import gradio as gr

from transformers import pipeline

classifier3 = pipeline('text-classification', model='nlmaldonadog/clasificador-rotten-tomatoes-bert-base-uncased')
classifier4 = pipeline('text-classification', model='nlmaldonadog/clasificador-rotten-tomatoes-xlnet-base-cased')

def val_label(lab):
    if lab == "LABEL_1":
        return "Positive"
    return "Negative"

def predict(text):
    m3 = classifier3(text)[0]
    prediction = f"Model 3: {val_label(m3['label'])} with {m3['score']} of confidence.\nModel 4: {val_label(m4['label'])} with {m4['score']} of confidence."
    return prediction

ifg = gr.Interface(fn=predict, inputs=[gr.Textbox(placeholder='Escribe aquí...')], outputs="text")
ifg.launch(share=True)