Switched to Radio for metric, added padding to graphs to prevent the label overlapping.

app.py

@@ -44,19 +44,18 @@ def ground_truth_plot(n_features):
 
     X = np.array(X)
     y = np.array(y)
+    
+    gt_plot, ax = plt.subplots()
 
-    # Plot the ground-truth labelling
-    gt_plot = plt.figure()
-    plt.axes([0, 0, 1, 1])
     for l, color, n in zip(range(n_clusters), colors, labels):
         lines = plt.plot(X[y == l].T, c=color, alpha=0.5)
         lines[0].set_label(n)
 
+    plt.subplots_adjust(top=0.8, bottom=0, left=0, right=1.0)
+    ax.set_title("Ground Truth", size=20, pad=1)
     plt.legend(loc="best")
-
-    plt.axis("tight")
     plt.axis("off")
-    plt.suptitle("Ground truth", size=20, y=1)
+
     return gt_plot, X, y
 
 def plot_cluster_waves(metric, X, y):
@@ -64,18 +63,21 @@ def plot_cluster_waves(metric, X, y):
         n_clusters=n_clusters, linkage="average", metric=metric
     )
     model.fit(X)
-    clust_plot = plt.figure()
-    plt.axes([0, 0, 1, 1])
+
+    clust_plot, ax = plt.subplots()
     for l, color in zip(np.arange(model.n_clusters), colors):
         plt.plot(X[model.labels_ == l].T, c=color, alpha=0.5)
+
+    plt.subplots_adjust(top=0.75, bottom=0, left=0, right=1.0)
+    ax.set_title("Agglomerative Clustering\n(metric=%s)" % metric, size=20, pad=1.0)
     plt.axis("tight")
     plt.axis("off")
-    plt.suptitle("AgglomerativeClustering(metric=%s)" % metric, size=20, y=1)
     return clust_plot
 
 def plot_distances(metric, X, y):
     avg_dist = np.zeros((n_clusters, n_clusters))
-    dist_plot = plt.figure()
+    dist_plot, ax = plt.subplots()
+   
     for i in range(n_clusters):
         for j in range(n_clusters):
             avg_dist[i, j] = pairwise_distances(
@@ -99,8 +101,9 @@ def plot_distances(metric, X, y):
     plt.xticks(range(n_clusters), labels, rotation=45)
     plt.yticks(range(n_clusters), labels)
     plt.colorbar()
-    plt.suptitle("Interclass %s distances" % metric, size=18, y=1)
-    plt.tight_layout()
+    plt.subplots_adjust(top=0.8)
+    ax.set_title("Interclass %s distances" % metric, size=20, pad=1.0)
+    plt.axis("off")
     return dist_plot
 
 def agg_cluster(n_feats, measure):
@@ -123,18 +126,23 @@ with gr.Blocks() as demo:
     with gr.Row():
         with gr.Column():
             n_feats = gr.Slider(10, 4000, 2000, label="Number of Features")
-            measure = gr.Dropdown(["cosine", "euclidean", "cityblock"], value="cosine")
-            btn = gr.Button(label="Run")
+            measure = gr.Radio(["cosine", "euclidean", "cityblock"], label="Metric", value="cosine")
         gt_graph = gr.Plot(label="Ground Truth Graph")
+        gt_graph.style()
     with gr.Row():
         dist_plot = gr.Plot(label="Interclass Distances")
         clust_waves = gr.Plot(label="Agglomerative Clustering")
 
-    btn.click(
-            fn=agg_cluster,
-            inputs=[n_feats, measure],
-            outputs=[gt_graph, clust_waves, dist_plot]
-            )
+    n_feats.change(
+                   fn=agg_cluster,
+                   inputs=[n_feats, measure],
+                   outputs=[gt_graph, clust_waves, dist_plot]
+                  )
+    measure.change(
+                   fn=agg_cluster,
+                   inputs=[n_feats, measure],
+                   outputs=[gt_graph, clust_waves, dist_plot]
+                  )
 
 if __name__ == '__main__':
     demo.launch()