Create app.py

app.py

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+import gradio as gr
+from tensorflow.keras.models import load_model
+from tensorflow.keras.layers import Input
+from tensorflow_addons.layers import InstanceNormalization
+import numpy as np
+from PIL import Image
+
+def load_image(img):
+    with open(img.name, 'rb') as f:
+        img_data = f.read()
+    return img_data
+
+def translate_image(image):
+    # load the model
+    cust = {'InstanceNormalization': InstanceNormalization}
+    model_AtoB = load_model('/Users/riyaparikh/Desktop/ML Projects /CycleGAN/g_model_BtoA_004000.h5', cust)
+
+    # convert to numpy array
+    image_np = np.array(image)
+#     image = Resizing(256, 256)(image)
+    # normalize pixels
+    image_np = (image_np - 127.5) / 127.5
+    # add batch dimension
+    image_np = np.expand_dims(image_np, axis=0)
+    # translate image
+    image_tar = model_AtoB.predict(image_np)
+    # scale from [-1,1] to [0,1]
+    image_tar = (image_tar + 1) / 2.0
+    # convert back to PIL image
+    image_tar = (image_tar[0] * 255).astype(np.uint8)
+    image_tar = Image.fromarray(image_tar)
+    return image_tar
+
+
+
+# create the interface
+input_image = gr.inputs.Image(type="pil")
+output_image = gr.outputs.Image(type="pil")
+
+iface = gr.Interface(fn=translate_image, inputs=input_image, outputs=output_image,
+                     title="Van Gogh-ify Your Images🎨👨‍🎨",
+                     description="Translate an image from one domain to another using CycleGAN")
+
+iface.launch()
+