fixed input method

app.py

@@ -17,24 +17,24 @@ def predict(img):
 # 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)
+    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
 # Replace this with the path to your image
-image = Image.open('<IMAGE_PATH>')
+    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)
+    size = (224, 224)
+    image = ImageOps.fit(image, size, Image.ANTIALIAS)
 
 #turn the image into a numpy array
-image_array = np.asarray(image)
+    image_array = np.asarray(image)
 # Normalize the image
-normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
+    normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
 # Load the image into the array
-data[0] = normalized_image_array
+    data[0] = normalized_image_array
 
 # run the inference
-prediction = model.predict(data)
-print(prediction)
+    prediction = model.predict(data)
+    print(prediction)
     return prediction