ci: auto fixes from pre-commit.ci

For more information, see https://pre-commit.ci

examples/quickstart/client.py

@@ -1,17 +1,18 @@
-import bentoml
 import numpy as np
 
-input_series=[
+import bentoml
+
+input_series = [
     [5.1, 3.5, 1.4, 0.2],
     [6.2, 2.9, 4.3, 1.3],
     [5.9, 3.0, 5.1, 1.8],
     [4.6, 3.1, 1.5, 0.2],
     [6.7, 3.1, 4.4, 1.4],
     [5.5, 2.6, 4.4, 1.2],
     [7.7, 3.0, 6.1, 2.3],
-    [4.9, 3.0, 1.4, 0.2]
+    [4.9, 3.0, 1.4, 0.2],
 ]
 
-with bentoml.SyncHTTPClient('http://localhost:3000') as client:
+with bentoml.SyncHTTPClient("http://localhost:3000") as client:
     pred: np.ndarray = client.classify(input_series)
     print(pred)

examples/quickstart/service.py

@@ -1,10 +1,10 @@
 import numpy as np
+from pydantic import Field
 from typing_extensions import Annotated
 
 import bentoml
 from bentoml.validators import Shape
 
-from pydantic import Field
 
 @bentoml.service(
     resources={
@@ -13,27 +13,29 @@
     },
 )
 class IrisClassifier:
-    '''
+    """
     A simple Iris classification service using a sklearn model
-    '''
+    """
 
     # Load in the class scope to declare the model as a dependency of the service
     iris_model = bentoml.models.get("iris_sklearn:latest")
 
     def __init__(self):
-        '''
+        """
         Initialize the service by loading the model from the model store
-        '''
+        """
         import joblib
 
         self.model = joblib.load(self.iris_model.path_of("model.pkl"))
 
     @bentoml.api
     def classify(
         self,
-        input_series: Annotated[np.ndarray, Shape((-1, 4))] = Field(default=[[5.2, 2.3, 5.0, 0.7]]),
+        input_series: Annotated[np.ndarray, Shape((-1, 4))] = Field(
+            default=[[5.2, 2.3, 5.0, 0.7]]
+        ),
     ) -> np.ndarray:
-        '''
+        """
         Define API with preprocessing and model inference logic
-        '''
+        """
         return self.model.predict(input_series)