Update app.py

app.py

@@ -44,16 +44,16 @@ def app_fn(k: int, n_features: int, n_informative: int, n_redundant: int):
     )
     return report_df, fig
 
-title = "🔥 Pipeline ANOVA SVM 🔥"
+title = "Pipeline ANOVA SVM"
 with gr.Blocks() as demo:
     gr.Markdown(f"# {title}")
     gr.Markdown(
         """
-        ## This example shows how a feature selection can be easily integrated within a machine learning pipeline \
+        This example shows how a feature selection can be easily integrated within a machine learning pipeline \
         using a synthetic dataset. The number of features to select and other parameters to generate the toy dataset \
         are provided as components to play around.
 
-        [Original Example](https://scikit-learn.org/stable/auto_examples/feature_selection/plot_feature_selection_pipeline.html#sphx-glr-auto-examples-feature-selection-plot-feature-selection-pipeline-py)
+        See original example [here](https://scikit-learn.org/stable/auto_examples/feature_selection/plot_feature_selection_pipeline.html#sphx-glr-auto-examples-feature-selection-plot-feature-selection-pipeline-py)
         """
     )
     with gr.Row():