Merge branch 'master' into update-region

Author: phatvo9

CHANGELOG.md

@@ -1,3 +1,9 @@
+## [[11.3.0]](https://github.com/Clarifai/clarifai-python/releases/tag/11.3.0) - [PyPI](https://pypi.org/project/clarifai/11.3.0/) - 2025-04-22
+
+### Changed
+- We support pythonic models now. See [runners-examples](https://github.com/clarifai/runners-examples) [(#525)](https://github.com/Clarifai/clarifai-python/pull/525)
+- Fixed failing tests. [(#559)](https://github.com/Clarifai/clarifai-python/pull/559)
+
 ## [[11.2.3]](https://github.com/Clarifai/clarifai-python/releases/tag/11.2.3) - [PyPI](https://pypi.org/project/clarifai/11.2.3/) - 2025-04-08
 
 ### Changed

clarifai/__init__.py

@@ -1 +1 @@
-__version__ = "11.2.3"
+__version__ = "11.3.0"

clarifai/cli/model.py

@@ -69,6 +69,32 @@ def download_checkpoints(model_path, out_path, stage):
   builder.download_checkpoints(stage=stage, checkpoint_path_override=out_path)
 
 
+@model.command()
+@click.argument(
+    "model_path",
+    type=click.Path(exists=True),
+    required=False,
+    default=".",
+)
+@click.option(
+    '--out_path',
+    type=click.Path(exists=False),
+    required=False,
+    default=None,
+    help='Path to write the method signature defitions to. If not provided, use stdout.')
+def signatures(model_path, out_path):
+  """Generate method signatures for the model."""
+
+  from clarifai.runners.models.model_builder import ModelBuilder
+  builder = ModelBuilder(model_path, download_validation_only=True)
+  signatures = builder.method_signatures_yaml()
+  if out_path:
+    with open(out_path, 'w') as f:
+      f.write(signatures)
+  else:
+    click.echo(signatures)
+
+
 @model.command()
 @click.argument(
     "model_path",

clarifai/client/deployment.py

@@ -3,6 +3,7 @@
 from clarifai.client.base import BaseClient
 from clarifai.client.lister import Lister
 from clarifai.utils.logging import logger
+from clarifai.utils.protobuf import dict_to_protobuf
 
 
 class Deployment(Lister, BaseClient):
@@ -28,7 +29,8 @@ def __init__(self,
         **kwargs: Additional keyword arguments to be passed to the deployment.
     """
     self.kwargs = {**kwargs, 'id': deployment_id, 'user_id': user_id}
-    self.deployment_info = resources_pb2.Deployment(**self.kwargs)
+    self.deployment_info = resources_pb2.Deployment()
+    dict_to_protobuf(self.deployment_info, self.kwargs)
     self.logger = logger
     BaseClient.__init__(
         self,

clarifai/client/model.py

@@ -32,7 +32,7 @@
 from clarifai.utils.model_train import (find_and_replace_key, params_parser,
                                         response_to_model_params, response_to_param_info,
                                         response_to_templates)
-
+from clarifai.utils.protobuf import dict_to_protobuf
 MAX_SIZE_PER_STREAM = int(89_128_960)  # 85GiB
 MIN_CHUNK_FOR_UPLOAD_FILE = int(5_242_880)  # 5MiB
 MAX_CHUNK_FOR_UPLOAD_FILE = int(5_242_880_000)  # 5GiB
@@ -73,8 +73,11 @@ def __init__(self,
       user_id, app_id, _, model_id, model_version_id = ClarifaiUrlHelper.split_clarifai_url(url)
       model_version = {'id': model_version_id}
       kwargs = {'user_id': user_id, 'app_id': app_id}
+
     self.kwargs = {**kwargs, 'id': model_id, 'model_version': model_version, }
-    self.model_info = resources_pb2.Model(**self.kwargs)
+    self.model_info = resources_pb2.Model()
+    dict_to_protobuf(self.model_info, self.kwargs)
+
     self.logger = logger
     self.training_params = {}
     self.input_types = None
@@ -983,7 +986,8 @@ def load_info(self) -> None:
 
     dict_response = MessageToDict(response, preserving_proto_field_name=True)
     self.kwargs = self.process_response_keys(dict_response['model'])
-    self.model_info = resources_pb2.Model(**self.kwargs)
+    self.model_info = resources_pb2.Model()
+    dict_to_protobuf(self.model_info, self.kwargs)
 
   def __str__(self):
     if len(self.kwargs) < 10:

clarifai/client/model_client.py

@@ -259,7 +259,6 @@ def _predict(
       inputs,  # TODO set up functions according to fetched signatures?
       method_name: str = 'predict',
   ) -> Any:
-
     input_signature = self._method_signatures[method_name].input_fields
     output_signature = self._method_signatures[method_name].output_fields
 
