app.py

import gradio as gr
import tensorflow as tf
import tensorflow_hub as hub
import matplotlib.pyplot as plt
import numpy as np
import PIL.Image

hub_model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2') 

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)

def stylize(content_image, style_image):
    content_image = content_image.astype(np.float32)[np.newaxis, ...] / 255.
    style_image = style_image.astype(np.float32)[np.newaxis, ...] / 255.
    stylized_image = hub_model(tf.constant(content_image), tf.constant(style_image))[0]
    return tensor_to_image(stylized_image)
    
content_examples =[["example_paris.jpeg"], ["example_vangogh.jpeg"]]
style_examples = [["example_aristotle.jpeg"], ["example_dali.jpeg"]]
title = "Fast Neural Style Transfer using TF-Hub"
description = "Demo for neural style transfer using the pretrained Arbitrary Image Stylization model from TensorFlow Hub."
inputs = gr.inputs.Image(label="Content Image", source=["upload", "webcam"])
outputs =  gr.outputs.Image(label="Style Image")

iface = gr.Interface(fn=stylize, 
                     inputs=["image", "image"], 
                     outputs="image",
                     title=title,
                     description=description,
                     examples=[content_examples, style_examples])
iface.launch()