Update app.py

app.py

@@ -55,19 +55,19 @@ def getClusteringPlot(algorithm):
         colors = [plt.cm.nipy_spectral((l + 4) / float(n_regions + 4))]
         plt.contour(labels == l, colors=colors)
         # To view individual segments as appear comment in plt.pause(0.5)
-    return plt
+    return (plt, "%.3fs" % (t1 - t0))
 
 with gr.Blocks() as demo:
-    gr.Markdown(
-    """
-    # Segmenting the picture of greek coins in regions 🪙
-    An application of spectral clustering.
-    ![Image of coins](coins.png "a title")
-
-    """)
+    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")
-    plot = gr.Plot(label="Plot")
-    inp.change(getClusteringPlot, inputs=inp, outputs=[plot])
-    demo.load(getClusteringPlot, inputs=[inp], outputs=[plot])
+    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()