Created the demo

app.py

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+import numpy as np
+
+from sklearn.cluster import AffinityPropagation
+from sklearn import metrics
+from sklearn.datasets import make_blobs
+
+import matplotlib.pyplot as plt
+import matplotlib
+matplotlib.use('agg')
+
+import gradio as gr
+
+def generate_data(num_centers, num_samples):
+    all_centers = [[1, 1], [-1, -1], [1, -1], [-1, 1]]
+    centers = all_centers[:num_centers]
+    
+    X, labels_true = make_blobs(n_samples=num_samples, centers=centers, cluster_std=0.5, random_state=0)
+
+    return X, labels_true
+
+
+def create_plot(num_clusters, num_samples):
+    X, labels_true = generate_data(num_clusters, num_samples)
+
+    af = AffinityPropagation(preference=-50, random_state=0).fit(X)
+    cluster_centers_indices = af.cluster_centers_indices_
+    labels = af.labels_
+
+    n_clusters_ = len(cluster_centers_indices)
+
+    metrics_str = f"Estimated number of clusters: {n_clusters_}\n" 
+    metrics_str += f"Homogeneity: {metrics.homogeneity_score(labels_true, labels):0.3f}\n"
+    metrics_str += f"Completeness: {metrics.completeness_score(labels_true, labels):0.3f}\n"
+    metrics_str += f"V-measure: {metrics.v_measure_score(labels_true, labels):0.3f}\n" 
+    metrics_str += f"Adjusted Rand Index: {metrics.adjusted_rand_score(labels_true, labels):0.3f}\n" 
+    metrics_str += f"Adjusted Mutual Information: {metrics.adjusted_mutual_info_score(labels_true, labels):0.3f}\n"
+    metrics_str += f"Silhouette Coefficient: {metrics.silhouette_score(X, labels, metric='sqeuclidean'):0.3f}\n"
+    
+    fig = plt.figure(1)
+    plt.clf()
+
+    colors = plt.cycler("color", plt.cm.viridis(np.linspace(0, 1, n_clusters_)))
+
+    for k, col in zip(range(n_clusters_), colors):
+        class_members = labels == k
+        cluster_center = X[cluster_centers_indices[k]]
+        plt.scatter(
+            X[class_members, 0], X[class_members, 1], color=col["color"], marker="."
+        )
+        plt.scatter(
+            cluster_center[0], cluster_center[1], s=14, color=col["color"], marker="o"
+        )
+        for x in X[class_members]:
+            plt.plot(
+                [cluster_center[0], x[0]], [cluster_center[1], x[1]], color=col["color"]
+            )
+
+    plt.title("Estimated number of clusters: %d" % n_clusters_)
+
+    return fig, metrics_str 
+
+title = "Affinity propagation clustering algorithm"
+description = "This demo plots clusters of a synthetic 2D dataset that contains up to 4 clusters using the affinity propagation algorithm."
+with gr.Blocks() as demo:
+    gr.Markdown(f"## {title}")
+    gr.Markdown(description)
+
+    num_clusters = gr.Slider(minimum=2, maximum=4, step=1, value=2, label="Number of clusters")
+    num_samples = gr.Slider(minimum=100, maximum=300, step=100, value=200, label="Number of samples")
+
+    with gr.Row():
+        plot = gr.Plot()
+        text_box = gr.Textbox(label="Results")
+
+
+    num_clusters.change(fn=create_plot, inputs=[num_clusters, num_samples], outputs=[plot, text_box])
+    num_samples.change(fn=create_plot, inputs=[num_clusters, num_samples], outputs=[plot, text_box])
+
+demo.launch(enable_queue=True)

requirements.txt

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+scikit-learn
+matplotlib
+numpy
+