Suggested modifications

app.py

@@ -80,6 +80,8 @@ with gr.Blocks(title=title) as demo:
         on a synthetic dataset. The transformations are then used to train a linear model on the \
         transformed data. The plot shows the ROC curve of the different models trained on the \
         transformed data. The plot is interactive and you can zoom in and out.
+        
+        [Original Example](https://scikit-learn.org/stable/auto_examples/ensemble/plot_feature_transformation.html#sphx-glr-auto-examples-ensemble-plot-feature-transformation-py)
         """
     )
     
@@ -88,10 +90,10 @@ with gr.Blocks(title=title) as demo:
             n_samples = gr.inputs.Slider(50_000, 100_000, 1000, label="Number of Samples", default=80_000)
             n_estimators = gr.inputs.Slider(10, 100, 10, label="Number of Estimators", default=10)
             max_depth = gr.inputs.Slider(1, 10, 1, label="Max Depth", default=3)
+            btn = gr.Button("Run")
             plot = gr.Plot(label="ROC Curve")
 
-    Reduction = gr.Button("Run")
-    Reduction.click(fn=app_fn, inputs=[n_samples, n_estimators, max_depth], outputs=[plot])
+    btn.click(fn=app_fn, inputs=[n_samples, n_estimators, max_depth], outputs=[plot])
     demo.load(fn=app_fn, inputs=[n_samples, n_estimators, max_depth], outputs=[plot])
 
 demo.launch()