) * 100
|
|
|
|
conditions = [(target_df['cumulative_pct'] <= 80), (target_df['cumulative_pct'] <= 95)]
|
|
choices = ['A', 'B']
|
|
target_df['ABC_Class'] = np.select(conditions, choices, default='C')
|
|
|
|
# 支援依類別篩選匯出
|
|
filter_class = request.args.get('class')
|
|
if filter_class:
|
|
target_df = target_df[target_df['ABC_Class'] == filter_class]
|
|
|
|
# 計算平均單價
|
|
if col_qty:
|
|
target_df['avg_unit_price'] = (target_df[col_amount] / target_df[col_qty]).fillna(0)
|
|
|
|
# 計算建議補貨量
|
|
if col_qty:
|
|
custom_factor = request.args.get('factor')
|
|
if custom_factor:
|
|
try:
|
|
factor = float(custom_factor)
|
|
target_df['suggested_restock'] = (target_df[col_qty] * factor).astype(int)
|
|
except:
|
|
conditions_restock = [(target_df['ABC_Class'] == 'A'), (target_df['ABC_Class'] == 'B')]
|
|
choices_restock = [target_df[col_qty] * 1.5, target_df[col_qty] * 1.2]
|
|
target_df['suggested_restock'] = np.select(conditions_restock, choices_restock, default=0).astype(int)
|
|
else:
|
|
conditions_restock = [(target_df['ABC_Class'] == 'A'), (target_df['ABC_Class'] == 'B')]
|
|
choices_restock = [target_df[col_qty] * 1.5, target_df[col_qty] * 1.2]
|
|
target_df['suggested_restock'] = np.select(conditions_restock, choices_restock, default=0).astype(int)
|
|
|
|
# 整理匯出欄位
|
|
export_cols = []
|
|
header_map = {}
|
|
if col_pid:
|
|
export_cols.append(col_pid)
|
|
header_map[col_pid] = '商品ID'
|
|
if col_name:
|
|
export_cols.append(col_name)
|
|
header_map[col_name] = '商品名稱'
|
|
if col_category:
|
|
export_cols.append(col_category)
|
|
header_map[col_category] = '分類'
|
|
if col_brand:
|
|
export_cols.append(col_brand)
|
|
header_map[col_brand] = '品牌'
|
|
if col_vendor:
|
|
export_cols.append(col_vendor)
|
|
header_map[col_vendor] = '廠商'
|
|
export_cols.append('ABC_Class')
|
|
header_map['ABC_Class'] = 'ABC分類'
|
|
if col_amount:
|
|
export_cols.append(col_amount)
|
|
header_map[col_amount] = '銷售金額'
|
|
if col_qty:
|
|
export_cols.append(col_qty)
|
|
header_map[col_qty] = '銷售數量'
|
|
if 'avg_unit_price' in target_df.columns:
|
|
export_cols.append('avg_unit_price')
|
|
header_map['avg_unit_price'] = '平均單價'
|
|
if col_cost:
|
|
export_cols.append(col_cost)
|
|
header_map[col_cost] = '成本'
|
|
if col_profit:
|
|
export_cols.append(col_profit)
|
|
header_map[col_profit] = '毛利'
|
|
if 'calculated_margin_rate' in target_df.columns:
|
|
export_cols.append('calculated_margin_rate')
|
|
header_map['calculated_margin_rate'] = '毛利率(%)'
|
|
if 'suggested_restock' in target_df.columns:
|
|
export_cols.append('suggested_restock')
|
|
header_map['suggested_restock'] = '建議補貨量'
|
|
|
|
export_df = target_df[export_cols].rename(columns=header_map)
|
|
|
|
output = io.BytesIO()
|
|
with pd.ExcelWriter(output, engine='openpyxl') as writer:
|
|
export_df.to_excel(writer, index=False, sheet_name='ABC分析')
|
|
output.seek(0)
|
|
|
|
filename_prefix = f"ABC_Analysis_{filter_class}_" if filter_class else "ABC_Analysis_"
|
|
return send_file(
|
|
output,
|
|
as_attachment=True,
|
|
download_name=f"{filename_prefix}{datetime.now().strftime('%Y%m%d_%H%M')}.xlsx",
|
|
mimetype='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
|
|
)
|
|
|
|
return "無資料可匯出", 404
|
|
except Exception as e:
|
|
sys_log.error(f"ABC Export Error: {e}")
|
|
return f"匯出失敗: {e}", 500
|
|
|
|
|
|
@export_bp.route('/api/export/excel/vendor')
|
|
@login_required
|
|
def export_vendor_analysis():
|
|
"""匯出廠商獲利能力排行 (Excel)"""
|
|
try:
|
|
table_name = 'realtime_sales_monthly'
|
|
_SALES_PROCESSED_CACHE = _get_sales_cache()
|
|
|
|
# 嘗試從快取讀取資料
|
|
df = None
|
|
cols_map = {}
|
|
|
|
if table_name in _SALES_PROCESSED_CACHE:
|
|
cache_data = _SALES_PROCESSED_CACHE[table_name]
|
|
df = cache_data['df']
|
|
cols_map = cache_data['cols']
|
|
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))
|
|
|
|
col_vendor = cols_map.get('vendor')
|
|
col_amount = cols_map.get('amount')
|
|
col_profit = cols_map.get('profit')
|
|
col_cost = cols_map.get('cost')
|
|
|
|
if not col_vendor or not col_amount:
|
|
return "資料缺少必要欄位（廠商、銷售金額）", 400
|
|
|
|
# 按廠商聚合
|
|
agg_rules = {col_amount: 'sum'}
|
|
if col_profit:
|
|
agg_rules[col_profit] = 'sum'
|
|
if col_cost:
|
|
agg_rules[col_cost] = 'sum'
|
|
|
|
vendor_df = df.groupby(col_vendor).agg(agg_rules).reset_index()
|
|
|
|
# 計算毛利率
|
|
if col_profit:
|
|
vendor_df['margin_rate'] = (vendor_df[col_profit] / vendor_df[col_amount]) * 100
|
|
elif col_cost:
|
|
vendor_df['margin_rate'] = ((vendor_df[col_amount] - vendor_df[col_cost]) / vendor_df[col_amount]) * 100
|
|
else:
|
|
vendor_df['margin_rate'] = 0
|
|
|
|
vendor_df['margin_rate'] = vendor_df['margin_rate'].replace([np.inf, -np.inf, np.nan], 0)
|
|
|
|
# 排序
|
|
vendor_df = vendor_df.sort_values(by=col_amount, ascending=False)
|
|
|
|
# 重命名欄位
|
|
rename_map = {col_vendor: '廠商', col_amount: '銷售金額', 'margin_rate': '毛利率(%)'}
|
|
if col_profit:
|
|
rename_map[col_profit] = '毛利'
|
|
if col_cost:
|
|
rename_map[col_cost] = '成本'
|
|
|
|
export_df = vendor_df.rename(columns=rename_map)
|
|
|
|
output = io.BytesIO()
|
|
with pd.ExcelWriter(output, engine='openpyxl') as writer:
|
|
export_df.to_excel(writer, index=False, sheet_name='廠商分析')
|
|
output.seek(0)
|
|
|
|
return send_file(
|
|
output,
|
|
as_attachment=True,
|
|
download_name=f"Vendor_Analysis_{datetime.now().strftime('%Y%m%d_%H%M')}.xlsx",
|
|
mimetype='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
|
|
)
|
|
|
|
except Exception as e:
|
|
sys_log.error(f"Vendor Export Error: {e}")
|
|
return f"匯出失敗: {e}", 500
|