Modified::added Gradio Blocks with Space description

app.py

@@ -33,18 +33,40 @@ def agglomorative_cluster(n_samples: int, n_neighbours: int, n_clusters: int, li
 
 
 
-
-
-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")]
-)
+with gr.Blocks() as demo:
+    gr.Markdown(""" 
+    # Agglomorative Clustering with and without Structure
+
+    This space is an implementation of the scikit-learn document [Agglomorative clustering with and without structure](https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_clustering.html#sphx-glr-auto-examples-cluster-plot-agglomerative-clustering-py)
+    This space shows the effects of imposing **connectivity graph** to capture local structure in the data. 
+    You can uncheck the checkbox `connectivity` to see the effect on data clustering without **connectivity graph**. There are other parameters in this space 
+    which you can play with such as `n_samples` (the number of data samples), `n_neighbours` (the number of neighbours), `n_clusters` (the number of clusters) and
+    what type of linkage to use for Agglomorative clustering `linkage`. 
+
+    Have fun playing with the tool 🤗
+    """)
+
+    n_samples = gr.Slider(0, 20_000, label="n_samples", info="the number of samples in the data.", step=1)
+    n_neighbours = gr.Slider(0, 30, label="n_neighbours", info="the number of neighbours in the data", step=1)
+    n_clusters = gr.Slider(3, 30, label="n_clusters", info="the number of clusters in the data", step=2)
+    linkage = gr.Dropdown(['average', 'complete', 'ward', 'single'], label="linkage", info="the different types of aggolomorative clustering techniques")
+    connectivity = gr.Checkbox(True, label="connectivity", info="whether to impose a connectivity into the graph")
+    output = gr.Plot(label="Plot") 
+
+    plot_btn = gr.Button("Plot")
+    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()