Remove watermark

google/generativeai/types/image_types/_image_types.py

@@ -8,7 +8,6 @@
 from typing import Any, Dict, Optional, Union
 
 from google.generativeai import protos
-from google.generativeai import client
 
 # pylint: disable=g-import-not-at-top
 if typing.TYPE_CHECKING:
@@ -38,7 +37,7 @@
     ImageType = Union["Image", "PIL.Image.Image", "IPython.display.Image"]
 # pylint: enable=g-import-not-at-top
 
-__all__ = ["Image", "GeneratedImage", "check_watermark", "CheckWatermarkResult", "ImageType"]
+__all__ = ["Image", "GeneratedImage", "ImageType"]
 
 
 def _pil_to_blob(image: PIL.Image.Image) -> protos.Blob:
@@ -121,48 +120,6 @@ def __bool__(self):
             raise ValueError(f"Unrecognized result: {decision}")
 
 
-def check_watermark(
-    img: Union[pathlib.Path, ImageType], model_id: str = "models/image-verification-001"
-) -> "CheckWatermarkResult":
-    """Checks if an image has a Google-AI watermark.
-
-    Args:
-        img: can be a `pathlib.Path` or a `PIL.Image.Image`, `IPython.display.Image`, or `google.generativeai.Image`.
-        model_id: Which version of the image-verification model to send the image to.
-
-    Returns:
-
-    """
-    if isinstance(img, Image):
-        pass
-    elif isinstance(img, pathlib.Path):
-        img = Image.load_from_file(img)
-    elif IPython.display is not None and isinstance(img, IPython.display.Image):
-        img = Image(image_bytes=img.data)
-    elif PIL.Image is not None and isinstance(img, PIL.Image.Image):
-        blob = _pil_to_blob(img)
-        img = Image(image_bytes=blob.data)
-    elif isinstance(img, protos.Blob):
-        img = Image(image_bytes=img.data)
-    else:
-        raise TypeError(
-            f"Not implemented: Could not convert a {type(img)} into `Image`\n    {img=}"
-        )
-
-    prediction_client = client.get_default_prediction_client()
-    if not model_id.startswith("models/"):
-        model_id = f"models/{model_id}"
-
-    instance = {"image": {"bytesBase64Encoded": base64.b64encode(img._loaded_bytes).decode()}}
-    parameters = {"watermarkVerification": True}
-
-    response = prediction_client.predict(
-        model=model_id, instances=[instance], parameters=parameters
-    )
-
-    return CheckWatermarkResult(response.predictions)
-
-
 class Image:
     """Image."""
 
@@ -257,8 +214,6 @@ def _as_base64_string(self) -> str:
     def _repr_png_(self):
         return self._pil_image._repr_png_()  # type:ignore
 
-    check_watermark = check_watermark
-
 
 _EXIF_USER_COMMENT_TAG_IDX = 0x9286
 _IMAGE_GENERATION_PARAMETERS_EXIF_KEY = (

google/generativeai/vision_models/__init__.py

@@ -14,15 +14,14 @@
 #
 """Classes for working with vision models."""
 
-from google.generativeai.types.image_types import check_watermark, Image, GeneratedImage
+from google.generativeai.types.image_types import Image, GeneratedImage
 
 from google.generativeai.vision_models._vision_models import (
     ImageGenerationModel,
     ImageGenerationResponse,
 )
 
 __all__ = [
-    "check_watermark",
     "Image",
     "GeneratedImage",
     "ImageGenerationModel",