Update app.py

app.py

@@ -16,98 +16,7 @@ width, height = keras.preprocessing.image.load_img(base_image_path).size
 img_nrows = 400
 img_ncols = int(width * img_nrows / height)
 
-def preprocess_image(image_path):
-    # Util function to open, resize and format pictures into appropriate tensors
-    img = keras.preprocessing.image.load_img(
-        image_path, target_size=(img_nrows, img_ncols)
-    )
-    img = keras.preprocessing.image.img_to_array(img)
-    img = np.expand_dims(img, axis=0)
-    img = vgg19.preprocess_input(img)
-    return tf.convert_to_tensor(img)
-
-def deprocess_image(x):
-    # Util function to convert a tensor into a valid image
-    x = x.reshape((img_nrows, img_ncols, 3))
-    # Remove zero-center by mean pixel
-    x[:, :, 0] += 103.939
-    x[:, :, 1] += 116.779
-    x[:, :, 2] += 123.68
-    # 'BGR'->'RGB'
-    x = x[:, :, ::-1]
-    x = np.clip(x, 0, 255).astype("uint8")
-    return x
-
-# The gram matrix of an image tensor (feature-wise outer product)
-
-def gram_matrix(x):
-    x = tf.transpose(x, (2, 0, 1))
-    features = tf.reshape(x, (tf.shape(x)[0], -1))
-    gram = tf.matmul(features, tf.transpose(features))
-    return gram
-
-# The "style loss" is designed to maintain
-# the style of the reference image in the generated image.
-# It is based on the gram matrices (which capture style) of
-# feature maps from the style reference image
-# and from the generated image
-
-def style_loss(style, combination):
-    S = gram_matrix(style)
-    C = gram_matrix(combination)
-    channels = 3
-    size = img_nrows * img_ncols
-    return tf.reduce_sum(tf.square(S - C)) / (4.0 * (channels ** 2) * (size ** 2))
-
-# An auxiliary loss function
-# designed to maintain the "content" of the
-# base image in the generated image
-
-def content_loss(base, combination):
-    return tf.reduce_sum(tf.square(combination - base))
-
-# The 3rd loss function, total variation loss,
-# designed to keep the generated image locally coherent
-
-def total_variation_loss(x):
-    a = tf.square(
-        x[:, : img_nrows - 1, : img_ncols - 1, :] - x[:, 1:, : img_ncols - 1, :]
-    )
-    b = tf.square(
-        x[:, : img_nrows - 1, : img_ncols - 1, :] - x[:, : img_nrows - 1, 1:, :]
-    )
-    return tf.reduce_sum(tf.pow(a + b, 1.25))
-
-def compute_loss(combination_image, base_image, style_reference_image):
-    input_tensor = tf.concat(
-        [base_image, style_reference_image, combination_image], axis=0
-    )
-    features = feature_extractor(input_tensor)
-
-    # Initialize the loss
-    loss = tf.zeros(shape=())
-
-    # Add content loss
-    layer_features = features[content_layer_name]
-    base_image_features = layer_features[0, :, :, :]
-    combination_features = layer_features[2, :, :, :]
-    loss = loss + content_weight * content_loss(
-        base_image_features, combination_features
-    )
-    # Add style loss
-    for layer_name in style_layer_names:
-        layer_features = features[layer_name]
-        style_reference_features = layer_features[1, :, :, :]
-        combination_features = layer_features[2, :, :, :]
-        sl = style_loss(style_reference_features, combination_features)
-        loss += (style_weight / len(style_layer_names)) * sl
-
-    # Add total variation loss
-    loss += total_variation_weight * total_variation_loss(combination_image)
-    return loss
-
 # Build a VGG19 model loaded with pre-trained ImageNet weights
-# model = vgg19.VGG19(weights="imagenet", include_top=False)
 model = from_pretrained_keras("rushic24/keras-VGG19")
 
 # Get the symbolic outputs of each "key" layer (we gave them unique names).