Add better description of the space

app.py

@@ -87,13 +87,29 @@ def get_proba_plots(
 
 
 with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        # Class probabilities by the `VotingClassifier`
+
+        This space shows the effect of the weight of different classifiers when using sklearn's [VotingClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier).
+
+        For example, suppose you set the weights as in the table below, and the models have the following predicted probabilities:
+        
+        |         | Weights | Predicted Probabilities |
+        |---------|:-------:|:----------------:|
+        | Model 1 |    1    |        0.5       |
+        | Model 2 |    2    |        0.8       |
+        | Model 3 |    5    |        0.9       |
+
+        The predicted probability by the `VotingClassifier` will be $(1*0.5 + 2*0.8 + 5*0.9) / (1 + 2 + 5)$
+
+        You can experiment with different model types and weights and see their effect on the VotingClassifier's prediction.
+
+        This space is based on [sklearn’s original demo](https://scikit-learn.org/stable/auto_examples/ensemble/plot_voting_probas.html#sphx-glr-auto-examples-ensemble-plot-voting-probas-py).
+        """
+    )
     with gr.Row():
         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(
                     [
@@ -104,7 +120,9 @@ with gr.Blocks() as demo:
                     label="Model 1",
                     value="Logistic Regression",
                 )
-                model_1_weight = gr.Number(value=1, label="Model 1 Weight", precision=0)
+                model_1_weight = gr.Slider(
+                    value=1, label="Model 1 Weight", max=10, step=1
+                )
             with gr.Row():
                 model_2 = gr.Dropdown(
                     [
@@ -115,7 +133,9 @@ with gr.Blocks() as demo:
                     label="Model 2",
                     value="Random Forest",
                 )
-                model_2_weight = gr.Number(value=1, label="Model 2 Weight", precision=0)
+                model_2_weight = gr.Slider(
+                    value=1, label="Model 2 Weight", max=10, step=1
+                )
             with gr.Row():
                 model_3 = gr.Dropdown(
                     [
@@ -127,7 +147,9 @@ with gr.Blocks() as demo:
                     value="Gaussian Naive Bayes",
                 )
 
-                model_3_weight = gr.Number(value=5, label="Model 3 Weight", precision=0)
+                model_3_weight = gr.Slider(
+                    value=5, label="Model 3 Weight", max=10, step=1
+                )
         with gr.Column(scale=4):
             proba_plots = gr.Plot()