Update app.py

app.py

@@ -47,12 +47,7 @@ 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.")
-    
-    xor_image = Image.open("xor.png")
-    gr.Image(xor_image, label="Table explaining the 'XOR' operator")
-                
-    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("### Feel free to experiment with kernel and gamma values below to see how the quality of the decision function changes with the hyperparameters.")
@@ -62,11 +57,16 @@ with gr.Blocks() as demo:
     btn = gr.Button(value="Submit")
             
     with gr.Row():
-        plot = gr.Plot(label=f"Decision function plot for Non-Linear SVC with the '{inp1}' kernel and '{inp2}' gamma ")
+        plot = gr.Plot(label=f"Decision function plot for Non-Linear SVC with the '{inp1}' kernel and '{inp2}' gamma ").properties(object-fit="contain")
         num = gr.Textbox(label="Test Accuracy")
     
     btn.click(getColorMap,  inputs=[inp1, inp2], outputs=[plot, num])
 
+    gr.HTML("<hr>")
+
+    xor_image = Image.open("xor.png")
+    gr.Image(xor_image, label="Table explaining the 'XOR' operator")
+
 
 if __name__ == "__main__":
     print("hdh")