Update app.py

app.py

@@ -8,30 +8,26 @@ def greet(name):
     return "Hello " + name + "!!"
 
 def predict(img): 
-
-# Load the model
+    # Load the model
     model = load_model('keras_model.h5')
-
-# Create the array of the right shape to feed into the keras model
-# The 'length' or number of images you can put into the array is
-# determined by the first position in the shape tuple, in this case 1.
+    # Create the array of the right shape to feed into the keras model
+    # The 'length' or number of images you can put into the array is
+    # determined by the first position in the shape tuple, in this case 1.
     data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
-# Replace this with the path to your image
+    # Replace this with the path to your image
     image = img
-# image = Image.open('<IMAGE_PATH>')
-#resize the image to a 224x224 with the same strategy as in TM2:
-#resizing the image to be at least 224x224 and then cropping from the center
+    # image = Image.open('<IMAGE_PATH>')
+    #resize the image to a 224x224 with the same strategy as in TM2:
+    #resizing the image to be at least 224x224 and then cropping from the center
     size = (224, 224)
     image = ImageOps.fit(image, size, Image.ANTIALIAS)
-
-#turn the image into a numpy array
+    #turn the image into a numpy array
     image_array = np.asarray(image)
-# Normalize the image
+    # Normalize the image
     normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
-# Load the image into the array
+    # Load the image into the array
     data[0] = normalized_image_array
-
-# run the inference
+    # run the inference
     prediction = model.predict(data)
     gr.outputs.Label = open(labels.txt)
     return prediction