ghostai1/CPU-only-Zero-Shot-Text-Classification

1# 🏷️ Zero-Shot Text Classification | CPU-only HF Space2 3import gradio as gr4from transformers import pipeline5 6# Load the zero-shot pipeline once at startup7classifier = pipeline(8    "zero-shot-classification",9    model="facebook/bart-large-mnli",10    device=-1        # CPU only11)12 13def zero_shot(text: str, labels: str, multi_label: bool):14    if not text.strip() or not labels.strip():15        return []16    # parse comma-separated labels17    candidate_list = [lbl.strip() for lbl in labels.split(",") if lbl.strip()]18    res = classifier(text, candidate_list, multi_label=multi_label)19    # build table of [label, score]20    table = [21        [label, round(score, 3)]22        for label, score in zip(res["labels"], res["scores"])23    ]24    return table25 26with gr.Blocks(title="🏷️ Zero-Shot Classifier") as demo:27    gr.Markdown(28        "# 🏷️ Zero-Shot Text Classification\n"29        "Paste any text, list your candidate labels (comma-separated),\n"30        "choose single- or multi-label mode, and see scores instantly."31    )32 33    with gr.Row():34        text_in = gr.Textbox(35            label="Input Text",36            lines=4,37            placeholder="e.g. The new conditioner left my hair incredibly soft!"38        )39        labels_in = gr.Textbox(40            label="Candidate Labels",41            lines=2,42            placeholder="e.g. Positive, Negative, Question, Feedback"43        )44 45    multi_in = gr.Checkbox(46        label="Multi-label classification",47        info="Assign multiple labels if checked; otherwise picks the top label."48    )49 50    run_btn = gr.Button("Classify 🏷️", variant="primary")51 52    result_df = gr.Dataframe(53        headers=["Label", "Score"],54        datatype=["str", "number"],55        interactive=False,56        wrap=True,57        label="Prediction Scores"58    )59 60    run_btn.click(61        zero_shot,62        inputs=[text_in, labels_in, multi_in],63        outputs=result_df64    )65 66if __name__ == "__main__":67    demo.launch(server_name="0.0.0.0")68