Update app.py

app.py

@@ -13,46 +13,54 @@ import tensorflow_hub as hub
 IMAGE_DIM = 299   # required/default image dimensionality
 model =  tf.keras.models.load_model("nsfw.299x299.h5", custom_objects={'KerasLayer': hub.KerasLayer},compile=False)
 
-def load_images(image_paths, image_size, verbose=True):
-    '''
-    Function for loading images into numpy arrays for passing to model.predict
-    inputs:
-        image_paths: list of image paths to load
-        image_size: size into which images should be resized
-        verbose: show all of the image path and sizes loaded
+# def load_images(image_paths, image_size, verbose=True):
+#     '''
+#     Function for loading images into numpy arrays for passing to model.predict
+#     inputs:
+#         image_paths: list of image paths to load
+#         image_size: size into which images should be resized
+#         verbose: show all of the image path and sizes loaded
     
-    outputs:
-        loaded_images: loaded images on which keras model can run predictions
-        loaded_image_indexes: paths of images which the function is able to process
+#     outputs:
+#         loaded_images: loaded images on which keras model can run predictions
+#         loaded_image_indexes: paths of images which the function is able to process
     
-    '''
-    loaded_images = []
-    loaded_image_paths = []
+#     '''
+#     loaded_images = []
+#     loaded_image_paths = []
 
-    if isdir(image_paths):
-        parent = abspath(image_paths)
-        image_paths = [join(parent, f) for f in listdir(image_paths) if isfile(join(parent, f))]
-    elif isfile(image_paths):
-        image_paths = [image_paths]
+#     if isdir(image_paths):
+#         parent = abspath(image_paths)
+#         image_paths = [join(parent, f) for f in listdir(image_paths) if isfile(join(parent, f))]
+#     elif isfile(image_paths):
+#         image_paths = [image_paths]
 
-    for img_path in image_paths:
-        try:
-            if verbose:
-                print(img_path, "size:", image_size)
-            image = keras.preprocessing.image.load_img(img_path, target_size=image_size)
-            image = keras.preprocessing.image.img_to_array(image)
-            image /= 255
-            image
-            loaded_images.append(image)
-            loaded_image_paths.append(img_path)
-        except Exception as ex:
-            print("Image Load Failure: ", img_path, ex)
+#     for img_path in image_paths:
+#         try:
+#             if verbose:
+#                 print(img_path, "size:", image_size)
+#             image = keras.preprocessing.image.load_img(img_path, target_size=image_size)
+#             image = keras.preprocessing.image.img_to_array(image)
+#             image /= 255
+#             image
+#             loaded_images.append(image)
+#             loaded_image_paths.append(img_path)
+#         except Exception as ex:
+#             print("Image Load Failure: ", img_path, ex)
     
+#     return np.asarray(loaded_images)
+def load_images(image):
+    loaded_images = []
+    image = keras.preprocessing.image.array_to_img(image)
+    image = image.resize((299, 299))
+    image = keras.preprocessing.image.img_to_array(image)
+    image /= 255
+    loaded_images.append(image)
     return np.asarray(loaded_images)
 
 def classify_nd(model, nd_images, predict_args={}):
-    file_path = nd_images.filename
-    img = load_images(file_path,(299,299))
+    # file_path = nd_images.filename
+    img = load_images(nd_images)
     model_preds = model.predict(img, **predict_args)
     categories = ['drawings', 'hentai', 'neutral', 'porn', 'sexy']