Create app.py

app.py

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+import os
+import tensorflow as tf
+os.environ['TFHUB_MODEL_LOAD_FORMAT'] = 'COMPRESSED'
+import numpy as np
+import PIL.Image
+import gradio as gr
+import tensorflow_hub as hub
+import matplotlib.pyplot as plt
+
+
+def tensor_to_image(tensor):
+    tensor = tensor*255
+    tensor = np.array(tensor, dtype=np.uint8)
+    if np.ndim(tensor)>3:
+      assert tensor.shape[0] == 1
+      tensor = tensor[0]
+    return PIL.Image.fromarray(tensor)
+
+
+style_urls = {
+    'Kanagawa great wave': 'The_Great_Wave_off_Kanagawa.jpg',
+    'Kandinsky composition 7': 'Kandinsky_Composition_7.jpg',
+    'Hubble pillars of creation': 'Pillars_of_creation_2014_HST_WFC3-UVIS_full-res_denoised.jpg',
+    'Van gogh starry night': 'Van_Gogh_-_Starry_Night_-_Google_Art_Project.jpg',
+    'Turner nantes': 'JMW_Turner_-_Nantes_from_the_Ile_Feydeau.jpg',
+    'Munch scream': 'Edvard_Munch.jpg',
+    'Picasso demoiselles avignon': 'Les_Demoiselles.jpg',
+    'Picasso violin': 'picaso_violin.jpg',
+    'Picasso bottle of rum': 'picaso_rum.jpg',
+    'Fire': 'Large_bonfire.jpg',
+    'Derkovits woman head': 'Derkovits_Gyula_Woman_head_1922.jpg',
+    'Amadeo style life': 'Amadeo_Souza_Cardoso.jpg',
+    'Derkovtis talig': 'Derkovits_Gyula_Talig.jpg',
+    'Kadishman': 'kadishman.jpeg'
+}
+
+
+style_images = [k for k, v in style_urls.items()]
+
+
+content_image_input = gr.inputs.Image(label="Content Image")
+radio_style = gr.Radio(style_images, label="Choose Style")
+
+
+def perform_neural_transfer(content_image_input, style_image_input):
+
+    content_image = content_image_input.astype(np.float32)[np.newaxis, ...] / 255.
+    
+    style_image_input = style_urls[style_image_input]
+    style_image_input = plt.imread(style_image_input)
+    style_image = style_image_input.astype(np.float32)[np.newaxis, ...] / 255.
+    
+    style_image = tf.image.resize(style_image, (256, 256))
+
+    hub_module = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')
+
+    outputs = hub_module(tf.constant(content_image), tf.constant(style_image))
+    stylized_image = outputs[0]
+
+    return tensor_to_image(stylized_image)
+
+
+app_interface = gr.Interface(fn=perform_neural_transfer,
+                             inputs=[content_image_input, radio_style],
+                             outputs="image",
+                             title="Art Generation with Neural Style Transfer",
+                            )
+app_interface.launch()