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import gradio as gr
import numpy as np
from PIL import Image
import tensorflow as tf
import tensorflow_hub as hub
style_transfer_model = hub.load("https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2")
def perform_style_transfer(content_image, style_image):
content_image = tf.convert_to_tensor(content_image, np.float32)[tf.newaxis, ...] / 255.
style_image = tf.convert_to_tensor(style_image, np.float32)[tf.newaxis, ...] / 255.
output = style_transfer_model(content_image, style_image)
stylized_image = output[0]
return Image.fromarray(np.uint8(stylized_image[0] * 255))
content_image_input = gr.inputs.Image(label="Content Image")
style_image_input = gr.inputs.Image(shape=(256, 256), label="Style Image")
# Examples
golden_gate = ["examples/golden_gate_bridge.jpeg", "examples/the_great_wave.jpeg"]
joshua_tree = ["examples/joshua_tree.jpeg", "examples/starry_night.jpeg"]
glacier = ["examples/glacier_national_park.jpeg", "examples/the_scream.jpg"]
app_interface = gr.Interface(fn=perform_style_transfer,
inputs=[content_image_input, style_image_input],
outputs="image",
title="Fast Neural Style Transfer",
description="Gradio demo for Fast Neural Style Transfer using a pretrained Image Stylization model from TensorFlow Hub. To use it, simply upload a content image and style image, or click one of the examples to load them. To learn more about the project, please find the references listed below.",
examples=[glacier, golden_gate, joshua_tree],
article="**References**\n\n"
"<a href='https://www.tensorflow.org/hub/tutorials/tf2_arbitrary_image_stylization' target='_blank'>1. Tutorial to implement Fast Neural Style Transfer using the pretrained model from TensorFlow Hub</a> \n"
"<a href='https://huggingface.co/spaces/luca-martial/neural-style-transfer' target='_blank'>2. The idea to build a neural style transfer application was inspired from this Hugging Face Space </a>")
app_interface.launch()
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