app.py

import os
from huggingface_hub import login
from transformers import AutoModelForCausalLM, AutoTokenizer
import gradio as gr
import torch

# Autenticar usando el token almacenado como secreto
hf_token = os.getenv("HF_API_TOKEN")
login(hf_token)

# Cargar el modelo y el tokenizador
model_name = "DeepESP/gpt2-spanish"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name).to("cuda")

def chat_with_gpt2_spanish(input_text):
    # Comprobar si la GPU está disponible
    device = "cuda" if torch.cuda.is_available() else "cpu"
    print(f"Using device: {device}")

    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=512).to(device)
    outputs = model.generate(
        **inputs, 
        max_length=100,
        num_beams=1,
        temperature=0.7,
        top_p=0.9,
        no_repeat_ngram_size=2,
        early_stopping=True
    )
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

# Crear la interfaz con Gradio
iface = gr.Interface(
    fn=chat_with_gpt2_spanish,
    inputs="text",
    outputs="text",
    title="Chat con GPT-2 en Español",
    description="Interfaz simple para comunicarte con el modelo GPT-2 en español."
)

iface.launch()