init

app.py

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+import gradio as gr
+import tensorflow as tf
+import wget
+
+enc_url = 'https://huggingface.co/ariG23498/nst/blob/main/nst-encoder.h5'
+enc_filename = wget.download(enc_url)
+
+dec_url = 'https://huggingface.co/ariG23498/nst/blob/main/nst-decoder.h5'
+dec_filename = wget.download(dec_url)
+
+encoder = tf.keras.models.load_model(enc_filename, compile=False)
+decoder = tf.keras.models.load_model(dec_filename, compile=False)
+
+def get_mean_std(tensor, epsilon=1e-5):
+    axes = [1, 2]
+    tensor_mean, tensor_var = tf.nn.moments(tensor, axes=axes, keepdims=True)
+    tensor_std = tf.sqrt(tensor_var + epsilon)
+    return tensor_mean, tensor_std
+
+def ada_in(style, content, epsilon=1e-5):
+    c_mean, c_std = get_mean_std(content)
+    s_mean, s_std = get_mean_std(style)
+    t = s_std * (content - c_mean) / c_std + s_mean
+    return t
+
+def load_resize(image):
+    image = tf.image.convert_image_dtype(image, dtype="float32")
+    image = tf.image.resize(image, (224, 224))
+    return image
+
+def infer(style, content):
+    style = load_resize(style)
+    style = style[tf.newaxis, ...]
+    content = load_resize(content)
+    content = content[tf.newaxis, ...]
+
+    style_enc = encoder(style)
+    content_enc = encoder(content)
+    
+    t = ada_in(style=style_enc, content=content_enc)
+    
+    recons_image = decoder(t)
+    return recons_image[0].numpy()
+
+iface = gr.Interface(
+	fn=infer, 
+	inputs=[gr.inputs.Image(label="style"),
+		gr.inputs.Image(label="content")],
+	outputs="image").launch()

requirements.txt

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+tensorflow>2.4
+wget