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import gradio as gr | |
import tensorflow as tf | |
import tensorflow_hub as hub | |
from PIL import Image | |
import numpy as np | |
model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2') | |
# Function to execute | |
def style_transfer(content_img, style_img): | |
content_img = tf.image.resize(tf.convert_to_tensor(content_img,tf.float32)[tf.newaxis,...] / 255.,(512,512),preserve_aspect_ratio=True) | |
style_img = tf.convert_to_tensor(style_img,tf.float32)[tf.newaxis,...] / 255. | |
generated_img = model(content_img, style_img)[0] | |
return Image.fromarray(np.uint8(generated_img[0]*255)) | |
# Metadata | |
title = "Neural Style Transfer" | |
description = "Transform your photos into stunning works of art with our Neural Style Transfer app. Simply upload your content image and style image, and watch as the AI-powered model applies the artistic style of your choice to your photos. Unleash your creativity and turn ordinary images into extraordinary masterpieces!" | |
# Define labels for the input images | |
# input_labels = ["Content/Base Image", "Style Image"] | |
inputs = [ | |
gr.Image(label="Content/Base Image"), | |
gr.Image(label="Style Image") | |
] | |
# Example images | |
examples = [ | |
["content_example_1.jpg", "style_example_1.jpg"], | |
["content_example_2.jpg", "style_example_2.jpg"] | |
] | |
interface = gr.Interface(fn=style_transfer, | |
inputs=inputs, | |
outputs=["image"], | |
input_labels=input_labels, | |
title=title, | |
description=description, | |
examples=examples | |
) | |
interface.launch() |