better ordering

app.py

@@ -44,9 +44,9 @@ def ci_plot(mean: float, low: float, upp: float):
     fig.tight_layout()
     return fig
 
-def predict_with_ci(essay: str, article: str) -> tuple[float, float, float, plt.Figure]:
+def predict_with_ci(article: str, essay: str) -> tuple[float, float, float, plt.Figure]:
     _warmup()
-    mean, var = predict(essay, article)
+    mean, var = predict(essay, article) # the order is essay-article in UPLME model
     # scores were originally in [1, 7]
     # lets scale them to [0, 100]
     scale = 100 / 6
@@ -63,16 +63,16 @@ with gr.Blocks(title="UPLME", theme=Soft(primary_hue="blue")) as demo:
     gr.Markdown("# Empathy Prediction with Uncertainty Estimation")
     with gr.Row():
         with gr.Column():
-            essay_input = gr.Textbox(label="Response (E.g., Essay) towards the stimulus", lines=6)
             article_input = gr.Textbox(label="Stimulus (E.g., News Article)", lines=6)
+            essay_input = gr.Textbox(label="Response (E.g., Essay) towards the stimulus", lines=6)
             button = gr.Button("Predict")
 
             gr.Examples(
             examples=[
-                ["My heart just breaks for the people who are suffering.", "A month after Hurricane Matthew, 800,000 Haitians urgently need food."],
-                ["I see, but this doesn't sound too worrisome to me.", "A month after Hurricane Matthew, 800,000 Haitians urgently need food."],
+                ["A month after Hurricane Matthew, 800,000 Haitians urgently need food.", "My heart just breaks for the people who are suffering."],
+                ["A month after Hurricane Matthew, 800,000 Haitians urgently need food.", "I see, but this doesn't sound too worrisome to me."],
             ],
-            inputs=[essay_input, article_input],
+            inputs=[article_input, essay_input]
         )
             
         with gr.Column():
@@ -82,7 +82,7 @@ with gr.Blocks(title="UPLME", theme=Soft(primary_hue="blue")) as demo:
         
             fig = gr.Plot(show_label=False)
 
-        button.click(fn=predict_with_ci, inputs=[essay_input, article_input], outputs=[output_mean, ci_low, ci_upp, fig])        
+        button.click(fn=predict_with_ci, inputs=[article_input, essay_input], outputs=[output_mean, ci_low, ci_upp, fig])        
 
     gr.Markdown("## About")
     gr.Markdown("""