chore: pre-commit

Author: boczekbartek

src/rai_core/rai/aggregators/base.py

@@ -14,37 +14,36 @@
 
 from abc import ABC, abstractmethod
 from collections import deque
-from typing import Deque, Generic, TypeVar, List
+from typing import Deque, Generic, List, TypeVar
 
 from langchain_core.messages import HumanMessage
-from rai.messages.multimodal import HumanMultimodalMessage
 
+from rai.messages.multimodal import HumanMultimodalMessage
 
 T = TypeVar("T")
 
 
 class BaseAggregator(ABC, Generic[T]):
     """
-    Interface for aggregators. 
+    Interface for aggregators.
 
     `__call__` method receives a message and appends it to the buffer.
     `get` method returns the aggregated message.
     """
-    def __init__(self, max_size: int | None=None) -> None:
+
+    def __init__(self, max_size: int | None = None) -> None:
         super().__init__()
         self._buffer: Deque[T] = deque()
         self.max_size = max_size
 
-    def __call__(
-        self, msg: T
-    ) -> None:
+    def __call__(self, msg: T) -> None:
         if self.max_size is not None and len(self._buffer) >= self.max_size:
             self._buffer.popleft()
         self._buffer.append(msg)
 
     @abstractmethod
     def get(self) -> HumanMessage | HumanMultimodalMessage | None:
-        """ Returns the aggregated message """
+        """Returns the aggregated message"""
         pass
 
     def clear(self) -> None:
@@ -55,4 +54,3 @@ def get_buffer(self) -> List[T]:
 
     def __str__(self) -> str:
         return f"{self.__class__.__name__}"
-    

src/rai_core/rai/aggregators/ros2/aggregators.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any, List, Sequence, cast
+from typing import Any, List, cast
 
 from langchain_core.messages import HumanMessage, SystemMessage
 from pydantic import BaseModel, Field
@@ -63,14 +63,12 @@ def get(self) -> HumanMultimodalMessage | None:
         return HumanMultimodalMessage(content="", images=[b64_image])
 
 
-class ROS2ImgVLMDescriptionAggregator(
-    BaseAggregator[Image | CompressedImage]
-):
+class ROS2ImgVLMDescriptionAggregator(BaseAggregator[Image | CompressedImage]):
     """
     Returns the VLM analysis of the last image in the aggregation buffer
     """
 
-    def __init__(self, max_size: int | None=None) -> None:
+    def __init__(self, max_size: int | None = None) -> None:
         super().__init__(max_size)
         self.llm = get_llm_model(model_type="simple_model", streaming=True)
 
@@ -110,7 +108,7 @@ class ROS2ImgVLMDiffAggregator(BaseAggregator[Image | CompressedImage]):
     aggregation buffer: 1st, midden, last
     """
 
-    def __init__(self, max_size: int | None=None) -> None:
+    def __init__(self, max_size: int | None = None) -> None:
         super().__init__(max_size)
         self.llm = get_llm_model(model_type="simple_model", streaming=True)
 
@@ -130,7 +128,7 @@ def get(self) -> HumanMessage | None:
             return None
 
         b64_images = [encode_ros2_img_to_base64(msg) for msg in msgs]
-        
+
         self.clear()
 
         b64_images = self.get_key_elements(b64_images)

src/rai_core/rai/tools/ros2/generic/topics.py

@@ -22,7 +22,10 @@
 from pydantic import BaseModel, Field
 
 from rai.communication.ros2 import ROS2Connector, ROS2Message
-from rai.communication.ros2.api.conversion import encode_ros2_img_to_base64, ros2_message_to_dict
+from rai.communication.ros2.api.conversion import (
+    encode_ros2_img_to_base64,
+    ros2_message_to_dict,
+)
 from rai.messages import MultimodalArtifact
 from rai.tools.ros2.base import BaseROS2Tool, BaseROS2Toolkit