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import gradio as gr |
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from transformers import pipeline |
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pipe = pipeline("text-classification", model="peter2000/xlm-roberta-base-finetuned-ecoicop") |
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def predict(text): |
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preds = pipe(text)[0] |
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return preds["label"].split('_')[1],preds["label"].split('_')[0], round(preds["score"], 5) |
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gradio_ui = gr.Interface( |
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fn=predict, |
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title="Predicting E-Coicop Product Categories", |
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description="Enter some product text (trained on name, category and url) from an online supermarket and predict the corresponding ECOICOP (level 5) product category for food and baverages.", |
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inputs=[ |
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gr.inputs.Textbox(lines=5, label="Paste some text here"), |
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], |
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outputs=[ |
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gr.outputs.Textbox(label="Label"), |
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gr.outputs.Textbox(label="Index"), |
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gr.outputs.Textbox(label="Score") |
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], |
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examples=[ |
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["Tiefkühl Eiscreme & Eiswürfel Bechereis | rewe beste wahl peanut butter eiscreme | REWE Beste Wahl Peanut Butter Eiscreme 500ml"], |
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["epicerie-sucree | cereales chocolat fraise nat | Céréales chocolat & fraise NAT"], |
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["Pelati e passate | unknown | Mutti Polpa di Pomodoro 3 x 400 g"] |
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], |
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) |
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gradio_ui.launch(debug=True) |