Update app.py

app.py

@@ -51,7 +51,6 @@ def getClusteringPlot(algorithm):
     plt.xticks(())
     plt.yticks(())
     title = "Spectral clustering: %s, %.2fs" % (algorithm, (t1 - t0))
-    print(title)
     plt.title(title)
     for l in range(n_regions):
         colors = [plt.cm.nipy_spectral((l + 4) / float(n_regions + 4))]
@@ -65,13 +64,17 @@ with gr.Blocks() as demo:
     gr.Markdown("## Segmenting the picture of Greek coins in regions 🪙")
     gr.Markdown("This demo is based on this [scikit-learn example](https://scikit-learn.org/stable/auto_examples/cluster/plot_coin_segmentation.html#sphx-glr-auto-examples-cluster-plot-coin-segmentation-py).")
     gr.Markdown("In this demo, we compare three strategies for performing segmentation-clustering and breaking the below image of Greek coins into multiple partly-homogeneous regions.")
-    gr.Image(coins(), label="An image of 24 Greek coins")
-    gr.Markdown("The image is retrieved from scikit-image's data [gallery](https://scikit-image.org/docs/stable/auto_examples/).")
+    
     inp = gr.Radio(["kmeans", "discretize", "cluster_qr"], label="Solver", info="Choose a clustering algorithm", value="kmeans")
     with gr.Row():
         plot = gr.Plot(label="Plot")
         num = gr.Textbox(label="Running Time")
     inp.change(getClusteringPlot, inputs=[inp], outputs=[plot, num])
     demo.load(getClusteringPlot, inputs=[inp], outputs=[plot, num])
-    
-demo.launch()
+
+    gr.HTML("<hr>")
+    gr.Image(coins(), label="An image of 24 Greek coins")
+    gr.Markdown("The image is retrieved from scikit-image's data [gallery](https://scikit-image.org/docs/stable/auto_examples/).")
+
+if __name__ == "__main__":
+    demo.launch()