app.py

import gradio as gr
from transformers import pipeline


#api = gr.Interface.load("huggingface/EleutherAI/gpt-j-6B")
#api = gr.Interface.load("./models/mt5-small-finetuned-amazon-en-es")
#api = gr.Interface.load("huggingface/vhpvmx/mt5-small-finetuned-amazon-en-es") #ESTE LO CARGA

hub_model_id = "vhpvmx/mt5-small-finetuned-amazon-en-es"
summarizer = pipeline("summarization", model=hub_model_id)

#model="./models/mt5-small-finetuned-amazon-en-es"
#summarizer = pipeline("summarization", model)

def summary(text):
    return summarizer(text)[0]["summary_text"]


with gr.Blocks() as demo:
    input_text = gr.Textbox(placeholder="Ingresa la reseña del libro...", lines=4)
    output_text = gr.Textbox(label="Resumen")
    btn = gr.Button("Genera el resumen")

    btn.click(summary, input_text, output_text)

demo.launch()