app.py

from PIL import Image
import tensorflow
import gradio as gr
import numpy as np
from tensorflow.keras.models import load_model
import tensorflow as tf

model = load_model('model')
print(model)
def infer(img):
  cartoonGAN = model.signatures["serving_default"]
  img = np.array(img.convert("RGB"))
  img = np.expand_dims(img, 0).astype(np.float32) / 127.5 - 1
  out = cartoonGAN(tf.constant(img))['output_1']
  out = ((out.numpy().squeeze() + 1) * 127.5).astype(np.uint8)
  return out

  
title = "CartoonGAN"
description = "Gradio Demo for CartoonGAN. To use it, simply upload an image."
article = "<p style='text-align: center'><a href='http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_CartoonGAN_Generative_Adversarial_CVPR_2018_paper.pdf' target='_blank'>Paper</a></p> <p style='text-align: center'>samples from repo: <img src='https://imgur.com/A9pkBlR.jpg'/><img src='https://imgur.com/s3YO3PB.jpg'/><img src='https://imgur.com/qExudXP.jpg'/><img src='https://imgur.com/q8Udor8.jpg'/><img src='https://imgur.com/Y3JqL3Q.jpg'/><img src='https://imgur.com/qpY0Drt.jpg'/></p>"
examples=[['ny_street.jpg'],['husky_study.jpg'],['tube_london.jpg'],['monalisa.jpg'],['dog-sleepy.gif'],['japan_fuji.jpg']]
gr.Interface(infer, gr.inputs.Image(type="pil"), gr.outputs.Image(type="pil"), title=title,description=description,article=article,enable_queue=True,examples=examples).launch(share=False)