| import gradio as gr |
| from transformers import pipeline |
|
|
| |
| MODEL_NAME = "ENTUM-AI/distilbert-clickbait-classifier" |
|
|
| try: |
| classifier = pipeline("text-classification", model=MODEL_NAME) |
| except Exception as e: |
| print(f"Error loading model: {e}") |
| classifier = None |
|
|
| def predict(text): |
| if not text.strip(): |
| return "Please enter a headline." |
| |
| if classifier is None: |
| return "Model has not loaded yet or an error occurred." |
| |
| result = classifier(text)[0] |
| label = result['label'] |
| score = result['score'] |
| |
| |
| if label == "Clickbait": |
| return f"🚨 CLICKBAIT! (Confidence: {score:.1%})" |
| else: |
| return f"📰 NORMAL NEWS (Confidence: {score:.1%})" |
|
|
| |
| theme = gr.themes.Soft( |
| primary_hue="blue", |
| secondary_hue="indigo", |
| ) |
|
|
| with gr.Blocks(theme=theme, title="Clickbait Detector 🎣") as demo: |
| gr.Markdown( |
| """ |
| # 🎣 Clickbait Headline Detector |
| |
| This model, based on **DistilBERT**, predicts whether a news headline or article title is "clickbait". |
| It was trained on tens of thousands of real media headlines. |
| |
| *Enter any English headline below to check it!* |
| """ |
| ) |
| |
| with gr.Row(): |
| with gr.Column(scale=2): |
| input_text = gr.Textbox( |
| label="Enter headline", |
| placeholder="Example: 10 Bizarre Facts About Apples...", |
| lines=3 |
| ) |
| submit_btn = gr.Button("Check Headline 🔍", variant="primary") |
| |
| with gr.Column(scale=1): |
| output_text = gr.Textbox( |
| label="Model Verdict", |
| lines=3, |
| interactive=False |
| ) |
| |
| |
| gr.Examples( |
| examples=[ |
| ["10 Bizarre Facts About Apples That Will BLOW YOUR MIND! 🍎🤯"], |
| ["Apple releases new quarterly earnings report showing 5% growth."], |
| ["You'll Never Guess What Happened Next..."], |
| ["Federal Reserve announces increase in interest rates by 0.25%"] |
| ], |
| inputs=input_text |
| ) |
| |
| submit_btn.click(fn=predict, inputs=input_text, outputs=output_text) |
|
|
| |
| demo.launch() |
|
|
