app.py

import gradio as gr
from transformers import pipeline

# This pulls your fine-tuned model from your HF Model Repo
model_path = "MuhammadSaad1234/bert-news-classifier"
pipe = pipeline("text-classification", model=model_path)

labels = ["World", "Sports", "Business", "Sci/Tech"]

def classify_news(text):
    if not text.strip():
        return {label: 0.0 for label in labels}
    out = pipe(text)[0]
    idx = int(out['label'].split('_')[-1])
    return {labels[idx]: float(out['score'])}

demo = gr.Interface(
    fn=classify_news,
    inputs=gr.Textbox(lines=3, label="News Headline", placeholder="Enter news here..."),
    outputs=gr.Label(num_top_classes=4, label="Topic Prediction"),
    title="📰 BERT News Topic Classifier",
    description="Fine-tuned BERT-base-uncased on the AG News dataset."
)

if __name__ == "__main__":
    demo.launch()