Update app.py

app.py

@@ -13,29 +13,16 @@ 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):
-    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]
-
-    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
-            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), loaded_image_paths
+def load_image(image, image_size):
+    try:
+        image = keras.preprocessing.image.array_to_img(image)
+        image = image.resize((image_size, image_size))
+        image = keras.preprocessing.image.img_to_array(image)
+        image /= 255
+        return np.asarray(image)
+    except Exception as ex:
+        print("Image Load Failure: ", ex)
+        return None
 
 def load_model(model_path):
     if model_path is None or not exists(model_path):
@@ -46,7 +33,8 @@ def load_model(model_path):
 
 
 def classify_nd(model, nd_images, predict_args={}):
-    model_preds = model.predict(nd_images, **predict_args)
+    img = load_image(nd_images,(299, 299))
+    model_preds = model.predict(img, **predict_args)
     categories = ['drawings', 'hentai', 'neutral', 'porn', 'sexy']
 
     probs = []