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https://github.com/blakeblackshear/frigate.git
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765a28d812
* Implement model training via ZMQ and add model states to represent training * Get model updates working * Improve toasts and model state * Clean up logging * Add back in
44 lines
1.2 KiB
Python
44 lines
1.2 KiB
Python
import logging
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from enum import Enum
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from pydantic import Field, ValidationInfo, model_validator
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from typing_extensions import Self
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from .base import FrigateBaseModel
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__all__ = ["LoggerConfig", "LogLevel"]
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class LogLevel(str, Enum):
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debug = "debug"
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info = "info"
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warning = "warning"
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error = "error"
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critical = "critical"
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class LoggerConfig(FrigateBaseModel):
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default: LogLevel = Field(default=LogLevel.info, title="Default logging level.")
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logs: dict[str, LogLevel] = Field(
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default_factory=dict, title="Log level for specified processes."
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)
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@model_validator(mode="after")
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def post_validation(self, info: ValidationInfo) -> Self:
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if isinstance(info.context, dict) and info.context.get("install", False):
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logging.getLogger().setLevel(self.default.value.upper())
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log_levels = {
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"absl": LogLevel.error,
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"httpx": LogLevel.error,
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"tensorflow": LogLevel.error,
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"werkzeug": LogLevel.error,
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"ws4py": LogLevel.error,
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**self.logs,
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}
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for log, level in log_levels.items():
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logging.getLogger(log).setLevel(level.value.upper())
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return self
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