Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from sklearn.cluster import KMeans
+from PIL import Image
+
+def flatten_colors(input_img, num_colors):
+    if input_img is None:
+        return None
+    
+    # Convert PIL image to numpy array
+    img_array = np.array(input_img)
+    original_shape = img_array.shape
+    
+    # Reshape to a list of pixels (R, G, B)
+    pixels = img_array.reshape(-1, 3)
+    
+    # Apply K-Means to find dominant colors
+    kmeans = KMeans(n_clusters=int(num_colors), random_state=42, n_init=10)
+    labels = kmeans.fit_predict(pixels)
+    colors = kmeans.cluster_centers_.astype('uint8')
+    
+    # Map each pixel to its corresponding cluster center (the flat color)
+    flattened_pixels = colors[labels]
+    
+    # Reshape back to original image dimensions
+    flattened_img = flattened_pixels.reshape(original_shape)
+    
+    return Image.fromarray(flattened_img)
+
+# Define the Gradio Interface
+demo = gr.Interface(
+    fn=flatten_colors,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Slider(minimum=2, maximum=20, value=8, step=1, label="Number of Colors")
+    ],
+    outputs=gr.Image(type="pil", label="Flat Color Result"),
+    title="🎨 Image Color Flattener",
+    description="Upload an image and reduce it to a flat color palette using K-Means clustering."
+)
+
+if __name__ == "__main__":
+    demo.launch()