app.py

import gradio as gr
from transformers import pipeline

CLASSIFIER_MODEL_ID = "sks01dev/clickbait-classifier"

# Load model assets directly from the Hub
classifier = pipeline(
    "sentiment-analysis", 
    model=CLASSIFIER_MODEL_ID,
    tokenizer=CLASSIFIER_MODEL_ID,
    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']      
    }
    return formatted_output

# Gradio Interface Setup
gr.Interface(
    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",
    description="DeBERTa-v3-small model deployed for high-confidence headline analysis.",
    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()