Update app.py

app.py

@@ -45,17 +45,17 @@ def getColorMap(kernel, gamma):
 
 with gr.Blocks() as demo:
     gr.Markdown("## Learning the XOR function: An application of Binary Classification using Non-linear SVM")
-    gr.Markdown("This demo is based on this [scikit-learn example](https://scikit-learn.org/stable/auto_examples/svm/plot_svm_nonlinear.html#sphx-glr-auto-examples-svm-plot-svm-nonlinear-py).")
-    gr.Markdown("In this demo, we use a non-linear SVC (Support Vector Classifier) to learn the decision function of the XOR operator.")
+    gr.Markdown("### This demo is based on this [scikit-learn example](https://scikit-learn.org/stable/auto_examples/svm/plot_svm_nonlinear.html#sphx-glr-auto-examples-svm-plot-svm-nonlinear-py).")
+    gr.Markdown("### In this demo, we use a non-linear SVC (Support Vector Classifier) to learn the decision function of the XOR operator.")
     
     xor_image = Image.open("xor.png")
     gr.Image(xor_image, label="Table explaining the 'XOR' operator", shape = (208.5, 250))
                 
     gr.HTML("<hr>")
     
-    gr.Markdown("Furthermore, we observe that we get different decision function plots by varying the Kernel and Gamma hyperparameters of the non-linear SVC.")
+    gr.Markdown("### Furthermore, we observe that we get different decision function plots by varying the Kernel and Gamma hyperparameters of the non-linear SVC.")
 
-    gr.Markdown("Feel free to experiment with kernel and gamma values below to see how the quality of the decision function changes with the hyperparameters.")
+    gr.Markdown("### Feel free to experiment with kernel and gamma values below to see how the quality of the decision function changes with the hyperparameters.")
 
     inp1 = gr.Radio(['poly', 'rbf', 'sigmoid'], label="Kernel", info="Choose a kernel")
     inp2 = gr.Radio(['scale', 'auto'], label="Gamma", info="Choose a gamma value")