Class swarmauri_standard.image_gens.OpenAIImgGenModel.OpenAIImgGenModel

swarmauri_standard.image_gens.OpenAIImgGenModel.OpenAIImgGenModel

OpenAIImgGenModel(**kwargs)

Bases: ImageGenBase

OpenAIImgGenModel is a class for generating images using OpenAI's DALL-E models.

ATTRIBUTE	DESCRIPTION
api_key	The API key for authenticating with the OpenAI API. TYPE: str
allowed_models	List of allowed model names. TYPE: List[str]
name	The name of the model to use. TYPE: str
type	The type of the model. TYPE: Literal['OpenAIImgGenModel']

Provider Resources: https://platform.openai.com/docs/api-reference/images/generate

Initialize the GroqAIAudio class with the provided data.

PARAMETER	DESCRIPTION
**kwargs	Additional keyword arguments, which may include api_key and allowed_models. TYPE: Dict[str, Any] DEFAULT: {}

Source code in swarmauri_standard/image_gens/OpenAIImgGenModel.py

def __init__(self, **kwargs: Dict[str, Any]) -> None:
    """
    Initialize the GroqAIAudio class with the provided data.

    Args:
        **kwargs (Dict[str, Any]): Additional keyword arguments, which may include api_key and allowed_models.
    """
    super().__init__(**kwargs)
    self._headers = {
        "Authorization": f"Bearer {self.api_key.get_secret_value()}",
        "Content-Type": "application/json",
    }

api_key instance-attribute

api_key

allowed_models class-attribute instance-attribute

allowed_models = ['dall-e-2', 'dall-e-3']

name class-attribute instance-attribute

name = 'dall-e-2'

type class-attribute instance-attribute

type = 'OpenAIImgGenModel'

timeout class-attribute instance-attribute

timeout = 600.0

model_config class-attribute instance-attribute

model_config = ConfigDict(
    extra="forbid", 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

default_logger class-attribute

default_logger = None

logger class-attribute instance-attribute

logger = None

resource class-attribute instance-attribute

resource = Field(default=IMAGE_GEN.value, frozen=True)

version class-attribute instance-attribute

version = '0.1.0'

generate_image

generate_image(
    prompt,
    size="1024x1024",
    quality="standard",
    n=1,
    style=None,
)

Generate images using the OpenAI DALL-E model synchronously.

Parameters: - prompt (str): The prompt to generate images from. - size (str): Size of the generated images. - quality (str): Quality of the generated images. - n (int): Number of images to generate. - style (str): Optional style of the generated images.

Returns: - List of URLs of the generated images.

Source code in swarmauri_standard/image_gens/OpenAIImgGenModel.py

@retry_on_status_codes((429, 529), max_retries=1)
def generate_image(
    self,
    prompt: str,
    size: str = "1024x1024",
    quality: str = "standard",
    n: int = 1,
    style: Optional[str] = None,
) -> List[str]:
    """
    Generate images using the OpenAI DALL-E model synchronously.

    Parameters:
    - prompt (str): The prompt to generate images from.
    - size (str): Size of the generated images.
    - quality (str): Quality of the generated images.
    - n (int): Number of images to generate.
    - style (str): Optional style of the generated images.

    Returns:
    - List of URLs of the generated images.
    """
    if self.name == "dall-e-3" and n > 1:
        raise ValueError("DALL-E 3 only supports generating 1 image at a time.")

    payload = {
        "model": self.name,
        "prompt": prompt,
        "size": size,
        "quality": quality,
        "n": n,
    }

    if style and self.name == "dall-e-3":
        payload["style"] = style

    try:
        with httpx.Client(timeout=self.timeout) as client:
            response = client.post(
                self._BASE_URL, headers=self._headers, json=payload
            )
            response.raise_for_status()
            return [image["url"] for image in response.json().get("data", [])]
    except httpx.HTTPStatusError as e:
        raise RuntimeError(f"Image generation failed: {e}")

agenerate_image async

agenerate_image(
    prompt,
    size="1024x1024",
    quality="standard",
    n=1,
    style=None,
)

Generate images using the OpenAI DALL-E model asynchronously.

Parameters: - prompt (str): The prompt to generate images from. - size (str): Size of the generated images. - quality (str): Quality of the generated images. - n (int): Number of images to generate. - style (str): Optional style of the generated images.

Returns: - List of URLs of the generated images.

Source code in swarmauri_standard/image_gens/OpenAIImgGenModel.py

@retry_on_status_codes((429, 529), max_retries=1)
async def agenerate_image(
    self,
    prompt: str,
    size: str = "1024x1024",
    quality: str = "standard",
    n: int = 1,
    style: Optional[str] = None,
) -> List[str]:
    """
    Generate images using the OpenAI DALL-E model asynchronously.

    Parameters:
    - prompt (str): The prompt to generate images from.
    - size (str): Size of the generated images.
    - quality (str): Quality of the generated images.
    - n (int): Number of images to generate.
    - style (str): Optional style of the generated images.

