- 新增图像生成接口,支持试用、积分和自定义API Key模式 - 实现生成图片结果异步上传至MinIO存储,带重试机制 - 优化积分预扣除和异常退还逻辑,保障用户积分准确 - 添加获取生成历史记录接口,支持时间范围和分页 - 提供本地字典配置接口,支持模型、比例、提示模板和尺寸 - 实现图片批量上传接口,支持S3兼容对象存储 feat(admin): 增加管理员角色管理与权限分配接口 - 实现角色列表查询、角色创建、更新及删除功能 - 增加权限列表查询接口 - 实现用户角色分配接口,便于统一管理用户权限 - 增加系统字典增删查改接口,支持分类过滤和排序 - 权限控制全面覆盖管理接口,保证安全访问 feat(auth): 完善用户登录注册及权限相关接口与页面 - 实现手机号验证码发送及校验功能,保障注册安全 - 支持手机号注册、登录及退出接口,集成日志记录 - 增加修改密码功能,验证原密码后更新 - 提供动态导航菜单接口,基于权限展示不同菜单 - 实现管理界面路由及日志、角色、字典管理页面访问权限控制 - 添加系统日志查询接口,支持关键词和等级筛选 feat(app): 初始化Flask应用并配置蓝图与数据库 - 创建应用程序工厂,加载配置,初始化数据库和Redis客户端 - 注册认证、API及管理员蓝图,整合路由 - 根路由渲染主页模板 - 应用上下文中自动创建数据库表,保证运行环境准备完毕 feat(database): 提供数据库创建与迁移支持脚本 - 新增数据库创建脚本,支持自动检测是否已存在 - 添加数据库表初始化脚本,支持创建和删除所有表 - 实现RBAC权限初始化,包含基础权限和角色创建 - 新增字段手动修复脚本,添加用户API Key和积分字段 - 强制迁移脚本支持清理连接和修复表结构,初始化默认数据及角色分配 feat(config): 新增系统配置参数 - 配置数据库、Redis、Session和MinIO相关参数 - 添加AI接口地址及试用Key配置 - 集成阿里云短信服务配置及开发模式相关参数 feat(extensions): 初始化数据库、Redis和MinIO客户端 - 创建全局SQLAlchemy数据库实例和Redis客户端 - 配置基于boto3的MinIO兼容S3客户端 chore(logs): 添加示例系统日志文件 - 记录用户请求、验证码发送成功与失败的日志信息
159 lines
5.2 KiB
Python
159 lines
5.2 KiB
Python
import pickle
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import warnings
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from apscheduler.job import Job
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from apscheduler.jobstores.base import BaseJobStore, ConflictingIdError, JobLookupError
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from apscheduler.util import (
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datetime_to_utc_timestamp,
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maybe_ref,
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utc_timestamp_to_datetime,
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)
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try:
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from bson.binary import Binary
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from pymongo import ASCENDING, MongoClient
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from pymongo.errors import DuplicateKeyError
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except ImportError as exc: # pragma: nocover
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raise ImportError("MongoDBJobStore requires PyMongo installed") from exc
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class MongoDBJobStore(BaseJobStore):
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"""
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Stores jobs in a MongoDB database. Any leftover keyword arguments are directly passed to
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pymongo's `MongoClient
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<http://api.mongodb.org/python/current/api/pymongo/mongo_client.html#pymongo.mongo_client.MongoClient>`_.
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Plugin alias: ``mongodb``
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:param str database: database to store jobs in
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:param str collection: collection to store jobs in
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:param client: a :class:`~pymongo.mongo_client.MongoClient` instance to use instead of
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providing connection arguments
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:param int pickle_protocol: pickle protocol level to use (for serialization), defaults to the
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highest available
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"""
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def __init__(
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self,
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database="apscheduler",
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collection="jobs",
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client=None,
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pickle_protocol=pickle.HIGHEST_PROTOCOL,
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**connect_args,
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):
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super().__init__()
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self.pickle_protocol = pickle_protocol
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if not database:
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raise ValueError('The "database" parameter must not be empty')
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if not collection:
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raise ValueError('The "collection" parameter must not be empty')
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if client:
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self.client = maybe_ref(client)
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else:
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connect_args.setdefault("w", 1)
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self.client = MongoClient(**connect_args)
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self.collection = self.client[database][collection]
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def start(self, scheduler, alias):
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super().start(scheduler, alias)
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self.collection.create_index("next_run_time", sparse=True)
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@property
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def connection(self):
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warnings.warn(
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'The "connection" member is deprecated -- use "client" instead',
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DeprecationWarning,
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)
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return self.client
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def lookup_job(self, job_id):
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document = self.collection.find_one(job_id, ["job_state"])
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return self._reconstitute_job(document["job_state"]) if document else None
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def get_due_jobs(self, now):
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timestamp = datetime_to_utc_timestamp(now)
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return self._get_jobs({"next_run_time": {"$lte": timestamp}})
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def get_next_run_time(self):
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document = self.collection.find_one(
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{"next_run_time": {"$ne": None}},
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projection=["next_run_time"],
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sort=[("next_run_time", ASCENDING)],
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)
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return (
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utc_timestamp_to_datetime(document["next_run_time"]) if document else None
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)
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def get_all_jobs(self):
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jobs = self._get_jobs({})
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self._fix_paused_jobs_sorting(jobs)
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return jobs
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def add_job(self, job):
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try:
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self.collection.insert_one(
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{
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"_id": job.id,
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"next_run_time": datetime_to_utc_timestamp(job.next_run_time),
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"job_state": Binary(
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pickle.dumps(job.__getstate__(), self.pickle_protocol)
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),
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}
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)
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except DuplicateKeyError:
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raise ConflictingIdError(job.id)
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def update_job(self, job):
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changes = {
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"next_run_time": datetime_to_utc_timestamp(job.next_run_time),
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"job_state": Binary(pickle.dumps(job.__getstate__(), self.pickle_protocol)),
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}
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result = self.collection.update_one({"_id": job.id}, {"$set": changes})
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if result and result.matched_count == 0:
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raise JobLookupError(job.id)
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def remove_job(self, job_id):
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result = self.collection.delete_one({"_id": job_id})
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if result and result.deleted_count == 0:
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raise JobLookupError(job_id)
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def remove_all_jobs(self):
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self.collection.delete_many({})
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def shutdown(self):
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self.client.close()
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def _reconstitute_job(self, job_state):
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job_state = pickle.loads(job_state)
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job = Job.__new__(Job)
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job.__setstate__(job_state)
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job._scheduler = self._scheduler
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job._jobstore_alias = self._alias
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return job
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def _get_jobs(self, conditions):
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jobs = []
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failed_job_ids = []
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for document in self.collection.find(
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conditions, ["_id", "job_state"], sort=[("next_run_time", ASCENDING)]
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):
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try:
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jobs.append(self._reconstitute_job(document["job_state"]))
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except BaseException:
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self._logger.exception(
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'Unable to restore job "%s" -- removing it', document["_id"]
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)
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failed_job_ids.append(document["_id"])
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# Remove all the jobs we failed to restore
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if failed_job_ids:
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self.collection.delete_many({"_id": {"$in": failed_job_ids}})
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return jobs
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def __repr__(self):
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return f"<{self.__class__.__name__} (client={self.client})>"
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