Updated plot layout

app.py

@@ -67,7 +67,8 @@ def plot_lda_pca():
         )
     plt.legend(loc="best", shadow=False, scatterpoints=1)
     axes[1].legend(loc="lower right")
-    axes[1].set_title("LDA of IRIS dataset")    
+    axes[1].set_title("LDA of IRIS dataset")
+    plt.tight_layout()   
 
 
     return fig
@@ -76,19 +77,16 @@ def plot_lda_pca():
 title = "2-D projection of Iris dataset using LDA and PCA"
 with gr.Blocks(title=title) as demo:
     gr.Markdown(f"# {title}")
-    gr.Markdown(" This example shows how one can use Prinicipal Components Analysis (PCA) and Factor Analysis (FA) for model selection by observing the likelihood of a held-out dataset with added noise <br>"
-    " 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>"
+    gr.Markdown(" This example shows how one can use Prinicipal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) to cluster the Iris dataset based on provided features. <br>"
+    " PCA applied to this data identifies the combination of attributes (principal components, or directions in the feature space) that account for the most variance in the data. Here we plot the different samples on the 2 first principal components.  <br>"
+    "   <br>"
+
     " For further details please see the sklearn docs:"    
     )
 
     gr.Markdown(" **[Demo is based on sklearn docs found here](https://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_lda.html#sphx-glr-auto-examples-decomposition-plot-pca-vs-lda-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>"
-                "  Larger Depth trying to overfit and learn even the finner details of the data.<br>"
-               )
+    gr.Markdown(" **Dataset** : The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour and Virginica) with 4 attributes: sepal length, sepal width, petal length and petal width. . <br>")
 
     # with gr.Row():
     #     n_samples = gr.Slider(value=100, minimum=10, maximum=1000, step=10, label="n_samples")