Update app.py

app.py

@@ -14,11 +14,15 @@ def predict_sentiment(text):
     
     sentiment = "Positive" if prediction == 1 else "Negative"
     
-    return {
-        "Sentiment": sentiment,
-        "Confidence": f"{confidence:.2f}",
-        "Explanation": f"The model predicts this text is {sentiment.lower()} with {confidence:.2%} confidence."
-    }
+    return (
+        sentiment,
+        f"{confidence:.2f}",
+        f"The model predicts this text is {sentiment.lower()} with {confidence:.2%} confidence."
+    )
+
+# Function to get predictions for examples
+def get_example_predictions(examples):
+    return [predict_sentiment(ex[0]) for ex in examples]
 
 # Example texts
 examples = [
@@ -28,16 +32,25 @@ examples = [
     ["ennes el kol za3nin w ma3andhomch flous"]
 ]
 
+# Get predictions for examples
+example_predictions = get_example_predictions(examples)
+
+# Create formatted examples with predictions
+formatted_examples = [
+    [ex[0], f"{pred[0]} (Confidence: {pred[1]})"] 
+    for ex, pred in zip(examples, example_predictions)
+]
+
 # Create Gradio interface
 iface = gr.Interface(
     fn=predict_sentiment,
     inputs=gr.Textbox(lines=3, placeholder="Enter Tunisian Arabiz text here..."),
-    outputs={
-        "Sentiment": gr.Label(label="Predicted Sentiment"),
-        "Confidence": gr.Label(label="Confidence Score"),
-        "Explanation": gr.Textbox(label="Explanation")
-    },
-    examples=examples,
+    outputs=[
+        gr.Label(label="Predicted Sentiment"),
+        gr.Label(label="Confidence Score"),
+        gr.Textbox(label="Explanation")
+    ],
+    examples=formatted_examples,
     title="Tunisian Arabiz Sentiment Analysis",
     description="""
     This model predicts the sentiment of Tunisian Arabiz text as either Positive or Negative.