Add details to description and plot

app.py

@@ -54,6 +54,9 @@ def train_plot(multi_class, num_samples):
     for i, color in zip(clf.classes_, colors):
         plot_hyperplane(i, color)
 
+    plt.xlabel("x")
+    plt.ylabel("y")
+
     return fig, clf.score(X, y)
 
 def plot_both(num_samples):
@@ -62,8 +65,21 @@ def plot_both(num_samples):
 
     return fig1, fig2, score1, score2
 
-title = "Plot multinomial and One-vs-Rest Logistic Regression "
-description = "Plot decision surface of multinomial and One-vs-Rest Logistic Regression. The hyperplanes corresponding to the three One-vs-Rest (OVR) classifiers are represented by the dashed lines."
+title = "Plot multinomial and One-vs-Rest Logistic Regression"
+description = """
+            The demo shows the difference between multinomial and One-vs-Rest Logistic Regression in a \
+            two-dimensional synthetic dataset. 
+            
+            The dataset is generated around three cluster centers to simulate three different classes. \
+            Two different types of logistic regression models are fit to the synthetic data: a multinomial \
+            and a one-vs-rest logistic regression. The figures show scatter plots of the data, the decision \
+            boundaries of each logistic regresion model and the decision surfaces in different colors per respective class. \
+            The hyperplanes corresponding to the three One-vs-Rest (OVR) classifiers are represented by the \
+            dashed lines. \
+            
+
+            The mean accuracy of the training data and labels for each classifier is given underneath each respective plot. 
+            """
 with gr.Blocks() as demo:
     gr.Markdown(f"## {title}")
     gr.Markdown(description)