Deploying app.py

app.py

@@ -4,26 +4,24 @@ from transformers import pipeline
 
 CLASSIFIER_MODEL_ID = "sks01dev/clickbait-classifier"
 
-# Load model assets directly from the Hub
 classifier = pipeline(
-    "sentiment-analysis", 
+    "sentiment-analysis",
     model=CLASSIFIER_MODEL_ID,
     tokenizer=CLASSIFIER_MODEL_ID,
-    return_all_scores=True 
+    return_all_scores=True
 )
 
 def predict(headline):
     results = classifier(headline)[0]
-    # Format output for clear confidence display
     formatted_output = {
-        "NOT CLICKBAIT (0)": results[0]['score'], 
-        "CLICKBAIT (1)": results[1]['score']      
+        "NOT CLICKBAIT (0)": results[0]['score'],
+        "CLICKBAIT (1)": results[1]['score']
     }
     return formatted_output
 
 # Gradio Interface Setup
 gr.Interface(
-    fn=predict, 
+    fn=predict,
     inputs=gr.Textbox(lines=2, label="Enter News Headline"),
     outputs=gr.Label(num_top_classes=2, title="Prediction Confidence"),
     title="World-Class Clickbait Predictor",
@@ -31,6 +29,5 @@ gr.Interface(
     examples=[
         ["10 Ways To Instantly Improve Your Mood"],
         ["You Won't Believe What Happened When We Tested This!"],
-        ["You Won't Believe What Happened When We Tested This!"],
     ]
 ).launch()