type hints + simplify code if input is numpy array

app.py

@@ -16,22 +16,14 @@ output_layer = compiled_model.output(0)
 ##### 
 #Inference
 ##### 
-def predict(img):
-    
-    print(f'input type: {type(img)}')
-    
-    # https://stackoverflow.com/a/32264327
-    image = cv2.cvtColor(np.array(img), cv2.COLOR_BGR2RGB)
+def predict(img: np.ndarray) -> str:
     
     # The MobileNet model expects images in RGB format.
-    #image = cv2.cvtColor(cv2.imread(img), code=cv2.COLOR_BGR2RGB)
+    image = cv2.cvtColor(img, code=cv2.COLOR_BGR2RGB)
     # Resize to MobileNet image shape.
     input_image = cv2.resize(src=image, dsize=(224, 224))
     # Reshape to model input shape.
     input_image = np.expand_dims(input_image, 0)
-   
-    
-    # TODO: get n best results with corresponding probabilities?
     
     # Get inference result
     result_infer = compiled_model([input_image])[output_layer]
@@ -43,7 +35,9 @@ def predict(img):
     # The model description states that for this model, class 0 is a background.
     # Therefore, a background must be added at the beginning of imagenet_classes.
     imagenet_classes = ['background'] + imagenet_classes
-    return imagenet_classes[result_index]
+    best_class = imagenet_classes[result_index]
+    # TODO: get n best results with corresponding probabilities?
+    return best_class
     
 ##### 
 #Gradio Setup