Merge branch 'main' into imagen

google/generativeai/__init__.py

@@ -59,6 +59,8 @@
 from google.generativeai.generative_models import GenerativeModel
 from google.generativeai.generative_models import ChatSession
 
+from google.generativeai.vision_models import *
+
 from google.generativeai.models import list_models
 from google.generativeai.models import list_tuned_models
 
@@ -77,7 +79,6 @@
 
 __version__ = version.__version__
 
-del embedding
 del files
 del generative_models
 del models

google/generativeai/client.py

@@ -3,11 +3,12 @@
 import os
 import contextlib
 import inspect
+import collections
 import dataclasses
 import pathlib
 import threading
 from typing import Any, cast
-from collections.abc import Sequence
+from collections.abc import Sequence, Mapping
 import httplib2
 from io import IOBase
 
@@ -24,6 +25,11 @@
 import googleapiclient.http
 import googleapiclient.discovery
 
+from google.protobuf import struct_pb2
+
+from proto.marshal.collections import maps
+from proto.marshal.collections import repeated
+
 try:
     from google.generativeai import version
 
@@ -132,6 +138,70 @@ async def create_file(self, *args, **kwargs):
         )
 
 
+# This is to get around https://github.com/googleapis/proto-plus-python/issues/488
+def to_value(value) -> struct_pb2.Value:
+    """Return a protobuf Value object representing this value."""
+    if isinstance(value, struct_pb2.Value):
+        return value
+    if value is None:
+        return struct_pb2.Value(null_value=0)
+    if isinstance(value, bool):
+        return struct_pb2.Value(bool_value=value)
+    if isinstance(value, (int, float)):
+        return struct_pb2.Value(number_value=float(value))
+    if isinstance(value, str):
+        return struct_pb2.Value(string_value=value)
+    if isinstance(value, collections.abc.Sequence):
+        return struct_pb2.Value(list_value=to_list_value(value))
+    if isinstance(value, collections.abc.Mapping):
+        return struct_pb2.Value(struct_value=to_mapping_value(value))
+    raise ValueError("Unable to coerce value: %r" % value)
+
+
+def to_list_value(value) -> struct_pb2.ListValue:
+    # We got a proto, or else something we sent originally.
+    # Preserve the instance we have.
+    if isinstance(value, struct_pb2.ListValue):
+        return value
+    if isinstance(value, repeated.RepeatedComposite):
+        return struct_pb2.ListValue(values=[v for v in value.pb])
+
+    # We got a list (or something list-like); convert it.
+    return struct_pb2.ListValue(values=[to_value(v) for v in value])
+
+
+def to_mapping_value(value) -> struct_pb2.Struct:
+    # We got a proto, or else something we sent originally.
+    # Preserve the instance we have.
+    if isinstance(value, struct_pb2.Struct):
+        return value
+    if isinstance(value, maps.MapComposite):
+        return struct_pb2.Struct(
+            fields={k: v for k, v in value.pb.items()},
+        )
+
+    # We got a dict (or something dict-like); convert it.
+    return struct_pb2.Struct(fields={k: to_value(v) for k, v in value.items()})
+
+
+class PredictionServiceClient(glm.PredictionServiceClient):
+    def predict(self, model=None, instances=None, parameters=None):
+        pr = protos.PredictRequest.pb()
+        request = pr(
+            model=model, instances=[to_value(i) for i in instances], parameters=to_value(parameters)
+        )
+        return super().predict(request)
+
+
+class PredictionServiceAsyncClient(glm.PredictionServiceAsyncClient):
+    async def predict(self, model=None, instances=None, parameters=None):
+        pr = protos.PredictRequest.pb()
+        request = pr(
+            model=model, instances=[to_value(i) for i in instances], parameters=to_value(parameters)
+        )
+        return await super().predict(request)
+
+
 @dataclasses.dataclass
 class _ClientManager:
     client_config: dict[str, Any] = dataclasses.field(default_factory=dict)
@@ -222,15 +292,20 @@ def configure(
         self.clients = {}
 
     def make_client(self, name):
-        if name == "file":
-            cls = FileServiceClient
-        elif name == "file_async":
-            cls = FileServiceAsyncClient
-        elif name.endswith("_async"):
-            name = name.split("_")[0]
-            cls = getattr(glm, name.title() + "ServiceAsyncClient")
-        else:
-            cls = getattr(glm, name.title() + "ServiceClient")
+        local_clients = {
+            "file": FileServiceClient,
+            "file_async": FileServiceAsyncClient,
+            "prediction": PredictionServiceClient,
+            "prediction_async": PredictionServiceAsyncClient,
+        }
+        cls = local_clients.get(name, None)
+
+        if cls is None:
+            if name.endswith("_async"):
+                name = name.split("_")[0]
+                cls = getattr(glm, name.title() + "ServiceAsyncClient")
+            else:
+                cls = getattr(glm, name.title() + "ServiceClient")
 
