Create app.py

app.py

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+# -*- coding: utf-8 -*-
+import numpy as np
+
+import tensorflow as tf
+
+import gradio as gr
+
+from huggingface_hub import from_pretrained_keras
+
+model = from_pretrained_keras("keras-io/CycleGAN")
+
+# Define the standard image size.
+orig_img_size = (286, 286)
+# Size of the random crops to be used during training.
+input_img_size = (256, 256, 3)
+
+
+def normalize_img(img):
+    img = tf.cast(img, dtype=tf.float32)
+    # Map values in the range [-1, 1]
+    return (img / 127.5) - 1.0
+
+
+def preprocess_test_image(img):
+    # Only resizing and normalization for the test images.
+    img = tf.image.resize(img, [input_img_size[0], input_img_size[1]])
+    img = normalize_img(img)
+    return img
+
+
+# img_path = './n02381460_1010.jpg'
+
+
+def generate_img(img_path):
+    img = tf.io.read_file(img_path)
+    img = tf.image.decode_png(img)
+    img = tf.expand_dims(img, axis=0)
+    img = preprocess_test_image(img)
+    prediction = model(img, training=False)[0].numpy()
+    prediction = (prediction * 127.5 + 127.5).astype(np.uint8)
+    return prediction
+
+
+image = gr.inputs.Image(type="filepath")
+op = gr.outputs.Image(type="numpy")
+
+iface = gr.Interface(
+    generate_img,
+    image,
+    op,
+    title="CycleGAN",
+    description="Keras Implementation of CycleGAN model",
+    article='Author: <a href="https://huggingface.co/anuragshas">Anurag Singh</a>. Based on the keras example from <a href="https://keras.io/examples/generative/cyclegan/">A_K_Nain</a>',
+)
+
+iface.launch()