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microsoft--semantic-kernel/python/semantic_kernel/contents/image_content.py
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Python

# Copyright (c) Microsoft. All rights reserved.
import logging
import mimetypes
from typing import Any, ClassVar, Literal, TypeVar
from numpy import ndarray
from pydantic import Field
from typing_extensions import deprecated
from semantic_kernel.contents.binary_content import BinaryContent
from semantic_kernel.contents.const import IMAGE_CONTENT_TAG, ContentTypes
from semantic_kernel.utils.feature_stage_decorator import experimental
logger = logging.getLogger(__name__)
_T = TypeVar("_T", bound="ImageContent")
@experimental
class ImageContent(BinaryContent):
"""Image Content class.
This can be created either the bytes data or a data uri, additionally it can have a uri.
The uri is a reference to the source, and might or might not point to the same thing as the data.
Use the .from_image_file method to create an instance from a image file.
This reads the file and guesses the mime_type.
If both data_uri and data is provided, data will be used and a warning is logged.
Args:
uri (Url | None): The reference uri of the content.
data_uri (DataUrl | None): The data uri of the content.
data (str | bytes | None): The data of the content.
data_format (str | None): The format of the data (e.g. base64).
mime_type (str | None): The mime type of the image, only used with data.
kwargs (Any): Any additional arguments:
inner_content (Any): The inner content of the response,
this should hold all the information from the response so even
when not creating a subclass a developer can leverage the full thing.
ai_model_id (str | None): The id of the AI model that generated this response.
metadata (dict[str, Any]): Any metadata that should be attached to the response.
Methods:
from_image_path: Create an instance from an image file.
__str__: Returns the string representation of the image.
Raises:
ValidationError: If neither uri or data is provided.
"""
content_type: Literal[ContentTypes.IMAGE_CONTENT] = Field(IMAGE_CONTENT_TAG, init=False) # type: ignore
tag: ClassVar[str] = IMAGE_CONTENT_TAG
def __init__(
self,
uri: str | None = None,
data_uri: str | None = None,
data: str | bytes | ndarray | None = None,
data_format: str | None = None,
mime_type: str | None = None,
**kwargs: Any,
):
"""Create an Image Content object, either from a data_uri or data.
Args:
uri: The reference uri of the content.
data_uri: The data uri of the content.
data: The data of the content.
data_format: The format of the data (e.g. base64).
mime_type: The mime type of the image, only used with data.
kwargs: Any additional arguments:
inner_content: The inner content of the response,
this should hold all the information from the response so even
when not creating a subclass a developer
can leverage the full thing.
ai_model_id: The id of the AI model that generated this response.
metadata: Any metadata that should be attached to the response.
"""
super().__init__(
uri=uri,
data_uri=data_uri,
data=data,
data_format=data_format,
mime_type=mime_type,
**kwargs,
)
@classmethod
@deprecated("The `from_image_path` method is deprecated; use `from_image_file` instead.", category=None)
def from_image_path(cls: type[_T], image_path: str) -> _T:
"""Create an instance from an image file."""
return cls.from_image_file(image_path)
@classmethod
def from_image_file(cls: type[_T], path: str) -> _T:
"""Create an instance from an image file."""
mime_type = mimetypes.guess_type(path)[0]
with open(path, "rb") as image_file:
return cls(data=image_file.read(), data_format="base64", mime_type=mime_type, uri=path)
def to_dict(self) -> dict[str, Any]:
"""Convert the instance to a dictionary."""
return {"type": "image_url", "image_url": {"url": str(self)}}