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import gradio as gr
import torch
from transformers import pipeline, GPTJForCausalLM, AutoModelForCausalLM
from peft import LoraConfig, get_peft_model, PeftModel, PeftConfig
# config = PeftConfig.from_pretrained("hackathon-somos-nlp-2023/bertin-gpt-j-6b-ner-es")
# model = AutoModelForCausalLM.from_pretrained("hackathon-somos-nlp-2023/bertin-gpt-j-6b-ner-es", return_dict=True, load_in_8bit=True, device_map='auto')
# # load tokenizer
# tokenizer = AutoTokenizer.from_pretrained("hackathon-somos-nlp-2023/bertin-gpt-j-6b-ner-es")
# # Load the Lora model
# model = PeftModel.from_pretrained(model, "hackathon-somos-nlp-2023/bertin-gpt-j-6b-ner-es")
# # load fp 16 model
model = AutoModelForCausalLM.from_pretrained("bertin-project/bertin-gpt-j-6B", revision="half", load_in_8bit=True, device_map='auto')
config = AutoConfig.from_pretrained("bertin-project/bertin-gpt-j-6B")
# create pipeline
pipe = pipeline("text-generation", model=model, config=config, tokenizer=tokenizer, device=0,)
def predict(text):
return pipe(f"text: {text}, entities:")["generated_text"]
iface = gr.Interface(
fn=predict,
inputs='text',
outputs='text',
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()
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