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app.py

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+import gradio as gr
+
+import kornia as K
+from kornia.core import Tensor
+
+import numpy as np
+
+def rescale_aa(file, height, width):
+    
+    img: Tensor = K.io.load_image(file.name, K.io.ImageLoadType.RGB32)
+    img = img[None]
+
+    img_rescale: Tensor = K.geometry.rescale(img, (float(height),float(width)),antialias=False)
+    img_rescale_aa: Tensor = K.geometry.rescale(img, (float(height),float(width)),antialias=True)
+    img_rescale = K.utils.tensor_to_image(img_rescale)
+    img_rescale_aa = K.utils.tensor_to_image(img_rescale_aa)
+    img_out = np.concatenate([img_rescale,img_rescale_aa],axis=1)
+    
+    # when antialiasing , some values are going greater than 1 i.e 1.00001 which is giving error while displaying the output image,so clipping the output values from 0 to 1
+    return np.clip(img_out ,0,1)
+
+
+examples = [
+    ["examples/a.png",1,1],
+    ["examples/iron_man.jpeg",1,1],
+]
+
+kornia_resizing_demo = gr.Interface(
+    rescale_aa,
+    [
+        gr.inputs.Image(type="file"),
+        gr.inputs.Slider(minimum=0.005, maximum=2, step=0.005, default=1, label="Height"),
+        gr.inputs.Slider(minimum=0.005, maximum=2, step=0.005, default=1, label="Width")
+    ],
+    "image",
+    examples=examples,
+    live=False,
+    enable_queue = True,
+    allow_flagging = "never"
+)
+
+kornia_resizing_demo.launch()