Change to a more compact layout

app.py

@@ -70,8 +70,8 @@ def get_proba_plots(
     ax.set_xticks(ind + width)
     ax.set_xticklabels(
         [
-            f"{model_2}\nweight {model_1_weight}",
-            f"{model_1}\nweight {model_2_weight}",
+            f"{model_1}\nweight {model_1_weight}",
+            f"{model_2}\nweight {model_2_weight}",
             f"{model_3}\nweight {model_3_weight}",
             "VotingClassifier\n(average probabilities)",
         ],
@@ -88,46 +88,48 @@ def get_proba_plots(
 
 with gr.Blocks() as demo:
     with gr.Row():
-        model_1 = gr.Dropdown(
-            [
-                "Logistic Regression",
-                "Random Forest",
-                "Gaussian Naive Bayes",
-            ],
-            label="Model 1",
-            value="Logistic Regression",
-        )
-        model_2 = gr.Dropdown(
-            [
-                "Logistic Regression",
-                "Random Forest",
-                "Gaussian Naive Bayes",
-            ],
-            label="Model 2",
-            value="Random Forest",
-        )
-        model_3 = gr.Dropdown(
-            [
-                "Logistic Regression",
-                "Random Forest",
-                "Gaussian Naive Bayes",
-            ],
-            label="Model 3",
-            value="Gaussian Naive Bayes",
-        )
-
-    with gr.Row():
-        model_1_weight = gr.Slider(
-            minimum=1, maximum=10, value=1, label="Model 1 Weight", step=1
-        )
-        model_2_weight = gr.Slider(
-            minimum=1, maximum=10, value=1, label="Model 2 Weight", step=1
-        )
-        model_3_weight = gr.Slider(
-            minimum=1, maximum=10, value=5, label="Model 3 Weight", step=1
-        )
-
-    proba_plots = gr.Plot()
+        with gr.Column(scale=3):
+            gr.Markdown(
+                "Choose the type of model and the weight of each model in the final vote."  # noqa: E501
+                + " For example if you set weights to 1, 1, 5 for the three models,"
+                + " the third model will have 5 times more weight than the other two models."  # noqa: E501
+            )
+            with gr.Row():
+                model_1 = gr.Dropdown(
+                    [
+                        "Logistic Regression",
+                        "Random Forest",
+                        "Gaussian Naive Bayes",
+                    ],
+                    label="Model 1",
+                    value="Logistic Regression",
+                )
+                model_1_weight = gr.Number(value=1, label="Model 1 Weight", precision=0)
+            with gr.Row():
+                model_2 = gr.Dropdown(
+                    [
+                        "Logistic Regression",
+                        "Random Forest",
+                        "Gaussian Naive Bayes",
+                    ],
+                    label="Model 2",
+                    value="Random Forest",
+                )
+                model_2_weight = gr.Number(value=1, label="Model 2 Weight", precision=0)
+            with gr.Row():
+                model_3 = gr.Dropdown(
+                    [
+                        "Logistic Regression",
+                        "Random Forest",
+                        "Gaussian Naive Bayes",
+                    ],
+                    label="Model 3",
+                    value="Gaussian Naive Bayes",
+                )
+
+                model_3_weight = gr.Number(value=5, label="Model 3 Weight", precision=0)
+        with gr.Column(scale=4):
+            proba_plots = gr.Plot()
 
     model_1.change(
         get_proba_plots,