Update app.py

app.py

@@ -83,18 +83,45 @@ def quant_evaluation(n_runs):
     plt.ylabel("inertia")
     plt.legend(plots, legends)
     plt.title("Mean inertia for various k-means init across %d runs" % n_runs)
-    return plt 
+    return plt
+
+def qual_evaluation():
+    X, y = make_data(random_state, n_samples_per_center, grid_size, scale)
+    km = MiniBatchKMeans(
+    n_clusters=n_clusters, init="random", n_init=1, random_state=random_state
+    ).fit(X)
+
+    plt.figure()
+    for k in range(n_clusters):
+        my_members = km.labels_ == k
+        color = cm.nipy_spectral(float(k) / n_clusters, 1)
+        plt.plot(X[my_members, 0], X[my_members, 1], ".", c=color)
+        cluster_center = km.cluster_centers_[k]
+        plt.plot(
+            cluster_center[0],
+            cluster_center[1],
+            "o",
+            markerfacecolor=color,
+            markeredgecolor="k",
+            markersize=6,
+        )
+        plt.title(
+            "Example cluster allocation with a single random init\nwith MiniBatchKMeans"
+        )
+    return plt
 
 with gr.Blocks(theme=theme) as demo:
     gr.Markdown('''
-            <div>
             <h1 style='text-align: center'>Empirical evaluation of the impact of k-means initialization 📊</h1>
-            </div>
         ''')
     gr.Markdown(description)
     n_runs = gr.Slider(minimum=1, maximum=10, step=1, value=5, label="Number of Evaluation Runs")
     run_button = gr.Button('Evaluate')
-    plot_inertia = gr.Plot()
+    run_button_qual = gr.Button('Generate Cluster Allocations')
+    with gr.Row():
+        plot_inertia = gr.Plot()
+        plot_vis = gr.Plot()
     run_button.click(fn=quant_evaluation, inputs=[n_runs], outputs=plot_inertia)
+    run_button_qual.click(fn=qual_evaluation, inputs=[], outputs=plot_vis)
 
 demo.launch()