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| import gradio as gr | |
| import joblib | |
| from huggingface_hub import hf_hub_download | |
| # Download model + vectorizer from your HF repo (public repo) | |
| model_path = hf_hub_download( | |
| repo_id="Devishetty100/clickbait-detector", | |
| filename="clickbait_detector.pkl", | |
| token=None # None for public repo | |
| ) | |
| vectorizer_path = hf_hub_download( | |
| repo_id="Devishetty100/clickbait-detector", | |
| filename="tfidf_vectorizer.pkl", | |
| token=None | |
| ) | |
| # Load them | |
| model = joblib.load(model_path) | |
| vectorizer = joblib.load(vectorizer_path) | |
| # Prediction function | |
| def predict_headline(headline): | |
| pred = model.predict(vectorizer.transform([headline]))[0] | |
| return pred | |
| # Gradio Interface | |
| iface = gr.Interface( | |
| fn=predict_headline, | |
| inputs=gr.Textbox(lines=2, placeholder="Enter a headline..."), | |
| outputs="text", | |
| title="π° Clickbait Detector", | |
| description="Classifies headlines as clickbait or real news." | |
| ) | |
| iface.launch() | |