@@ -328,7 +327,7 @@ def _predict_by_proto(
       response = self.STUB.PostModelOutputs(request)
       if status_is_retryable(
           response.status.code) and time.time() - start_time < 60 * 10:  # 10 minutes
-        logger.info(f"Model predict failed with response {response!r}")
+        logger.info("Model is still deploying, please wait...")
         time.sleep(next(backoff_iterator))
         continue
 

clarifai/runners/models/model_builder.py

@@ -43,6 +43,19 @@ def _clear_line(n: int = 1) -> None:
     print(LINE_UP, end=LINE_CLEAR, flush=True)
 
 
+def is_related(object_class, main_class):
+  # Check if the object_class is a subclass of main_class
+  if issubclass(object_class, main_class):
+    return True
+
+  # Check if the object_class is a subclass of any of the parent classes of main_class
+  parent_classes = object_class.__bases__
+  for parent in parent_classes:
+    if main_class in parent.__bases__:
+      return True
+  return False
+
+
 class ModelBuilder:
   DEFAULT_CHECKPOINT_SIZE = 50 * 1024**3  # 50 GiB
 
@@ -125,7 +138,7 @@ def custom_import(name, globals=None, locals=None, fromlist=(), level=0):
     # Find all classes in the model.py file that are subclasses of ModelClass
     classes = [
         cls for _, cls in inspect.getmembers(module, inspect.isclass)
-        if issubclass(cls, ModelClass) and cls.__module__ == module.__name__
+        if is_related(cls, ModelClass) and cls.__module__ == module.__name__
     ]
     #  Ensure there is exactly one subclass of BaseRunner in the model.py file
     if len(classes) != 1:

clarifai/runners/models/model_class.py

@@ -1,3 +1,4 @@
+import collections.abc as abc
 import inspect
 import itertools
 import logging
@@ -271,8 +272,9 @@ def _convert_input_protos_to_python(self, inputs: List[resources_pb2.Input],
         if k not in python_param_types:
           continue
 
-        if hasattr(python_param_types[k], "__args__") and getattr(python_param_types[k],
-                                                                  "__origin__", None) == Iterator:
+        if hasattr(python_param_types[k],
+                   "__args__") and (getattr(python_param_types[k], "__origin__",
+                                            None) in [abc.Iterator, abc.Generator, abc.Iterable]):
           # get the type of the items in the stream
           stream_type = python_param_types[k].__args__[0]
 

clarifai/runners/utils/data_utils.py

@@ -115,12 +115,7 @@ def to_proto(self, proto=None) -> InputFieldProto:
     # proto.is_param = self.is_param
 
     if self.default is not None:
-      if isinstance(self.default, str) or isinstance(self.default, bool) or isinstance(
-          self.default, (int, float)):
-        proto.default = str(self.default)
-      else:
-        import json
-        proto.default = json.dumps(self.default)
+      proto = self.set_default(proto, self.default)
 
     return proto
 
@@ -169,17 +164,57 @@ def from_proto(cls, proto):
 
