chengma
b05a856796
新增 shopee_update 配置段，默认关闭真实提交和封面更新，限制测试商品ID、单次最大更新条数，并支持成功后关闭本轮自动新开编辑页。 Tab⑤ 设置页新增 Shopee 更新安全表单；Tab③ 开始更新前先读取安全配置并阻断未授权真实提交、超量、非测试商品和未允许的封面更新。 ApplyWorker 将 close_success_tab 传入 editor.apply_task；editor 仅在提交成功且页面为本轮自动新开时关闭 tab，失败和复用页面保留。 同步架构、API、路由、任务看板、当前状态与 progress 文档；补充 GUI 与 editor 单测覆盖安全设置保存、共享配置读取、安全拦截和 tab 关闭策略。
428 lines
14 KiB
Python
428 lines
14 KiB
Python
"""Application-level configuration for cmshopee.
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This module owns `config.json` plus `config/ai_models.json`. `config.json`
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stores local app settings and AI role/generation parameters. AI provider
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definitions and local plaintext API keys live in ignored `config/ai_models.json`.
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"""
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import copy
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import json
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import os
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import urllib.error
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import urllib.request
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CONFIG_PATH = "config.json"
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AI_MODELS_PATH = os.path.join("config", "ai_models.json")
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CATEGORIES = {"text", "image"}
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API_TYPES = {"chat", "images_edits", "auto"}
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DEFAULT_CONFIG = {
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"chrome_path": r"C:\Program Files\Google\Chrome\Application\chrome.exe",
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"user_data_root": "chrome_user_data_dir",
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"image_dir": "images",
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"db_path": "cmshopee.db",
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"default_debug_port": 9222,
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"debug_port_range": [9222, 9260],
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"cdp_ready_timeout": 60,
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"ai": {
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"default_text_model": "GPT-5.5 文本",
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"default_image_model": "Nano Banana 2",
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"title_concurrency": 4,
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"image_concurrency": 4,
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"retry": 2,
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"jpg_quality": 90,
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"resolution": "1k",
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"resolution_timeouts": {
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"512": 180,
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"1k": 240,
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"2k": 360,
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"4k": 600,
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},
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},
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"shopee_update": {
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"test_item_id": "51100639510",
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"allow_real_submit": False,
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"allow_cover_update": False,
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"max_items_per_run": 1,
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"close_success_tab": False,
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},
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}
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DEFAULT_AI_MODELS_CONFIG = {
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"models": [
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{
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"name": "GPT-5.5 文本",
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"category": "text",
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"enabled": True,
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"url": "",
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"model": "",
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"api_key": "",
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"api_type": "chat",
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"connect_timeout_seconds": 30,
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"timeout_seconds": 0,
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"extra_body": {},
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},
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{
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"name": "Nano Banana 2",
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"category": "image",
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"enabled": True,
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"url": "https://api.vectorengine.ai/v1/chat/completions",
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"model": "gemini-3.1-flash-image-preview",
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"api_key": "",
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"api_type": "auto",
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"connect_timeout_seconds": 30,
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"timeout_seconds": 0,
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"extra_body": {},
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},
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]
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}
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SECRET_FIELD_NAMES = {"api_key", "apikey", "key", "token", "password"}
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class ConfigError(RuntimeError):
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"""Raised when app configuration is missing or malformed."""
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def default_config() -> dict:
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"""Return a new copy of the default config."""
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return copy.deepcopy(DEFAULT_CONFIG)
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def default_ai_models_config() -> dict:
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"""Return a new copy of the default AI model list."""
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return copy.deepcopy(DEFAULT_AI_MODELS_CONFIG)
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def _deep_merge(defaults, loaded):
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if not isinstance(defaults, dict):
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return copy.deepcopy(loaded) if loaded is not None else copy.deepcopy(defaults)
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merged = copy.deepcopy(defaults)
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if not isinstance(loaded, dict):
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return merged
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for key, value in loaded.items():
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if isinstance(merged.get(key), dict) and isinstance(value, dict):
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merged[key] = _deep_merge(merged[key], value)
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else:
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merged[key] = copy.deepcopy(value)
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return merged
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def _assert_no_secrets(config):
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def visit(value, path):
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if isinstance(value, dict):
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for key, child in value.items():
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lowered = str(key).lower()
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if lowered in SECRET_FIELD_NAMES or lowered.endswith(
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("_key", "_token", "_password")
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):
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raise ConfigError(
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f"config.json 不允许保存敏感字段: {'.'.join(path + [str(key)])}"
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)
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visit(child, path + [str(key)])
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elif isinstance(value, list):
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for index, child in enumerate(value):
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visit(child, path + [str(index)])
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visit(config, [])
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def save_config(config, path=CONFIG_PATH) -> dict:
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"""Persist config to JSON and return the normalized config."""
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normalized = _deep_merge(DEFAULT_CONFIG, config)
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_assert_no_secrets(normalized)
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directory = os.path.dirname(os.path.abspath(path))
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if directory:
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os.makedirs(directory, exist_ok=True)
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with open(path, "w", encoding="utf-8") as fh:
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json.dump(normalized, fh, ensure_ascii=False, indent=2)
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fh.write("\n")
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return normalized
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def load_config(path=CONFIG_PATH) -> dict:
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"""Load config, writing defaults first if the file does not exist."""
