app.py

import gradio as gr
from datasets import load_dataset
from transformers import pipeline
from presentation import main_title, examples

model_name= 'hackathon-pln-es/electricidad-small-discriminator-finetuned-clasificacion-comentarios-suicidas'

def clasificar_comentarios(comentario):
    cls= pipeline("text-classification", model=model_name)
    return cls(comentario)[0]['label']

if __name__ == "__main__":
    gr.Interface(
        fn=clasificar_comentarios,
        inputs=[
            gr.inputs.Textbox(
                lines=10,
                label="Comentario a analizar:",
                placeholder="Ingrese el comentario por favor...",
                optional=False,
            ),
        ],
        outputs=[
            gr.outputs.HTML(
                label="Resultado:"
            )
        ],
        description=main_title,
        examples=examples,
        theme="seafoam",
        thumbnail="None",
        css="https://cdn.jsdelivr.net/npm/bootstrap@3.3.7/dist/css/bootstrap.min.css",
    ).launch()