app.py

from transformers import AutoTokenizer, AutoModelForTokenClassification
import gradio as gr
import torch
id2label = {
    "0": "B-LOC",
    "1": "B-MISC",
    "2": "B-ORG",
    "3": "B-PER",
    "4": "I-LOC",
    "5": "I-MISC",
    "6": "I-ORG",
    "7": "I-PER",
    "8": "O"
}
tokenizer = AutoTokenizer.from_pretrained('mrm8488/TinyBERT-spanish-uncased-finetuned-ner')
model = AutoModelForTokenClassification.from_pretrained('mrm8488/TinyBERT-spanish-uncased-finetuned-ner')
def get_objects(text):
  input_ids = torch.tensor(tokenizer.encode(text)).unsqueeze(0)
  outputs = model(input_ids)
  last_hidden_states = outputs[0]
  personajes=[]
  locaciones=[]
  for m in last_hidden_states:
    for index, n in enumerate(m):
      if(index > 0 and index <= len(text.split(" "))):
        if('LOC' in id2label[str(torch.argmax(n).item())]):
          locaciones.append(text.split(" ")[index-1]+"=> \n")
        if('PER' in id2label[str(torch.argmax(n).item())]):
          personajes.append(text.split(" ")[index-1]+"=> \n")
  return ''.join(personajes) + "Ubicaciones:\n" + ''.join(locaciones) 
def change_objects(text, objetos):
  for personaje in objetos.split('\n'):
    if ('=>' in personaje and len(personaje.split('=>')) > 0):
      text = text.replace(personaje.split("=>")[0], personaje.split("=>")[1])
  return text

demo = gr.Blocks()
with demo:
    cuento = gr.Textbox(lines=2)
    objetos = gr.Textbox()
    label = gr.Label()
    b1 = gr.Button("Identificar Pesonajes y Ubicaciones")
    b2 = gr.Button("Cambiar objetos")
    b1.click(get_objects, inputs=cuento, outputs=objetos)
    b2.click(change_objects, inputs=[cuento, objetos], outputs=label)
demo.launch()