Upload app.py

app.py

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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)
+# model=load_model('g_model_AtoB_000540.h5',cust)
+
+path  = [['ex1.jpg'], ['ex2.jpg'], ['ex3.jpg'],['ex4.jpg'],['ex5.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(shape=(256,256),type="numpy", label="Output Image").style(width=256, height=256),
+]
+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()
+