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if not os.path.exists(path):
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return save_config(default_config(), path=path)
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with open(path, "r", encoding="utf-8") as fh:
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try:
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loaded = json.load(fh)
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except json.JSONDecodeError as exc:
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raise ConfigError(f"配置文件不是有效 JSON: {path}") from exc
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normalized = _deep_merge(DEFAULT_CONFIG, loaded)
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_assert_no_secrets(normalized)
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return normalized
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def update_config(updates, path=CONFIG_PATH) -> dict:
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"""Merge updates into the persisted config."""
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config = load_config(path)
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return save_config(_deep_merge(config, updates), path=path)
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def _config_or_load(config):
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return load_config() if config is None else config
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def chrome_path(config=None) -> str:
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return _config_or_load(config).get("chrome_path", "")
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def user_data_root(config=None) -> str:
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return _config_or_load(config).get("user_data_root", "chrome_user_data_dir")
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def image_dir(config=None) -> str:
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return _config_or_load(config).get("image_dir", "images")
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def db_path(config=None) -> str:
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return _config_or_load(config).get("db_path", "cmshopee.db")
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def default_debug_port(config=None) -> int:
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return int(_config_or_load(config).get("default_debug_port", 9222))
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def debug_port_range(config=None) -> tuple:
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values = _config_or_load(config).get("debug_port_range", [9222, 9260])
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if not isinstance(values, list) or len(values) != 2:
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raise ConfigError("debug_port_range 必须是 [start, end]")
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return int(values[0]), int(values[1])
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def cdp_ready_timeout(config=None) -> int:
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return int(_config_or_load(config).get("cdp_ready_timeout", 60))
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def ai_config(config=None) -> dict:
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return copy.deepcopy(_config_or_load(config).get("ai", DEFAULT_CONFIG["ai"]))
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def response_timeout(config=None) -> int:
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ai = ai_config(config)
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resolution = str(ai.get("resolution", DEFAULT_CONFIG["ai"]["resolution"]))
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timeouts = ai.get("resolution_timeouts", {})
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if resolution not in timeouts:
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raise ConfigError(f"未配置分辨率 {resolution} 的返回超时")
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return int(timeouts[resolution])
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def _normalize_ai_model(model):
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if not isinstance(model, dict):
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raise ConfigError("AI 模型定义必须是对象")
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normalized = {
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"name": str(model.get("name", "")).strip(),
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"category": str(model.get("category", "")).strip(),
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"enabled": bool(model.get("enabled", True)),
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"url": str(model.get("url", "")).strip(),
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"model": str(model.get("model", "")).strip(),
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"api_key": str(model.get("api_key", "")),
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"api_type": str(model.get("api_type", "auto")).strip() or "auto",
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"connect_timeout_seconds": int(model.get("connect_timeout_seconds", 30) or 30),
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"timeout_seconds": int(model.get("timeout_seconds", 0) or 0),
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"extra_body": copy.deepcopy(model.get("extra_body", {})),
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}
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if not normalized["name"]:
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raise ConfigError("AI 模型 name 不能为空")
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if normalized["category"] not in CATEGORIES:
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raise ConfigError("AI 模型 category 必须是 text 或 image")
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if normalized["api_type"] not in API_TYPES:
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raise ConfigError("AI 模型 api_type 必须是 chat、images_edits 或 auto")
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if normalized["connect_timeout_seconds"] <= 0:
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raise ConfigError("AI 模型 connect_timeout_seconds 必须大于 0")
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if normalized["timeout_seconds"] < 0:
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raise ConfigError("AI 模型 timeout_seconds 不能小于 0")
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if not isinstance(normalized["extra_body"], dict):
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raise ConfigError("AI 模型 extra_body 必须是对象")
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return normalized
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def _normalize_ai_models_config(config):
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models = config.get("models") if isinstance(config, dict) else None
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if not isinstance(models, list):
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raise ConfigError("config/ai_models.json 必须包含 models 列表")
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normalized = {"models": [_normalize_ai_model(model) for model in models]}
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_assert_unique_model_names(normalized["models"])
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_assert_required_categories(normalized["models"])
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return normalized
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def _assert_unique_model_names(models):
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names = [model["name"] for model in models]
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duplicates = sorted({name for name in names if names.count(name) > 1})
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if duplicates:
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raise ConfigError(f"AI 模型 name 重复: {', '.join(duplicates)}")
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def _assert_required_categories(models):
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enabled_categories = {
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model["category"] for model in models if model.get("enabled", True)
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}
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missing = sorted(CATEGORIES - enabled_categories)
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if missing:
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raise ConfigError(
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"AI 模型清单至少需要启用一个 text 和一个 image 模型，缺少: "
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+ ", ".join(missing)
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)
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def _model_index(models, name):
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for index, model in enumerate(models):
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if model["name"] == name:
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return index
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raise ConfigError(f"AI 模型不存在: {name}")
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def _mask_api_key(api_key):
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if not api_key:
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return ""
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if len(api_key) <= 8:
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return "***"
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return f"{api_key[:4]}***{api_key[-4:]}"
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def _public_model(model):
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public = copy.deepcopy(model)
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public["api_key"] = _mask_api_key(public.get("api_key", ""))
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public["api_key_set"] = bool(model.get("api_key"))
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return public
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def save_ai_models_config(config, path=AI_MODELS_PATH) -> dict:
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"""Persist AI model definitions, including local plaintext API keys."""
