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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from peft import PeftModel, PeftConfig
import gradio as gr
from accelerate import Accelerator
from googletrans import Translator
# Inicializar el acelerador
accelerator = Accelerator()
translator = Translator()
# Define the repository ID
repo_id = "Miguelpef/bart-base-lora-3DPrompt" # Replace with your repository name
# Load the PEFT configuration from the Hub
peft_config = PeftConfig.from_pretrained(repo_id)
# Cargar el modelo y el tokenizador con accelerate
model, tokenizer = accelerator.prepare(
AutoModelForSeq2SeqLM.from_pretrained(peft_config.base_model_name_or_path),
AutoTokenizer.from_pretrained(repo_id)
)
# Wrap the base model with PEFT
model = PeftModel.from_pretrained(model, repo_id)
# Now you can use the model for inference as before
async def generar_prompt_desde_objeto(objeto):
prompt = objeto
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
outputs = model.generate(**inputs, max_length=100)
prompt_generado = tokenizer.decode(outputs[0], skip_special_tokens=True)
try:
# Intentar traducir el texto generado
prompt_translate = await translator.translate(prompt_generado, src="es", dest="en")
return prompt_translate.text
except Exception as e:
# Manejo de errores en la traducci贸n
print(f"Error en la traducci贸n: {e}")
return prompt_generado
# Define the Gradio interface
iface = gr.Interface(
fn=generar_prompt_desde_objeto,
inputs=gr.Textbox(lines=2, placeholder="Introduce la descripci贸n del objeto aqu铆..."),
outputs="text",
title="Generador de Prompt 3D",
description="""Genera un prompt para crear una im谩gen del modelo 3D desde la descripcion de un objeto usando el modelo Facebook/bart fine-tuned\n
ATENCI脫N: Funciona solamente en el idioma Castellano\n
ATTENTION: It only works in the Spanish language""",
)
# Launch the interface
iface.launch() |