         # Attempt to configure using defaults.
         if not self.client_config:
@@ -386,3 +461,11 @@ def get_default_permission_client() -> glm.PermissionServiceClient:
 
 def get_default_permission_async_client() -> glm.PermissionServiceAsyncClient:
     return _client_manager.get_default_client("permission_async")
+
+
+def get_default_prediction_client() -> glm.PermissionServiceClient:
+    return _client_manager.get_default_client("prediction")
+
+
+def get_default_prediction_async_client() -> glm.PermissionServiceAsyncClient:
+    return _client_manager.get_default_client("prediction_async")

google/generativeai/generative_models.py

@@ -4,7 +4,7 @@
 
 from collections.abc import Iterable
 import textwrap
-from typing import Any, Union, overload
+from typing import Any, Literal, Union, overload
 import reprlib
 
 # pylint: disable=bad-continuation, line-too-long

google/generativeai/types/content_types.py

@@ -16,42 +16,16 @@
 from __future__ import annotations
 
 from collections.abc import Iterable, Mapping, Sequence
-import io
 import inspect
-import mimetypes
-import pathlib
-import typing
 from typing import Any, Callable, Union
 from typing_extensions import TypedDict
 
 import pydantic
 
 from google.generativeai.types import file_types
+from google.generativeai.types.image_types import _image_types
 from google.generativeai import protos
 
-if typing.TYPE_CHECKING:
-    import PIL.Image
-    import PIL.ImageFile
-    import IPython.display
-
-    IMAGE_TYPES = (PIL.Image.Image, IPython.display.Image)
-else:
-    IMAGE_TYPES = ()
-    try:
-        import PIL.Image
-        import PIL.ImageFile
-
-        IMAGE_TYPES = IMAGE_TYPES + (PIL.Image.Image,)
-    except ImportError:
-        PIL = None
-
-    try:
-        import IPython.display
-
-        IMAGE_TYPES = IMAGE_TYPES + (IPython.display.Image,)
-    except ImportError:
-        IPython = None
-
 
 __all__ = [
     "BlobDict",
@@ -94,62 +68,6 @@ def to_mode(x: ModeOptions) -> Mode:
     return _MODE[x]
 
 
-def _pil_to_blob(image: PIL.Image.Image) -> protos.Blob:
-    # If the image is a local file, return a file-based blob without any modification.
-    # Otherwise, return a lossless WebP blob (same quality with optimized size).
-    def file_blob(image: PIL.Image.Image) -> protos.Blob | None:
-        if not isinstance(image, PIL.ImageFile.ImageFile) or image.filename is None:
-            return None
-        filename = str(image.filename)
-        if not pathlib.Path(filename).is_file():
-            return None
-
-        mime_type = image.get_format_mimetype()
-        image_bytes = pathlib.Path(filename).read_bytes()
-
-        return protos.Blob(mime_type=mime_type, data=image_bytes)
-
-    def webp_blob(image: PIL.Image.Image) -> protos.Blob:
-        # Reference: https://pillow.readthedocs.io/en/stable/handbook/image-file-formats.html#webp
-        image_io = io.BytesIO()
-        image.save(image_io, format="webp", lossless=True)
-        image_io.seek(0)
-
-        mime_type = "image/webp"
-        image_bytes = image_io.read()
-
-        return protos.Blob(mime_type=mime_type, data=image_bytes)
-
-    return file_blob(image) or webp_blob(image)
-
-
-def image_to_blob(image) -> protos.Blob:
-    if PIL is not None:
-        if isinstance(image, PIL.Image.Image):
-            return _pil_to_blob(image)
-
-    if IPython is not None:
-        if isinstance(image, IPython.display.Image):
-            name = image.filename
-            if name is None:
-                raise ValueError(
-                    "Conversion failed. The `IPython.display.Image` can only be converted if "
-                    "it is constructed from a local file. Please ensure you are using the format: Image(filename='...')."
-                )
-            mime_type, _ = mimetypes.guess_type(name)
-            if mime_type is None:
-                mime_type = "image/unknown"
-
-            return protos.Blob(mime_type=mime_type, data=image.data)
-
-    raise TypeError(
-        "Image conversion failed. The input was expected to be of type `Image` "
-        "(either `PIL.Image.Image` or `IPython.display.Image`).\n"
-        f"However, received an object of type: {type(image)}.\n"
-        f"Object Value: {image}"
-    )
-
-
 class BlobDict(TypedDict):
     mime_type: str
     data: bytes
@@ -186,12 +104,7 @@ def is_blob_dict(d):
     return "mime_type" in d and "data" in d
 
 
-if typing.TYPE_CHECKING:
-    BlobType = Union[
-        protos.Blob, BlobDict, PIL.Image.Image, IPython.display.Image
-    ]  # Any for the images
-else:
-    BlobType = Union[protos.Blob, BlobDict, Any]
+BlobType = Union[protos.Blob, BlobDict, _image_types.ImageType]  # Any for the images
 
 
 def to_blob(blob: BlobType) -> protos.Blob:
@@ -200,8 +113,8 @@ def to_blob(blob: BlobType) -> protos.Blob:
 
     if isinstance(blob, protos.Blob):
         return blob
-    elif isinstance(blob, IMAGE_TYPES):
-        return image_to_blob(blob)
+    elif isinstance(blob, _image_types.IMAGE_TYPES):
+        return _image_types.image_to_blob(blob)
     else:
         if isinstance(blob, Mapping):
             raise KeyError(

google/generativeai/types/image_types/__init__.py

@@ -0,0 +1 @@
+from google.generativeai.types.image_types._image_types import *