Class swarmauri_standard.logger_formatters.HTMLFormatter.HTMLFormatter

swarmauri_standard.logger_formatters.HTMLFormatter.HTMLFormatter

Bases: FormatterBase

Formatter that renders log messages as HTML for embedding in web pages.

This formatter outputs log records as HTML snippets with appropriate tags for timestamp, level, message, and exception information. It supports custom CSS classes and inline styling options.

ATTRIBUTE	DESCRIPTION
include_timestamp	Whether to include timestamp in the formatted output TYPE: bool
date_format	Format string for the timestamp TYPE: str
css_class	CSS class to apply to the main log container TYPE: Optional[str]
level_css_classes	Dictionary mapping log levels to CSS classes TYPE: Dict[str, str]
use_colors	Whether to use colors for different log levels TYPE: bool
level_colors	Dictionary mapping log levels to HTML color codes TYPE: Dict[str, str]
include_line_breaks	Whether to add line breaks between log elements TYPE: bool

include_timestamp class-attribute instance-attribute

include_timestamp = True

date_format class-attribute instance-attribute

date_format = '%Y-%m-%d %H:%M:%S'

css_class class-attribute instance-attribute

css_class = 'log-entry'

level_css_classes class-attribute instance-attribute

level_css_classes = {
    "DEBUG": "log-debug",
    "INFO": "log-info",
    "WARNING": "log-warning",
    "ERROR": "log-error",
    "CRITICAL": "log-critical",
}

use_colors class-attribute instance-attribute

use_colors = True

level_colors class-attribute instance-attribute

level_colors = {
    "DEBUG": "#888888",
    "INFO": "#0000FF",
    "WARNING": "#FF8C00",
    "ERROR": "#FF0000",
    "CRITICAL": "#8B0000",
}

include_line_breaks class-attribute instance-attribute

include_line_breaks = True

type class-attribute instance-attribute

type = 'ObserveBase'

model_config class-attribute instance-attribute

model_config = ConfigDict(arbitrary_types_allowed=True)

id class-attribute instance-attribute

id = Field(default_factory=generate_id)

members class-attribute instance-attribute

members = None

owners class-attribute instance-attribute

owners = None

host class-attribute instance-attribute

host = None

name class-attribute instance-attribute

name = None

version class-attribute instance-attribute

version = '0.1.0'

format class-attribute instance-attribute

format = '[%(name)s][%(levelname)s] %(message)s'

model_post_init

model_post_init(*args, **kwargs)

Initialize the formatter after the model is created.

This method sets up the format string that will be used by the formatter.

Source code in swarmauri_standard/logger_formatters/HTMLFormatter.py

def model_post_init(self, *args, **kwargs):
    """
    Initialize the formatter after the model is created.

    This method sets up the format string that will be used by the formatter.
    """
    # We'll override the compile_formatter method instead of setting format
    # since HTML formatting requires more complex logic than a simple format string
    pass

escape_html

escape_html(text)

Escape HTML special characters in the given text.

PARAMETER	DESCRIPTION
text	The text to escape TYPE: str

RETURNS	DESCRIPTION
str	HTML-escaped text

Source code in swarmauri_standard/logger_formatters/HTMLFormatter.py

def escape_html(self, text: str) -> str:
    """
    Escape HTML special characters in the given text.

    Args:
        text: The text to escape

    Returns:
        HTML-escaped text
    """
    return html.escape(str(text))

compile_formatter

compile_formatter()

Create and return a custom HTML logging.Formatter.

RETURNS	DESCRIPTION
Formatter	A logging.Formatter that formats log records as HTML

Source code in swarmauri_standard/logger_formatters/HTMLFormatter.py

def compile_formatter(self) -> logging.Formatter:
    """
    Create and return a custom HTML logging.Formatter.

    Returns:
        A logging.Formatter that formats log records as HTML
    """
    return HTMLLoggingFormatter(self)

register_model classmethod

register_model()

Decorator to register a base model in the unified registry.

RETURNS	DESCRIPTION
Callable	A decorator function that registers the model class. TYPE: Callable[[Type[BaseModel]], Type[BaseModel]]

Source code in swarmauri_base/DynamicBase.py

@classmethod
def register_model(cls) -> Callable[[Type[BaseModel]], Type[BaseModel]]:
    """
    Decorator to register a base model in the unified registry.

