app.py

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 traducción
        print(f"Error en la traducción: {e}")
        return prompt_generado 

# Define the Gradio interface
iface = gr.Interface(
    fn=generar_prompt_desde_objeto, 
    inputs=gr.Textbox(lines=2, placeholder="Enter object description here..."), 
    outputs="text",
    title="3D Prompt Generator",
    description="Generates 3D prompts from object descriptions using a fine-tuned BART model.",
)



# Launch the interface
iface.launch()