Production Deploy: Final minimal app and configuration.

app.py

@@ -4,7 +4,7 @@ from transformers import pipeline
 
 CLASSIFIER_MODEL_ID = "sks01dev/clickbait-classifier"
 
-# The pipeline loads assets directly from the Hub and handles pre/post-processing.
+# Load model assets directly from the Hub
 classifier = pipeline(
     "sentiment-analysis", 
     model=CLASSIFIER_MODEL_ID,
@@ -13,9 +13,8 @@ classifier = pipeline(
 )
 
 def predict(headline):
-    # Runs inference and formats the output dictionary for Gradio Label
     results = classifier(headline)[0]
-    
+    # Format output for clear confidence display
     formatted_output = {
         "NOT CLICKBAIT (0)": results[0]['score'], 
         "CLICKBAIT (1)": results[1]['score']      
@@ -31,7 +30,7 @@ gr.Interface(
     description="DeBERTa-v3-small model deployed for high-confidence headline analysis.",
     examples=[
         ["10 Ways To Instantly Improve Your Mood"],
-        ["Government Releases New Economic Policy Report"],
+        ["You Won't Believe What Happened When We Tested This!"],
         ["You Won't Believe What Happened When We Tested This!"],
     ]
 ).launch()