import gradio as gr
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

peft_model_id = "hackathon-somos-nlp-2023/bertin-gpt-j-6b-ner-es"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(
    config.base_model_name_or_path,
    return_dict=True,
    load_in_8bit=True,
    device_map="auto",
    revision="half",
)
tokenizer = AutoTokenizer.from_pretrained(peft_model_id)
# Load the Lora model
model = PeftModel.from_pretrained(model, peft_model_id)

model.eval()


def gen_entities(in_text):
    """Does Named Entity Recognition in the given text."""
    text = f"<SP> text: {in_text}\n\n entities:"
    batch = tokenizer(text, return_tensors="pt")
    batch["input_ids"] = batch["input_ids"].to("cuda")
    with torch.cuda.amp.autocast():
        output_tokens = model.generate(**batch, max_new_tokens=256, eos_token_id=50258)

    response = tokenizer.batch_decode(
        output_tokens.detach().cpu().numpy(), skip_special_tokens=False
    )[0]

    return response[response.find("entities") : response.find("<EP>")]


iface = gr.Interface(
    fn=gen_entities,
    inputs="text",
    outputs="text",
    title="Podcast Named Entity Recognition",
    description="Introduce un texto corto para que el modelo identifique las identidades presentes en el mismo.",
    theme="gradio/monochrome",
    examples=[
        [
            "Yo hoy voy a hablar de mujeres en el mundo del arte, porque me ha leído un libro fantástico que se llama Historia del arte sin hombres, de Katie Hesel."
        ]
    ],
)
iface.launch()