Added: switched matplotlib backend

app.py

@@ -6,6 +6,7 @@ from sklearn.neighbors import kneighbors_graph
 
 import gradio as gr
 
+plt.switch_backend("agg")
 
 np.random.seed(42)
 
@@ -57,16 +58,5 @@ with gr.Blocks() as demo:
     plot_btn.click(fn=agglomorative_cluster, inputs=[n_samples, n_neighbours, n_clusters, linkage, connectivity],
                    outputs=output, api_name="plotcluster")
 
-    # demo = gr.Interface(
-    #     fn = agglomorative_cluster,
-    #     inputs = [gr.Slider(0, 20_000, label="n_samples", info="the number of samples in the data.", step=1), 
-    #             gr.Slider(0, 30, label="n_neighbours", info="the number of neighbours in the data", step=1), 
-    #             gr.Dropdown([3, 30], label="n_clusters", info="the number of clusters in the data"), 
-    #             gr.Dropdown(['average', 'complete', 'ward', 'single'], label="linkage", info="the different types of aggolomorative clustering techniques"),
-    #             gr.Checkbox(True, label="connectivity", info="whether to impose a connectivity into the graph")],
-        
-    #     outputs = [gr.Plot(label="Plot")]
-    # )
-
 demo.launch()