Update app.py

app.py

@@ -10,6 +10,17 @@ theme = gr.themes.Monochrome(
     secondary_hue="blue",
     neutral_hue="slate",
 )
+model_card = f"""
+## Description
+
+The **DecisionTreeClassifier** employs a pruning technique that can be configured using the cost complexity parameter, commonly referred to as **ccp_alpha**.
+By increasing the value of ccp_alpha, a greater number of nodes can be pruned. This demo demonstrates the impact of ccp_alpha on tree regularization
+
+## Dataset
+
+Breast Cancer
+
+"""
 
 X, y = load_breast_cancer(return_X_y=True)
 
@@ -77,8 +88,7 @@ with gr.Blocks(theme=theme) as demo:
             <h1 style='text-align: center'>⚒ Post pruning decision trees with cost complexity pruning 🛠</h1>
             </div>
         ''')
-    gr.Markdown("The DecisionTreeClassifier employs a pruning technique that can be configured using the cost complexity parameter, commonly referred to as ccp_alpha."
-    " By increasing the value of ccp_alpha, a greater number of nodes can be pruned. This demo demonstrates the impact of ccp_alpha on tree regularization\n Dataset: Breast Cancer")
+    gr.Markdown(model_card)
     gr.Markdown("Author: <a href=\"https://huggingface.co/vumichien\">Vu Minh Chien</a>. Based on the example from <a href=\"https://scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html#sphx-glr-auto-examples-tree-plot-cost-complexity-pruning-py\">scikit-learn</a>")
     test_size = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.2, label="Test size")
     random_state = gr.Slider(minimum=0, maximum=2000, step=1, value=0, label="Random state")