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