Better layout

app.py

@@ -48,11 +48,12 @@ def interpret(*args):
     shap_values = explainer.shap_values(xgb.DMatrix(df, enable_categorical=True))
     scores_desc = list(zip(shap_values[0], X_train.columns))
     scores_desc = sorted(scores_desc)
-    fig_m = plt.figure()
+    fig_m = plt.figure(tight_layout=True)
     plt.barh([s[1] for s in scores_desc], [s[0] for s in scores_desc])
     plt.title("Feature Shap Values")
     plt.ylabel("Shap Value")
     plt.xlabel("Feature")
+    plt.tight_layout()
     return fig_m
 
 
@@ -124,44 +125,44 @@ with gr.Blocks() as demo:
         with gr.Column():
             label = gr.Label()
             plot = gr.Plot()
-    with gr.Row():
-        predict_btn = gr.Button(value="Predict")
-        interpret_btn = gr.Button(value="Interpret")
-        predict_btn.click(
-            predict,
-            inputs=[
-                age,
-                work_class,
-                education,
-                years,
-                marital_status,
-                occupation,
-                relationship,
-                sex,
-                capital_gain,
-                capital_loss,
-                hours_per_week,
-                country,
-            ],
-            outputs=[label],
-        )
-        interpret_btn.click(
-            interpret,
-            inputs=[
-                age,
-                work_class,
-                education,
-                years,
-                marital_status,
-                occupation,
-                relationship,
-                sex,
-                capital_gain,
-                capital_loss,
-                hours_per_week,
-                country,
-            ],
-            outputs=[plot],
-        )
+            with gr.Row():
+                predict_btn = gr.Button(value="Predict")
+                interpret_btn = gr.Button(value="Interpret")
+            predict_btn.click(
+                predict,
+                inputs=[
+                    age,
+                    work_class,
+                    education,
+                    years,
+                    marital_status,
+                    occupation,
+                    relationship,
+                    sex,
+                    capital_gain,
+                    capital_loss,
+                    hours_per_week,
+                    country,
+                ],
+                outputs=[label],
+            )
+            interpret_btn.click(
+                interpret,
+                inputs=[
+                    age,
+                    work_class,
+                    education,
+                    years,
+                    marital_status,
+                    occupation,
+                    relationship,
+                    sex,
+                    capital_gain,
+                    capital_loss,
+                    hours_per_week,
+                    country,
+                ],
+                outputs=[plot],
+            )
 
 demo.launch()