ruff format

machine-learning/app/models/base.py

@@ -162,7 +162,7 @@ class InferenceModel(ABC):
     def _model_format_default(self) -> ModelFormat:
         if rknn.rknnpool.is_available and settings.rknn:
             return ModelFormat.RKNN
-        elif ann.ann.is_available and settings.ann :
+        elif ann.ann.is_available and settings.ann:
             return ModelFormat.ARMNN
         else:
             return ModelFormat.ONNX

machine-learning/app/sessions/rknn.py

@@ -12,24 +12,21 @@ from app.schemas import SessionNode
 
 from ..config import log
 
+
 def runInfrence(rknn_lite, input):
-    outputs = rknn_lite.inference(inputs=[input], data_format='nchw')
+    outputs = rknn_lite.inference(inputs=[input], data_format="nchw")
 
     return outputs
 
+
 class RknnSession:
     def __init__(self, model_path: Path | str):
-
         self.model_path = Path(model_path)
         self.ort_model_path = str(self.model_path).replace(".rknn", ".onnx")
-        self.tpe = 1 if 'textual' in str(self.model_path) else 2
+        self.tpe = 1 if "textual" in str(self.model_path) else 2
 
         log.info(f"Loading RKNN model from {self.model_path} with {self.tpe} threads.")
-        self.rknnpool = rknnPoolExecutor(
-            rknnModel=self.model_path.as_posix(),
-            TPEs= self.tpe,
-            func=runInfrence)
-
+        self.rknnpool = rknnPoolExecutor(rknnModel=self.model_path.as_posix(), TPEs=self.tpe, func=runInfrence)
 
         self.ort_session = ort.InferenceSession(
             self.ort_model_path,
@@ -39,7 +36,6 @@ class RknnSession:
 
         del self.ort_session
 
-
     def __del__(self):
         self.rknnpool.release()
 
@@ -53,11 +49,9 @@ class RknnSession:
         self,
         output_names: list[str] | None,
         input_feed: dict[str, NDArray[np.float32]] | dict[str, NDArray[np.int32]],
-	    run_options: Any = None,
+        run_options: Any = None,
     ):
-
         input_data = [np.ascontiguousarray(v) for v in input_feed.values()][0]
         self.rknnpool.put(input_data)
         outputs = self.rknnpool.get()[0]
         return outputs
-