   @classmethod
   def set_default(cls, proto=None, default=None):
-
-    if proto is None:
-      proto = InputFieldProto()
-    if default is not None:
-      if isinstance(default, str) or isinstance(default, bool) or isinstance(
-          default, (int, float)):
-        proto.default = str(default)
-      else:
-        import json
+    try:
+      import json
+      if proto is None:
+        proto = InputFieldProto()
+      if default is not None:
         proto.default = json.dumps(default)
-    return proto
+      return proto
+    except Exception:
+      if default is not None:
+        proto.default = str(default)
+      return proto
+    except Exception as e:
+      raise ValueError(
+          f"Error setting default value of type, {type(default)} and value: {default}: {e}")
+
+  @classmethod
+  def get_default(cls, proto):
+    default_str = proto.default
+    default = None
+    import json
+    try:
+      # Attempt to parse as JSON first (for complex types)
+      return json.loads(default_str)
+    except json.JSONDecodeError:
+      pass
+    # Check for boolean values stored as "True" or "False"
+    if proto.type == resources_pb2.ModelTypeField.DataType.BOOL:
+      try:
+        default = bool(default_str)
+      except ValueError:
+        pass
+    # Try to parse as integer
+    elif proto.type == resources_pb2.ModelTypeField.DataType.INT:
+      try:
+        default = int(default_str)
+      except ValueError:
+        pass
+
+    # Try to parse as float
+    elif proto.type == resources_pb2.ModelTypeField.DataType.FLOAT:
+      try:
+        default = float(default_str)
+      except ValueError:
+        pass
+    elif proto.type == resources_pb2.ModelTypeField.DataType.STR:
+      default = default_str
+
+    if default is None:
+      # If all parsing fails, return the string value
+      default = default_str
+    return default
 
 
 class DataConverter:
@@ -211,54 +246,68 @@ def _convert_field(cls, old_data: resources_pb2.Data,
     if data_type == resources_pb2.ModelTypeField.DataType.STR:
       if old_data.HasField('text'):
         new_data.string_value = old_data.text.raw
+        old_data.ClearField('text')
       return new_data
     elif data_type == resources_pb2.ModelTypeField.DataType.IMAGE:
       if old_data.HasField('image'):
         new_data.image.CopyFrom(old_data.image)
+        # Clear the old field to avoid duplication
+        old_data.ClearField('image')
       return new_data
     elif data_type == resources_pb2.ModelTypeField.DataType.VIDEO:
       if old_data.HasField('video'):
         new_data.video.CopyFrom(old_data.video)
+        old_data.ClearField('video')
       return new_data
     elif data_type == resources_pb2.ModelTypeField.DataType.BOOL:
       if old_data.bool_value is not False:
         new_data.bool_value = old_data.bool_value
+        old_data.bool_value = False
       return new_data
     elif data_type == resources_pb2.ModelTypeField.DataType.INT:
       if old_data.int_value != 0:
         new_data.int_value = old_data.int_value
+        old_data.int_value = 0
       return new_data
     elif data_type == resources_pb2.ModelTypeField.DataType.FLOAT:
       if old_data.float_value != 0.0:
         new_data.float_value = old_data.float_value
+        old_data.float_value = 0.0
       return new_data
     elif data_type == resources_pb2.ModelTypeField.DataType.BYTES:
       if old_data.bytes_value != b"":
         new_data.bytes_value = old_data.bytes_value
+        old_data.bytes_value = b""
       return new_data
     elif data_type == resources_pb2.ModelTypeField.DataType.NDARRAY:
       if old_data.HasField('ndarray'):
         new_data.ndarray.CopyFrom(old_data.ndarray)
+        old_data.ClearField('ndarray')
       return new_data
     elif data_type == resources_pb2.ModelTypeField.DataType.TEXT:
       if old_data.HasField('text'):
         new_data.text.CopyFrom(old_data.text)
+        old_data.ClearField('text')
       return new_data
     elif data_type == resources_pb2.ModelTypeField.DataType.AUDIO:
       if old_data.HasField('audio'):
         new_data.audio.CopyFrom(old_data.audio)
+        old_data.ClearField('audio')
       return new_data
     elif data_type == resources_pb2.ModelTypeField.DataType.CONCEPT:
       if old_data.concepts:
         new_data.concepts.extend(old_data.concepts)
+        old_data.ClearField('concepts')
       return new_data
     elif data_type == resources_pb2.ModelTypeField.DataType.REGION:
       if old_data.regions:
         new_data.regions.extend(old_data.regions)
+        old_data.ClearField('regions')
       return new_data
     elif data_type == resources_pb2.ModelTypeField.DataType.FRAME:
       if old_data.frames:
         new_data.frames.extend(old_data.frames)
+        old_data.ClearField('frames')
       return new_data
     elif data_type == resources_pb2.ModelTypeField.DataType.LIST:
       if not field.type_args: