running fine

app.py

@@ -27,10 +27,17 @@ def _warmup():
     return f"Model loaded in {time.time() - t0:.1f} seconds."
 
 def ci_plot(mean: float, low: float, upp: float):
-    fig, ax = plt.subplots(figsize=(6, 1.6))
-    ax.barh(y=0, width=100, linewidth=2, alpha=0.15)
-    ax.barh(y=0, width=max(0, upp-low), height=0.5)
-    ax.plot([mean], [0], "o")
+    fig, ax = plt.subplots(figsize=(6, 1.4))
+    ax.errorbar(
+        x=mean, y=0, 
+        xerr=[[mean - low], [upp - mean]], 
+        fmt='o', color='blue',
+        ecolor='orange',
+        elinewidth=5,
+        capsize=8,
+        capthick=4,
+        markersize=10
+    )
     ax.set_xlim(0, 100)
     ax.set_yticks([])
     ax.set_xlabel("Empathy Score (0-100) +/- 95% CI")
@@ -42,9 +49,10 @@ def predict_with_ci(essay: str, article: str) -> tuple[float, float, float, plt.
     mean, var = predict(essay, article)
     # scores were originally in [1, 7]
     # lets scale them to [0, 100]
-    mean = (mean - 1) / 6 * 100
+    scale = 100 / 6
+    mean = (mean - 1) * scale
+    std = np.sqrt(var) * scale
 
-    std = np.sqrt(var)
     ci_low = max(0.0, mean - 1.96 * std)
     ci_upp = min(100.0, mean + 1.96 * std)
     fig = ci_plot(mean, ci_low, ci_upp)
@@ -55,9 +63,18 @@ 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=10, placeholder="Enter the essay text here...")
-            article_input = gr.Textbox(label="Stimulus (E.g., News Article)", lines=10, placeholder="Enter the article text here...")
+            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)
             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."],
+            ],
+            inputs=[essay_input, article_input],
+        )
+            
         with gr.Column():
             output_mean = gr.Number(label="Predicted Empathy Score (0-100)", precision=2)
             ci_low = gr.Number(label="95% CI Lower Bound", precision=2)
@@ -65,7 +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=[essay_input, article_input], outputs=[output_mean, ci_low, ci_upp, fig])        
 
     gr.Markdown("## About")
     gr.Markdown("""