| import gradio as gr | |
| from transformers import pipeline | |
| def do_action(text): | |
| pipe = pipeline("text-classification", model="papluca/xlm-roberta-base-language-detection") | |
| result = pipe(text, top_k=10) | |
| # Reformat our result | |
| result = {item['label']: item['score'] for item in result} | |
| return result | |
| iface = gr.Interface(fn=do_action, inputs="text", outputs="label") | |
| iface.launch() | |