Update app.py

app.py

@@ -178,12 +178,12 @@ with gr.Blocks(title="Tabular Flower Classifier", theme=gr.themes.Soft()) as dem
     # Tabular Flower Classifier
     
     This app uses an **AutoGluon TabularPredictor** to classify flowers based on their features.
-    Provide feature values and click **Predict** to see the classification results.
+    Adjust the feature values below and click **Predict** to see the classification results.
     """)
     
     with gr.Row():
         # LEFT: Inputs
-        with gr.Column():
+        with gr.Column(scale=1):
             gr.Markdown("### Input Features")
             feature_inputs = []
             
@@ -196,29 +196,29 @@ with gr.Blocks(title="Tabular Flower Classifier", theme=gr.themes.Soft()) as dem
                     input_widget = gr.Number(value=0.0, label=feature)
                 feature_inputs.append(input_widget)
             
-            predict_btn = gr.Button("Predict", variant="primary")
+            predict_btn = gr.Button("Predict", variant="primary", size="lg")
         
         # RIGHT: Outputs
-        with gr.Column():
-            gr.Markdown("### Results")
-            prediction_output = gr.Markdown()
-            proba_display = gr.Label(num_top_classes=10, label="Class Probabilities")
+        with gr.Column(scale=1):
+            gr.Markdown("### Prediction Results")
+            prediction_output = gr.Markdown(value="*Adjust features and click Predict*")
+            proba_display = gr.Label(num_top_classes=5, label="Top 5 Class Probabilities")
     
+    # Button click handler
+    predict_btn.click(
+        fn=predict,
+        inputs=feature_inputs,
+        outputs=[proba_display, prediction_output]
+    )
+    
+    gr.Markdown("### Example flower measurements")
     # Example presets
     gr.Examples(
         examples=EXAMPLES,
         inputs=feature_inputs,
         outputs=[proba_display, prediction_output],
         fn=predict,
-        cache_examples=False,
-        label="Example flower measurements"
-    )
-    
-    # Button click handler
-    predict_btn.click(
-        fn=predict,
-        inputs=feature_inputs,
-        outputs=[proba_display, prediction_output]
+        cache_examples=False
     )
     
     gr.Markdown("""