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| import gradio as gr | |
| from transformers import pipeline | |
| # 1) Initialize zero-shot classifier | |
| classifier = pipeline( | |
| task="zero-shot-classification", | |
| model="facebook/bart-large-mnli" | |
| ) | |
| # 2) Define candidate labels | |
| LABELS = ["linear algebra", "calculus", "probability", "geometry"] | |
| # 3) Gradio interface function | |
| def tag_question(question): | |
| result = classifier(question, candidate_labels=LABELS) | |
| return {lbl: round(score, 3) for lbl, score in zip(result["labels"], result["scores"])} | |
| # 4) Build UI | |
| iface = gr.Interface( | |
| fn=tag_question, | |
| inputs=gr.Textbox(lines=3, placeholder="Enter your MCQ here..."), | |
| outputs=gr.Label(num_top_classes=3), | |
| title="Zero-Shot Question Tagger", | |
| description="Classify questions into math topics without any training data." | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() | |