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| import gradio as gr | |
| from transformers import pipeline | |
| pipe = pipeline("text-classification", model="lewtun/xlm-roberta-base-finetuned-marc") | |
| def predict(text): | |
| label2emoji = {"terrible": "💩", "poor": "😾", "ok": "🐱", "good": "😺", "great": "😻"} | |
| preds = pipe(text)[0] | |
| return label2emoji[preds["label"]], round(preds["score"], 5) | |
| gradio_ui = gr.Interface( | |
| fn=predict, | |
| title="Predicting review scores from customer reviews", | |
| description="Enter some review text about an Amazon product and check what the model predicts for it's star rating.", | |
| inputs=[ | |
| gr.inputs.Textbox(lines=5, label="Paste some text here"), | |
| ], | |
| outputs=[ | |
| gr.outputs.Textbox(label="Label"), | |
| gr.outputs.Textbox(label="Score"), | |
| ], | |
| examples=[ | |
| ["I love these running shoes!", "Ich hasse dieses Buch"], | |
| ], | |
| ) | |
| gradio_ui.launch(debug=True) |