from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from peft import PeftModel, PeftConfig
import gradio as gr

# 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)

# Load the base model from the Hub
model = AutoModelForSeq2SeqLM.from_pretrained(peft_config.base_model_name_or_path)

# Load the tokenizer from the Hub
tokenizer = 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
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)
    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()