fix: update code to use latest versions

app.py

@@ -1,26 +1,46 @@
 # created with great guidance from https://github.com/NimaBoscarino
 
+import os
 import gradio as gr
 
 import kornia as K
 from kornia.core import Tensor
+import torch
 
 
-def filters(file, box_blur, blur_pool2d, gaussian_blur2d, max_blur_pool2d, median_blur):      
-    # load the image using the rust backend          
-    img: Tensor = K.io.load_image(file.name, K.io.ImageLoadType.RGB32)
+def filters(file, box_blur, blur_pool2d, gaussian_blur2d, max_blur_pool2d, median_blur):
+    """
+    Apply a series of image filters to the input image.
+    """
+    # Handle filepath string and file object
+    if isinstance(file, str):
+        filepath = file
+    else:
+        filepath = file.name
+
+    # Check if file exists
+    if not os.path.exists(filepath):
+        raise ValueError(f"File not found: {filepath}")
+    # load the image using the rust backend
+    img: Tensor = K.io.load_image(filepath, 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.blur_pool2d(x_out, int(blur_pool2d))
-    x_out = K.filters.gaussian_blur2d(x_out, 
-                                      (int(gaussian_blur2d), int(gaussian_blur2d)), 
-                                      (float(gaussian_blur2d), float(gaussian_blur2d)))
+    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)
+    # Ensure output is in the range [0, 1]
+    x_out = torch.clamp(x_out, 0, 1)
+
+    # Convert to numpy array and scale to [0, 255]
+    output_image = (x_out.squeeze().permute(1, 2, 0).cpu().numpy() * 255).astype('uint8')
+
+    return output_image
 
 
 examples = [
@@ -28,26 +48,19 @@ examples = [
     ["examples/kitty.jpg", 1, 1, 1, 1, 1],
 ]
 
-title = "Kornia Image Filters"
-description = "<p style='text-align: center'>This is a Gradio demo for Kornia's Image Filters.</p><p style='text-align: center'>To use it, simply upload your image, or click one of the examples to load them, and use the sliders to enhance! Read more at the links at the bottom.</p>"
-article = "<p style='text-align: center'><a href='https://kornia.readthedocs.io/en/latest/' target='_blank'>Kornia Docs</a> | <a href='https://github.com/kornia/kornia' target='_blank'>Kornia Github Repo</a> | <a href='https://kornia-tutorials.readthedocs.io/en/latest/image_enhancement.html' target='_blank'>Kornia Enhancements Tutorial</a></p>"
-
-iface = gr.Interface(
-    filters,
-    [
-        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=10, step=1, default=1, label="Blur Pool"),
-        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"),
+demo = gr.Interface(
+    fn=filters,
+    inputs=[
+        gr.Image(type="filepath", label="Input Image"),
+        gr.Slider(minimum=1, maximum=10, step=1, value=1, label="Box Blur"),
+        gr.Slider(minimum=1, maximum=10, step=1, value=1, label="Blur Pool"),
+        gr.Slider(minimum=1, maximum=21, step=2, value=1, label="Gaussian Blur"),
+        gr.Slider(minimum=1, maximum=20, step=1, value=1, label="Max Pool"),
+        gr.Slider(minimum=1, maximum=5, step=2, value=1, label="Median Blur"),
     ],
-    "image",
+    outputs=gr.Image(type="numpy", label="Output Image"),
     examples=examples,
-    # title=title,
-    # description=description,
-    # article=article,
     live=True
 )
 
-iface.launch()
+demo.launch()