formatting

app.py

@@ -8,18 +8,17 @@ import matplotlib.cm as cm
 import numpy as np
 
 theme = gr.themes.Monochrome(
-    primary_hue="indigo",
-    secondary_hue="blue",
-    neutral_hue="slate",
+    primary_hue="indigo", secondary_hue="blue", neutral_hue="slate",
 )
 
+
 def main(
-    n_clusters:int=2,
-    n_samples:int=500,
-    n_features:int=2,
-    n_centers:int=4,
-    cluster_std:int=1,
-    ):
+    n_clusters: int = 2,
+    n_samples: int = 500,
+    n_features: int = 2,
+    n_centers: int = 4,
+    cluster_std: int = 1,
+):
     # Generating the sample data from make_blobs
     # This particular setting has one distinct cluster and 3 clusters placed close
     # together.
@@ -31,7 +30,7 @@ def main(
         center_box=(-10.0, 10.0),
         shuffle=True,
         random_state=1,
-    )  
+    )
     # For reproducibility
     n_clusters = int(n_clusters)
     fig1, ax1 = plt.subplots()
@@ -127,9 +126,11 @@ def main(
     ax2.set_title("The visualization of the clustered data.")
     ax2.set_xlabel("Feature space for the 1st feature")
     ax2.set_ylabel("Feature space for the 2nd feature")
+
     return fig1, fig2
 
-title = '''# Selecting the number of clusters with silhouette analysis on KMeans clustering 📊'''
+
+title = """# Selecting the number of clusters with silhouette analysis on KMeans clustering 📊"""
 description = """
             This app demonstrates a silhouette analysis for KMeans clustering on sample data.
             
@@ -139,21 +140,35 @@ description = """
 with gr.Blocks(theme=theme) as demo:
     gr.Markdown(title)
     gr.Markdown(description)
-    gr.Markdown('''### Dataset Generation Parameters''')
+    gr.Markdown("""### Dataset Generation Parameters""")
     with gr.Row():
         with gr.Column():
-            n_samples = gr.inputs.Slider(minimum=100, maximum=1000, default=500, step=50, label="n_samples")
-            n_features = gr.inputs.Slider(minimum=2, maximum=5, default=2, step=1, label="n_features")
-            n_clusters = gr.inputs.Slider(minimum=2, maximum=6, default=2, step=1, label="n_clusters")
-            n_centers = gr.inputs.Slider(minimum=2, maximum=5, default=4, step=1, label="n_centers")
-            cluster_std = gr.inputs.Slider(minimum=0.0, maximum=1.0, default=1, step=0.1, label="cluster_std")
-            run_button = gr.Button('Analyse Silhouette')
+            n_samples = gr.inputs.Slider(
+                minimum=100,
+                maximum=1000,
+                default=500,
+                step=50,
+                label="Number of Samples",
+            )
+            n_features = gr.inputs.Slider(
+                minimum=2, maximum=5, default=2, step=1, label="Number of Features"
+            )
+            n_centers = gr.inputs.Slider(
+                minimum=2, maximum=5, default=4, step=1, label="Number of Centers"
+            )
+            cluster_std = gr.inputs.Slider(
+                minimum=0.0, maximum=1.0, default=1, step=0.1, label="Cluster deviation"
+            )
+            n_clusters = gr.inputs.Slider(
+                minimum=2, maximum=6, default=2, step=1, label="Number of Clusters"
+            )
+            run_button = gr.Button("Analyse Silhouette")
     with gr.Row():
         plot_silhouette = gr.Plot()
         plot_clusters = gr.Plot()
     outputs = [plot_silhouette, plot_clusters]
-    inputs = [n_clusters, n_samples,n_features,n_centers,cluster_std]
+    inputs = [n_clusters, n_samples, n_features, n_centers, cluster_std]
     run_button.click(fn=main, inputs=inputs, outputs=outputs)
 
-    
-demo.launch()
+
+demo.launch()