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)