    Returns:
    - List of URLs of the generated images.
    """
    if self.name == "dall-e-3" and n > 1:
        raise ValueError("DALL-E 3 only supports generating 1 image at a time.")

    payload = {
        "model": self.name,
        "prompt": prompt,
        "size": size,
        "quality": quality,
        "n": n,
    }

    if style and self.name == "dall-e-3":
        payload["style"] = style

    try:
        async with httpx.AsyncClient(timeout=self.timeout) as client:
            response = await client.post(
                self._BASE_URL, headers=self._headers, json=payload
            )
            response.raise_for_status()
            return [image["url"] for image in response.json().get("data", [])]
    except httpx.HTTPStatusError as e:
        raise RuntimeError(f"Image generation failed: {e}")

batch

batch(
    prompts,
    size="1024x1024",
    quality="standard",
    n=1,
    style=None,
)

Synchronously process multiple prompts for image generation.

Parameters: - prompts (List[str]): List of prompts. - size (str): Size of the generated images. - quality (str): Quality of the generated images. - n (int): Number of images to generate. - style (str): Optional style of the generated images.

Returns: - List of lists of URLs of the generated images.

Source code in swarmauri_standard/image_gens/OpenAIImgGenModel.py

def batch(
    self,
    prompts: List[str],
    size: str = "1024x1024",
    quality: str = "standard",
    n: int = 1,
    style: Optional[str] = None,
) -> List[List[str]]:
    """
    Synchronously process multiple prompts for image generation.

    Parameters:
    - prompts (List[str]): List of prompts.
    - size (str): Size of the generated images.
    - quality (str): Quality of the generated images.
    - n (int): Number of images to generate.
    - style (str): Optional style of the generated images.

    Returns:
    - List of lists of URLs of the generated images.
    """
    return [
        self.generate_image(prompt, size=size, quality=quality, n=n, style=style)
        for prompt in prompts
    ]

abatch async

abatch(
    prompts,
    size="1024x1024",
    quality="standard",
    n=1,
    style=None,
    max_concurrent=5,
)

Asynchronously process multiple prompts for image generation with controlled concurrency.

Parameters: - prompts (List[str]): List of prompts. - size (str): Size of the generated images. - quality (str): Quality of the generated images. - n (int): Number of images to generate. - style (str): Optional style of the generated images. - max_concurrent (int): Maximum number of concurrent requests.

Returns: - List of lists of URLs of the generated images.

Source code in swarmauri_standard/image_gens/OpenAIImgGenModel.py

async def abatch(
    self,
    prompts: List[str],
    size: str = "1024x1024",
    quality: str = "standard",
    n: int = 1,
    style: Optional[str] = None,
    max_concurrent: int = 5,
) -> List[List[str]]:
    """
    Asynchronously process multiple prompts for image generation with controlled concurrency.

    Parameters:
    - prompts (List[str]): List of prompts.
    - size (str): Size of the generated images.
    - quality (str): Quality of the generated images.
    - n (int): Number of images to generate.
    - style (str): Optional style of the generated images.
    - max_concurrent (int): Maximum number of concurrent requests.

    Returns:
    - List of lists of URLs of the generated images.
    """
    semaphore = asyncio.Semaphore(max_concurrent)

    async def process_prompt(prompt) -> List[str]:
        async with semaphore:
            return await self.agenerate_image(
                prompt, size=size, quality=quality, n=n, style=style
            )

    tasks = [process_prompt(prompt) for prompt in prompts]
    return await asyncio.gather(*tasks)

get_allowed_models

get_allowed_models()

Queries the LLMProvider API endpoint to get the list of allowed models.

RETURNS	DESCRIPTION
List[str]	List[str]: List of allowed model names.

Source code in swarmauri_standard/image_gens/OpenAIImgGenModel.py

def get_allowed_models(self) -> List[str]:
    """
    Queries the LLMProvider API endpoint to get the list of allowed models.

    Returns:
        List[str]: List of allowed model names.
    """
    return ["dall-e-2", "dall-e-3"]

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_toml classmethod

model_validate_toml(toml_data)

Validate a model from a TOML string.

Source code in swarmauri_base/TomlMixin.py

@classmethod
def model_validate_toml(cls, toml_data: str):
    """Validate a model from a TOML string."""
    try:
        # Parse TOML into a Python dictionary
        toml_content = tomllib.loads(toml_data)

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

model_dump_toml

model_dump_toml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a TOML representation of the model.

Source code in swarmauri_base/TomlMixin.py

def model_dump_toml(self, fields_to_exclude=None, api_key_placeholder=None):
    """Return a TOML 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 TOML
    return toml.dumps(filtered_data)

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)

model_post_init

model_post_init(logger=None)

Assign a logger instance after model initialization.

Source code in swarmauri_base/LoggerMixin.py

def model_post_init(self, logger: Optional[FullUnion[LoggerBase]] = None) -> None:
    """Assign a logger instance after model initialization."""

    # Directly assign the provided FullUnion[LoggerBase] or fallback to the
    # class-level default.
    self.logger = self.logger or logger or self.default_logger

batch_generate abstractmethod

batch_generate(*args, **kwargs)

Generate images based on the input data provided to the model.

Source code in swarmauri_base/image_gens/ImageGenBase.py

@abstractmethod
def batch_generate(self, *args, **kwargs) -> any:
    """
    Generate images based on the input data provided to the model.
    """
    raise NotImplementedError("batch_generate() not implemented in subclass yet.")

abatch_generate abstractmethod async

abatch_generate(*args, **kwargs)

Generate images based on the input data provided to the model.

Source code in swarmauri_base/image_gens/ImageGenBase.py

@abstractmethod
async def abatch_generate(self, *args, **kwargs) -> any:
    """
    Generate images based on the input data provided to the model.
    """
    raise NotImplementedError("abatch_generate() not implemented in subclass yet.")