Adjust label

app.py

@@ -216,9 +216,10 @@ with gr.Blocks(title=title) as demo:
     " The number of samples (n_samples) will determine the number of data points to produce.  <br>"
     " The number of components (n_components) will determine the number of components each method will fit to, and will affect the likelihood of the held-out set.  <br>"
     " The number of features (n_components) determine the number of features the toy dataset X variable will have.  <br>"
-    " Play with the n_components parameter to see.<br>")
+    " For further details please see the sklearn docs:"    
+    )
 
-    gr.Markdown(" **[Demo is based on sklearn docs](https://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_fa_model_selection.html#sphx-glr-auto-examples-decomposition-plot-pca-vs-fa-model-selection-py)** <br>")
+    gr.Markdown(" **[Demo is based on sklearn docs found here](https://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_fa_model_selection.html#sphx-glr-auto-examples-decomposition-plot-pca-vs-fa-model-selection-py)** <br>")
 
     gr.Markdown(" **Dataset** : A toy dataset with corrupted with homoscedastic noise (noise variance is the same for each feature) or heteroscedastic noise (noise variance is the different for each feature) . <br>")
     gr.Markdown(" Different number of features and number of components affect how well the low rank space is recovered. <br>"
@@ -233,7 +234,7 @@ with gr.Blocks(title=title) as demo:
     
       # options for n_components    
     btn = gr.Button(value="Submit")
-    btn.click(plot_pca_fa_analysis_side, inputs= [n_samples, n_features, n_components], outputs= gr.Plot(label='Multi-output regression with decision trees') ) # 
+    btn.click(plot_pca_fa_analysis_side, inputs= [n_samples, n_features, n_components], outputs= gr.Plot(label='PCA vs FA Model Selection with added noise') ) # 
     
 
 demo.launch()