    Returns:
        Callable: A decorator function that registers the model class.
    """

    def decorator(model_cls: Type[BaseModel]):
        """Register ``model_cls`` as a base model."""
        model_name = model_cls.__name__
        if model_name in cls._registry:
            glogger.warning(
                "Model '%s' is already registered; skipping duplicate.", model_name
            )
            return model_cls

        cls._registry[model_name] = {"model_cls": model_cls, "subtypes": {}}
        glogger.debug("Registered base model '%s'.", model_name)
        DynamicBase._recreate_models()
        return model_cls

    return decorator

register_type classmethod

register_type(resource_type=None, type_name=None)

Decorator to register a subtype under one or more base models in the unified registry.

PARAMETER	DESCRIPTION
resource_type	The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase) are used. TYPE: Optional[Union[Type[T], List[Type[T]]]] DEFAULT: None
type_name	An optional custom type name for the subtype. TYPE: Optional[str] DEFAULT: None

RETURNS	DESCRIPTION
Callable	A decorator function that registers the subtype. TYPE: Callable[[Type[DynamicBase]], Type[DynamicBase]]

Source code in swarmauri_base/DynamicBase.py

@classmethod
def register_type(
    cls,
    resource_type: Optional[Union[Type[T], List[Type[T]]]] = None,
    type_name: Optional[str] = None,
) -> Callable[[Type["DynamicBase"]], Type["DynamicBase"]]:
    """
    Decorator to register a subtype under one or more base models in the unified registry.

    Parameters:
        resource_type (Optional[Union[Type[T], List[Type[T]]]]):
            The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase)
            are used.
        type_name (Optional[str]): An optional custom type name for the subtype.

    Returns:
        Callable: A decorator function that registers the subtype.
    """

    def decorator(subclass: Type["DynamicBase"]):
        """Register ``subclass`` as a subtype."""
        if resource_type is None:
            resource_types = [
                base for base in subclass.__bases__ if base is not cls
            ]
        elif not isinstance(resource_type, list):
            resource_types = [resource_type]
        else:
            resource_types = resource_type

        for rt in resource_types:
            if not issubclass(subclass, rt):
                raise TypeError(
                    f"'{subclass.__name__}' must be a subclass of '{rt.__name__}'."
                )
            final_type_name = type_name or getattr(
                subclass, "_type", subclass.__name__
            )
            base_model_name = rt.__name__

            if base_model_name not in cls._registry:
                cls._registry[base_model_name] = {"model_cls": rt, "subtypes": {}}
                glogger.debug(
                    "Created new registry entry for base model '%s'.",
                    base_model_name,
                )

            subtypes_dict = cls._registry[base_model_name]["subtypes"]
            if final_type_name in subtypes_dict:
                glogger.warning(
                    "Type '%s' already exists under '%s'; skipping duplicate.",
                    final_type_name,
                    base_model_name,
                )
                continue

            subtypes_dict[final_type_name] = subclass
            glogger.debug(
                "Registered '%s' as '%s' under '%s'.",
                subclass.__name__,
                final_type_name,
                base_model_name,
            )

        DynamicBase._recreate_models()
        return subclass

    return decorator

model_validate_yaml classmethod

model_validate_yaml(yaml_data)

Validate a model from a YAML string.

Source code in swarmauri_base/YamlMixin.py

@classmethod
def model_validate_yaml(cls, yaml_data: str):
    """Validate a model from a YAML string."""
    try:
        # Parse YAML into a Python dictionary
        yaml_content = yaml.safe_load(yaml_data)

        # Convert the dictionary to JSON and validate using Pydantic
        return cls.model_validate_json(json.dumps(yaml_content))
    except yaml.YAMLError as e:
        raise ValueError(f"Invalid YAML data: {e}")
    except ValidationError as e:
        raise ValueError(f"Validation failed: {e}")

model_dump_yaml

model_dump_yaml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a YAML representation of the model.

Source code in swarmauri_base/YamlMixin.py

def model_dump_yaml(self, fields_to_exclude=None, api_key_placeholder=None):
    """Return a YAML representation of the model."""
    if fields_to_exclude is None:
        fields_to_exclude = []

    # Load the JSON string into a Python dictionary
    json_data = json.loads(self.model_dump_json())

    # Function to recursively remove specific keys and handle api_key placeholders
    def process_fields(data, fields_to_exclude):
        """Recursively filter fields and apply placeholders."""
        if isinstance(data, dict):
            return {
                key: (
                    api_key_placeholder
                    if key == "api_key" and api_key_placeholder is not None
                    else process_fields(value, fields_to_exclude)
                )
                for key, value in data.items()
                if key not in fields_to_exclude
            }
        elif isinstance(data, list):
            return [process_fields(item, fields_to_exclude) for item in data]
        else:
            return data

    # Filter the JSON data
    filtered_data = process_fields(json_data, fields_to_exclude)

    # Convert the filtered data into YAML using safe mode
    return yaml.safe_dump(filtered_data, default_flow_style=False)