| import gradio as gr | |
| from transformers import pipeline | |
| classifier = pipeline('text-classification', model='nlmaldonadog/clasificador-rotten-tomatoes-xlnet-base-cased') | |
| def predict(text): | |
| prediction = classifier(text) | |
| score = int(round(prediction[0]['score'] * 100)) | |
| if prediction[0]['label'] == "LABEL_0": | |
| output = f"Negative sentiment with a confidence level of {score}%." | |
| else: | |
| output = f"Positive sentiment with a confidence level of {score}%." | |
| return output | |
| iface = gr.Interface(fn=predict, inputs=[gr.Textbox(placeholder='Escribe aquí...')], outputs="text") | |
| iface.launch(share=True) |