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houhuan 975f9e5887 优化: Docker 环境下的下载性能和网络稳定性

- Chromium 启动参数优化：禁用 dev-shm 和 GPU 加速，防止Docker内存不足
- 增加所有超时时间：login/navigate/export 超时 30s，下载超时 300s
- 改进网络延迟处理：增加数据加载等待时间，添加网络加载检测
- Docker Compose 资源配置：限制 2 CPU / 2GB 内存，DNS 配置国际公共 DNS
- Dockerfile 优化：添加 PYTHONHASHSEED 环境变量，跳过浏览器下载校验
- 新增 docker-debug.sh 脚本：便捷测试 Docker 容器中的下载功能
- 新增 .dockerignore：加速 Docker 构建，减少镜像大小

Docker 下载现在支持更长的网络延迟和更大的数据量

2026-05-17 16:09:55 +08:00

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CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

SalesShow is a monolithic Flask web application for analyzing sales data from Excel files. It supports manual Excel uploads and automated daily downloads from secsion.com via Playwright browser automation. There is no database — all data lives as Excel files on disk in uploads/.

Commands

# Install dependencies
pip install -r requirements.txt

# Run development server (Flask on port 5000, debug via FLASK_DEBUG env var)
python app.py

# Run with Docker (builds image, installs Playwright Chromium, port 5000)
docker-compose up -d

# Production
gunicorn -w 4 -b 0.0.0.0:8000 app:app

# CLI automation — download reports from secsion.com
python -m automation.secsion --start 2026-04-28 --end 2026-04-28

No test framework or linter is configured in this project.

Architecture

Backend (Flask, single app.py):

Routes handle file upload (/upload), file listing (/files), data loading/processing (/load/<filename>), deletion, and cleanup.
process_sales_data() (~lines 371-575 in app.py) is the core logic. It uses a state-machine approach to handle two Excel formats: "flat tables" (each row has code + product) and "hierarchical tables" (code row is a header, product rows are children). Outputs daily summaries with per-product breakdowns.
find_header_row() dynamically detects the header row by scanning first 20 rows for keyword matches.

Automation module (automation/):

secsion.py — SecsionDownloader uses Playwright headless Chromium to log into secsion.com, navigate to reports, set date range via TDesign date picker, optionally inject shop_id via route interception, and download exports.
uploader.py — copies downloaded files into uploads/ with timestamp-prefix naming (same convention as manual uploads).
scheduler.py — APScheduler BackgroundScheduler with CronTrigger runs daily auto-download (default 01:00).

Configuration (config.py):

Three-tier priority: Web UI settings (data/config.json) > environment variables (.env / system env) > defaults.
Config class provides static methods for reading/writing secsion credentials, shop ID, and scheduler settings.

Frontend (vanilla JS/CSS, no build step):

main.js — all client-side interactivity: file upload (drag-and-drop), AJAX to API, data rendering (card/table view), client-side filtering, sorting, pagination (50 items/page), export.
style.css — Glassmorphism design with CSS custom properties.
settings.html — self-contained settings page with inline <script> (no separate JS file).

Key Design Decisions

No database — Excel files on disk are the data store.
No frontend build step — vanilla JS/CSS served directly via Flask static files.
Playwright automation runs in daemon threads with a global download_status dict for status tracking.
Passwords are masked (******) when returned via the API.