Create app.py

app.py

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+import spaces
+import gradio as gr
+import torch
+import torchvision.transforms as T
+import numpy as np
+from pixeloe.pixelize import pixelize
+from PIL import Image
+
+def pixelize_image(
+    image,
+    downscale_mode="contrast",
+    target_size=128,
+    patch_size=16,
+    thickness=2,
+    color_matching=True,
+    upscale=True
+):
+    """
+    Apply pixelization effect to an image or batch of images.
+    """
+    # Convert PIL Image to numpy array
+    if isinstance(image, Image.Image):
+        image = np.array(image)
+    
+    # Process the image
+    processed = pixelize(
+        image,
+        mode=downscale_mode,
+        target_size=target_size,
+        patch_size=patch_size,
+        thickness=thickness,
+        contrast=1.0,
+        saturation=1.0,
+        color_matching=color_matching,
+        no_upscale=not upscale
+    )
+    
+    # Convert back to PIL Image
+    return Image.fromarray(processed)
+
+# Gradio interface function
+@spaces.GPU
+def process_image(image, downscale_mode, target_size, patch_size, thickness, color_matching, upscale):
+    if image is None:
+        return None
+    
+    result = pixelize_image(
+        image,
+        downscale_mode=downscale_mode,
+        target_size=target_size,
+        patch_size=patch_size,
+        thickness=thickness,
+        color_matching=color_matching,
+        upscale=upscale
+    )
+    
+    return result
+
+# Create Gradio interface
+with gr.Blocks(title="Image Pixelizer") as demo:
+    gr.Markdown("# Image Pixelizer")
+    gr.Markdown("Upload an image and adjust the parameters to create pixel art effects.")
+    
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(label="Input Image", type="pil")
+            
+            downscale_mode = gr.Dropdown(
+                choices=["contrast", "bicubic", "nearest", "center", "k-centroid"],
+                value="contrast",
+                label="Downscale Mode"
+            )
+            
+            target_size = gr.Slider(
+                minimum=8,
+                maximum=1024,
+                value=128,
+                step=8,
+                label="Target Size"
+            )
+            
+            patch_size = gr.Slider(
+                minimum=4,
+                maximum=32,
+                value=16,
+                step=2,
+                label="Patch Size"
+            )
+            
+            thickness = gr.Slider(
+                minimum=1,
+                maximum=16,
+                value=2,
+                step=1,
+                label="Thickness"
+            )
+            
+            color_matching = gr.Checkbox(
+                value=True,
+                label="Color Matching"
+            )
+            
+            upscale = gr.Checkbox(
+                value=True,
+                label="Upscale"
+            )
+            
+            process_btn = gr.Button("Process Image")
+        
+        with gr.Column():
+            output_image = gr.Image(label="Processed Image")
+    
+    # Set up processing event
+    process_btn.click(
+        fn=process_image,
+        inputs=[
+            input_image,
+            downscale_mode,
+            target_size,
+            patch_size,
+            thickness,
+            color_matching,
+            upscale
+        ],
+        outputs=output_image
+    )
+    
+    # Add example images
+    gr.Examples(
+        examples=[
+            ["example1.jpg", "contrast", 128, 16, 2, True, True],
+            ["example2.jpg", "bicubic", 256, 8, 1, True, True],
+        ],
+        inputs=[
+            input_image,
+            downscale_mode,
+            target_size,
+            patch_size,
+            thickness,
+            color_matching,
+            upscale
+        ],
+        outputs=output_image,
+        cache_examples=True,
+    )
+
+# Launch the interface
+if __name__ == "__main__":
+    demo.launch(share=True)