Simplify plotting in a grid

app.py

@@ -234,6 +234,16 @@ description = (
     "Colored cirles are (predicted) labels and black x are outliers."
 )
 
+
+def iter_grid(n_rows, n_cols):
+    # create a grid using gradio Block
+    for _ in range(n_rows):
+        with gr.Row():
+            for _ in range(n_cols):
+                with gr.Column():
+                    yield
+
+
 with gr.Blocks(title=title) as demo:
     gr.HTML(f"<b>{title}</b>")
     gr.Markdown(description)
@@ -253,20 +263,16 @@ with gr.Blocks(title=title) as demo:
     )
     n_rows = int(math.ceil(len(input_models) / N_COLS))
     counter = 0
-    # code below is not very elegant, maybe there is a better way?
-    for i in range(n_rows):
-        with gr.Row():
-            for j in range(N_COLS):
-                with gr.Column():
-                    if counter >= len(input_models):
-                        break
-
-                    input_model = input_models[counter]
-                    plot = gr.Plot(label=input_model)
-                    fn = partial(cluster, clustering_algorithm=input_model)
-                    input_data.change(fn=fn, inputs=[input_data, input_n_clusters], outputs=plot)
-                    input_n_clusters.change(fn=fn, inputs=[input_data, input_n_clusters], outputs=plot)
-                    counter += 1
+    for _ in iter_grid(n_rows, N_COLS):
+        if counter >= len(input_models):
+            break
+
+        input_model = input_models[counter]
+        plot = gr.Plot(label=input_model)
+        fn = partial(cluster, clustering_algorithm=input_model)
+        input_data.change(fn=fn, inputs=[input_data, input_n_clusters], outputs=plot)
+        input_n_clusters.change(fn=fn, inputs=[input_data, input_n_clusters], outputs=plot)
+        counter += 1
 
 
 demo.launch()