try tab approach

app.py

@@ -6,35 +6,23 @@ import kornia as K
 from kornia.core import Tensor
 
 
-def filters_without(file, blur_pool2d, box_blur, gaussian_blur2d, max_blur_pool2d, median_blur):      
+def box_blur_fn(file, box_blur):      
     # load the image using the rust backend          
     img: Tensor = K.io.load_image(file.name, K.io.ImageLoadType.RGB32)
     img = img[None]  # 1xCxHxW / fp32 / [0, 1]
 
-    # apply tensor image enhancement
     x_out: Tensor = K.filters.box_blur(img, (int(box_blur), int(box_blur)))
-    x_out = K.filters.gaussian_blur2d(x_out, 
-                                      (int(gaussian_blur2d), int(gaussian_blur2d)), 
-                                      (float(gaussian_blur2d), float(gaussian_blur2d)))
-    x_out = K.filters.max_blur_pool2d(x_out, int(max_blur_pool2d))
-    x_out = K.filters.median_blur(x_out, (int(median_blur), int(median_blur)))
-
+    
     return K.utils.tensor_to_image(x_out)
 
-def filters_with(file, blur_pool2d, box_blur, gaussian_blur2d, max_blur_pool2d, median_blur):      
+def blur_pool2d_fn(file, blur_pool2d):      
     # load the image using the rust backend          
     img: Tensor = K.io.load_image(file.name, K.io.ImageLoadType.RGB32)
     img = img[None]  # 1xCxHxW / fp32 / [0, 1]
 
     # apply tensor image enhancement
     x_out: Tensor = K.filters.blur_pool2d(x_out, int(blur_pool2d))
-    x_out = K.filters.box_blur(img, (int(box_blur), int(box_blur)))
-    x_out = K.filters.gaussian_blur2d(x_out, 
-                                      (int(gaussian_blur2d), int(gaussian_blur2d)), 
-                                      (float(gaussian_blur2d), float(gaussian_blur2d)))
-    x_out = K.filters.max_blur_pool2d(x_out, int(max_blur_pool2d))
-    x_out = K.filters.median_blur(x_out, (int(median_blur), int(median_blur)))
-
+    
     return K.utils.tensor_to_image(x_out)
 
 
@@ -43,49 +31,42 @@ examples = [
     ["examples/pikachu.jpg", 1, 1, 1, 1, 1],
 ]
 
-without_downsampling_demo = gr.Interface(
-    filters_without,
+box_blur_fn_demo = gr.Interface(
+    box_blur_fn,
     [
         gr.inputs.Image(type="file"),
         gr.inputs.Slider(minimum=1, maximum=10, step=1, default=1, label="Box Blur"),
-        gr.inputs.Slider(minimum=1, maximum=21, step=2, default=1, label="Gaussian Blur"),
-        gr.inputs.Slider(minimum=1, maximum=20, step=1, default=1, label="Max Pool"),
-        gr.inputs.Slider(minimum=1, maximum=5, step=2, default=1, label="Median Blur"),
     ],
     "image",
     examples=examples,
     # title=title,
-    description= 'If you want to use the filters with downsampled image, use tab "With image downsampling"',
+    # description=description,
     # article=article,
     live=True
 )
 
-with_downsampling_demo = gr.Interface(
-    filters_with,
+blur_pool2d_fn_demo = gr.Interface(
+    blur_pool2d_fn,
     [
         gr.inputs.Image(type="file"),
         gr.inputs.Slider(minimum=1, maximum=10, step=1, default=1, label="Blur Pool"),
-        gr.inputs.Slider(minimum=1, maximum=10, step=1, default=1, label="Box Blur"),
-        gr.inputs.Slider(minimum=1, maximum=21, step=2, default=1, label="Gaussian Blur"),
-        gr.inputs.Slider(minimum=1, maximum=20, step=1, default=1, label="Max Pool"),
-        gr.inputs.Slider(minimum=1, maximum=5, step=2, default=1, label="Median Blur"),
     ],
     "image",
     examples=examples,
     # title=title,
-    description = 'Blur Pooling downsamples the image in the default setting!',
+    # description=description,
     # article=article,
     live=True
 )
 
 demo = gr.TabbedInterface(
     [
-      without_downsampling_demo,
-      with_downsampling_demo
+      box_blur_fn_demo,
+      blur_pool2d_fn_demo
     ], 
     [
-      "Without image downsampling", 
-      "With image downsampling"
+      "Box Blur", 
+      "Blur Pool"
     ]
 )