Created gradio implementation of the example.

app.py

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+# Gradio Implementation: Lenix Carter
+# License: BSD 3-Clause or CC-0
+
+import gradio as gr
+import matplotlib.pyplot as plt
+
+from sklearn.cluster import kmeans_plusplus
+from sklearn.datasets import make_blobs
+
+plt.switch_backend("agg")
+
+def initial_points(n_samples, n_components, clst_std, n_clust):
+    plt.clf()
+    # Generate sample data
+
+    X, y_true = make_blobs(
+        n_samples=n_samples, centers=n_components, cluster_std=clst_std, random_state=0
+    )
+    X = X[:, ::-1]
+
+    # Calculate seeds from k-means++
+    centers_init, indices = kmeans_plusplus(X, n_clusters=n_clust, random_state=0)
+
+    # Plot init seeds along side sample data
+    plt.figure(1)
+
+    for k in range(n_components):
+        cluster_data = y_true == k
+        plt.scatter(X[cluster_data, 0], X[cluster_data, 1], marker=".", s=10)
+
+    plt.scatter(centers_init[:, 0], centers_init[:, 1], c="b", s=50)
+    plt.title("K-Means++ Initialization")
+    plt.xticks([])
+    plt.yticks([])
+    return plt
+
+title = "An example of K-Means++ Initialization"
+with gr.Blocks() as demo:
+    gr.Markdown(f" # {title}")
+    gr.Markdown("""
+                This example shows the ouput of the K-Means++ function. 
+
+                This is based on the example [here](https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_plusplus.html#sphx-glr-auto-examples-cluster-plot-kmeans-plusplus-py).
+                """)
+    with gr.Row():
+        with gr.Column():
+            n_samples = gr.Slider(100, 4000, 1000, label="Number of Samples")
+            n_components = gr.Slider(1, 10, 4, step=1, label="Number of blobs")
+            clst_std = gr.Slider(.1, 1, .6, label="Blob Standard Deviation")
+            n_clusters = gr.Slider(1, 10, 4, step=1, label="Number of Clusters to Initialize")
+            btn = gr.Button(label="Run")
+        with gr.Column():
+            graph_points = gr.Plot(label="K-Means++ Initial Points")
+    btn.click(
+            fn=initial_points,
+            inputs=[n_samples, n_components, clst_std, n_clusters],
+            outputs=[graph_points]
+            )
+
+if __name__ == '__main__':
+    demo.launch()
+

requirements.txt

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+matplotlib==3.6.3
+scikit-learn==1.2.2