Update app.py

app.py

@@ -81,21 +81,21 @@ with gr.Blocks(title=title) as demo:
             # out = gr.Textbox(label="explaination of the loss function")
             gr.Markdown(
             """
-            # Data Generation
+            ## Data Generation
             We generate 2 clusters one spherical and the other slightly deformed, from Standard Normal distribution
             For the sake of consistency inliers are assigned a ground label of 1 and outliers are assigned a label -1.
             The plot is a visualization of the clusters of the input dataset.
 
             """)
 
-    with gr.Tab("**Plot Decision Boundary**"):
+    with gr.Tab("Plot Decision Boundary"):
       # btn_decision = gr.Button(value="Plot decision boundary")
       # btn_decision.click(plot_decision_boundary, outputs= gr.Plot(label='Plot decision boundary') )
       with gr.Row():
         image_decision = gr.Image('./downloaded-model/decision_boundary.png')
         gr.Markdown(
         """
-        # Plot the Discrete Decision Boundary
+        ## Plot the Discrete Decision Boundary
         We plot the discrete decision boundary. 
         The background colour represents whether a sample in that given area is predicted to be an outlier or not. 
         The scatter plot displays the true labels
@@ -107,24 +107,21 @@ with gr.Blocks(title=title) as demo:
         image_path = gr.Image('./downloaded-model/plot_path.png')
         gr.Markdown(
         """
-        # Plot the path length of the decision boundary
-        By setting the response_method="decision_function", the background of the DecisionBoundaryDisplay represents 
+        ## Plot the path length of the decision boundary
+        By setting the `response_method="decision_function"`, the background of the `DecisionBoundaryDisplay` represents 
         the measure of the normality of an observation. 
 
-        Normality of Observation = path length/(Number_of_forests_of_random trees) - Eqn.1 
+        Normality of Observation = Path Length/Number of Forests of Random Trees
 
         
-        The RHS of the above equation Eqn.1 is given by the number of splits required to isolate a given sample
+        The RHS of the above equation is given by the number of splits required to isolate a given sample
         Such score is given by the path length averaged over a forest of random trees, which itself is given by the depth of 
-        the leaf (or equivalently the number of splits) 
-        required to isolate a given sample.
+        the leaf (or equivalently the number of splits) required to isolate a given sample.
 
         When a forest of random trees collectively produces short path lengths for isolating some particular samples, 
-        they are highly likely to be anomalies and the measure of normality is close to 0. 
+        they are more likely to have anomalies, and the measure of normality is close to 0. 
         Similarly, large paths correspond to values close to 1 and are more likely to be inliers.
 
         """)
 
-    
-    gr.Markdown( f"## Success")
 demo.launch()