app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

MODEL_ID = "Camayli/practica8"   # tu repo del modelo

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_ID)

device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)

def traducir(texto):
    inputs = tokenizer(
        [texto],
        return_tensors="pt",
        truncation=True,
        padding=True,
        max_length=128
    ).to(device)

    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=64,
            num_beams=4
        )

    return tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]

demo = gr.Interface(
    fn=traducir,
    inputs=gr.Textbox(lines=4, label="Texto en inglés"),
    outputs=gr.Textbox(lines=4, label="Traducción"),
    title="Práctica 8 - Traductor EN → ES",
    description="Space desplegado en Hugging Face usando Gradio."
)

if __name__ == "__main__":
    demo.launch()