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normalized = _normalize_ai_models_config(config)
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directory = os.path.dirname(os.path.abspath(path))
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if directory:
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os.makedirs(directory, exist_ok=True)
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with open(path, "w", encoding="utf-8") as fh:
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json.dump(normalized, fh, ensure_ascii=False, indent=2)
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fh.write("\n")
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return normalized
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def load_ai_models_config(path=AI_MODELS_PATH) -> dict:
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"""Load AI model definitions, writing defaults first if missing."""
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if not os.path.exists(path):
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return save_ai_models_config(default_ai_models_config(), path=path)
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with open(path, "r", encoding="utf-8") as fh:
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try:
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loaded = json.load(fh)
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except json.JSONDecodeError as exc:
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raise ConfigError(f"AI 模型清单不是有效 JSON: {path}") from exc
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return _normalize_ai_models_config(loaded)
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def list_ai_models(category=None, path=AI_MODELS_PATH, reveal_api_key=False):
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"""Return AI models, optionally filtered by category."""
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if category is not None and category not in CATEGORIES:
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raise ConfigError("category 必须是 text 或 image")
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models = load_ai_models_config(path)["models"]
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filtered = [
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copy.deepcopy(model)
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for model in models
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if category is None or model["category"] == category
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]
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if reveal_api_key:
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return filtered
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return [_public_model(model) for model in filtered]
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def add_ai_model(model, path=AI_MODELS_PATH) -> None:
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config = load_ai_models_config(path)
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normalized = _normalize_ai_model(model)
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if any(item["name"] == normalized["name"] for item in config["models"]):
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raise ConfigError(f"AI 模型 name 已存在: {normalized['name']}")
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config["models"].append(normalized)
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save_ai_models_config(config, path=path)
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def update_ai_model(model_name, path=AI_MODELS_PATH, **fields) -> None:
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if not fields:
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return
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config = load_ai_models_config(path)
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index = _model_index(config["models"], model_name)
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updated = copy.deepcopy(config["models"][index])
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updated.update(fields)
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normalized = _normalize_ai_model(updated)
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if normalized["name"] != model_name and any(
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model["name"] == normalized["name"] for model in config["models"]
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):
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raise ConfigError(f"AI 模型 name 已存在: {normalized['name']}")
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config["models"][index] = normalized
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save_ai_models_config(config, path=path)
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def delete_ai_model(name, path=AI_MODELS_PATH) -> None:
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config = load_ai_models_config(path)
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index = _model_index(config["models"], name)
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remaining = config["models"][:index] + config["models"][index + 1 :]
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_assert_required_categories(remaining)
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save_ai_models_config({"models": remaining}, path=path)
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def get_model(name, path=AI_MODELS_PATH) -> dict:
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"""Return the model definition including api_key. Callers must not log it."""
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models = load_ai_models_config(path)["models"]
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return copy.deepcopy(models[_model_index(models, name)])
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def _test_request_payload(model):
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if model["api_type"] == "images_edits":
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payload = {"model": model["model"], "prompt": "ping"}
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payload.update(model.get("extra_body", {}))
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return payload
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payload = {
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"model": model["model"],
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"messages": [{"role": "user", "content": "ping"}],
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}
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payload.update(model.get("extra_body", {}))
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return payload
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def test_ai_model(name, path=AI_MODELS_PATH) -> dict:
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"""Send a minimal request to the configured model endpoint."""
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try:
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model = get_model(name, path=path)
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missing = [
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field
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for field in ("url", "model", "api_key")
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if not str(model.get(field, "")).strip()
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]
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if missing:
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return {"ok": False, "error": "模型缺少字段: " + ", ".join(missing)}
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data = json.dumps(_test_request_payload(model), ensure_ascii=False).encode(
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"utf-8"
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)
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request = urllib.request.Request(
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model["url"],
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data=data,
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headers={
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"Authorization": "Bearer " + model["api_key"],
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"Content-Type": "application/json",
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},
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method="POST",
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)
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with urllib.request.urlopen(
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request, timeout=int(model["connect_timeout_seconds"])
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) as response:
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response.read(1024)
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return {"ok": 200 <= response.status < 300, "status": response.status}
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except urllib.error.HTTPError as exc:
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return {"ok": False, "status": exc.code, "error": f"HTTP {exc.code}"}
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except urllib.error.URLError as exc:
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return {"ok": False, "error": str(exc.reason)}
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except Exception as exc:
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return {"ok": False, "error": str(exc)}
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