Suggested modifications

app.py

@@ -81,11 +81,14 @@ with gr.Blocks() as demo:
         Then it averages the individual predictions to form a final prediction. This example will use three different regressors to \
         predict the data: GradientBoostingRegressor, RandomForestRegressor, and LinearRegression. Then the 3 regressors will be used for the VotingRegressor. \
         The dataset used consists of 10 features collected from a cohort of diabetes patients. The target is a quantitative measure of disease progression one year after baseline.
+
+        [Original example](https://scikit-learn.org/stable/auto_examples/ensemble/plot_voting_regressor.html#sphx-glr-auto-examples-ensemble-plot-voting-regressor-py)
         """
     )
     n = gr.inputs.Slider(10, 30, 5, 20, "Number of training samples")
-    plot = gr.Plot(label="Individual & Voting Predictions")
     button = gr.Button(label="Update Plot")
+    plot = gr.Plot(label="Individual & Voting Predictions")
+    
     button.click(fn=app_fn, inputs=[n], outputs=[plot])
     demo.load(fn=app_fn, inputs=[n], outputs=[plot])