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import gradio as gr
import keras
from keras.models import load_model
# from keras_contrib.layers.normalization.instancenormalization import InstanceNormalization
from tensorflow_addons.layers import InstanceNormalization
import matplotlib.pyplot as plt
import numpy as np
cust = {'InstanceNormalization': InstanceNormalization}
model=load_model('g_model_AtoB_002160.h5',cust)
path = [['ex1.jpg'], ['ex2.jpg']]
def show_preds_image(image_path):
A = plt.imread(image_path)
A = (A - 127.5) / 127.5
A = np.expand_dims(A,axis=0)
B = model.predict(A)
B = np.squeeze(B,axis=0)
B = (B + 1) / 2.0
return B
inputs_image = [
gr.components.Image(shape=(256,256),type="filepath", label="Input Image"),
]
outputs_image = [
gr.components.Image(type="numpy", label="Output Image"),
]
interface_image = gr.Interface(
fn=show_preds_image,
inputs=inputs_image,
outputs=outputs_image,
title="photo2monet",
examples=path,
cache_examples=False,
)
gr.TabbedInterface(
[interface_image],
tab_names=['Image inference']
).queue().launch()
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