app.py

import gradio as gr
import transformers
from transformers import AutoModelForTokenClassification, AutoTokenizer
import torch

# model large
model_name = "pucpr/clinicalnerpt-chemical"
model_large = AutoModelForTokenClassification.from_pretrained(model_name)
tokenizer_large = AutoTokenizer.from_pretrained(model_name)

# model base
model_name = "pucpr/clinicalnerpt-chemical"
model_base = AutoModelForTokenClassification.from_pretrained(model_name)
tokenizer_base = AutoTokenizer.from_pretrained(model_name)

# css
background_colors_entity_word = {
    'ChemicalDrugs': "#fae8ff",
}

background_colors_entity_tag = {
    'ChemicalDrugs': "#d946ef", 
}

css = {
'entity_word': 'color:#000000;background: #xxxxxx; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 2.5; border-radius: 0.35em;',
'entity_tag': 'color:#fff;background: #xxxxxx; font-size: 0.8em; font-weight: bold; line-height: 2.5; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5em;'
}

list_EN = "<span style='"
list_EN += f"{css['entity_tag'].replace('#xxxxxx',background_colors_entity_tag['ChemicalDrugs'])};padding:0.5em;"
list_EN += "'>ChemicalDrugs</span>"

# infos
title = "BioBERTpt - Chemical entities"
description = "BioBERTpt - Chemical entities"
allow_screenshot = False
allow_flagging = False
examples = [
["Dispneia venoso central em subclavia D duplolumen recebendo solução salina e glicosada em BI."],
["Paciente com Sepse pulmonar em D8 tazocin (paciente não recebeu por 2 dias Atb)."],
["FOI REALIZADO CURSO DE ATB COM LEVOFLOXACINA POR 7 DIAS."],
]

def ner(input_text):

  num = 0
  for tokenizer,model in zip([tokenizer_large,tokenizer_base],[model_large,model_base]):

    # tokenization
    inputs = tokenizer(input_text, max_length=512, truncation=True, return_tensors="pt")
    tokens = inputs.tokens()

    # get predictions
    outputs = model(**inputs).logits
    predictions = torch.argmax(outputs, dim=2)
    preds = [model_base.config.id2label[prediction] for prediction in predictions[0].numpy()]

    # variables
    groups_pred = dict()
    group_indices = list()
    group_label = ''
    pred_prec = ''
    group_start =  ''
    count = 0

    # group the NEs
    for i,en in enumerate(preds):

      if en == 'O': 

        if len(group_indices) > 0:
          groups_pred[count] = {'indices':group_indices,'en':group_label}
          group_indices = list()
          group_label = ''
          count += 1

      if en.startswith('B'):

        if len(group_indices) > 0:
          groups_pred[count] = {'indices':group_indices,'en':group_label}
          group_indices = list()
          group_label = '' 
          count += 1 

        group_indices.append(i)
        group_label = en.replace('B-','')
        pred_prec = en

      elif en.startswith('I'):
        
        if len(group_indices) > 0:
          if en.replace('I-','') == group_label:
            group_indices.append(i)
          else:
            groups_pred[count] = {'indices':group_indices,'en':group_label}
            group_indices = [i]
            group_label = en.replace('I-','')
            count += 1 
        else:
          group_indices = [i]
          group_label = en.replace('I-','')

      if i == len(preds) - 1 and len(group_indices) > 0:
        groups_pred[count] = {'indices':group_indices,'en':group_label}
        group_indices = list()
        group_label = ''
        count += 1

    # there is at least one NE
    len_groups_pred = len(groups_pred)
    inputs = inputs['input_ids'][0].numpy()#[1:-1]
    if len_groups_pred > 0:
      for pred_num in range(len_groups_pred):
        en = groups_pred[pred_num]['en']
        indices = groups_pred[pred_num]['indices']
        if pred_num == 0:
          if indices[0] > 0:
            output = tokenizer.decode(inputs[:indices[0]]) + f'<span style="{css["entity_word"].replace("#xxxxxx",background_colors_entity_word[en])}">' + tokenizer.decode(inputs[indices[0]:indices[-1]+1]) + f'<span style="{css["entity_tag"].replace("#xxxxxx",background_colors_entity_tag[en])}">' + en + '</span></span> '
          else:
            output = f'<span style="{css["entity_word"].replace("#xxxxxx",background_colors_entity_word[en])}">' + tokenizer.decode(inputs[indices[0]:indices[-1]+1]) + f'<span style="{css["entity_tag"].replace("#xxxxxx",background_colors_entity_tag[en])}">' + en + '</span></span> '
        else:
          output += tokenizer.decode(inputs[indices_prev[-1]+1:indices[0]]) + f'<span style="{css["entity_word"].replace("#xxxxxx",background_colors_entity_word[en])}">' + tokenizer.decode(inputs[indices[0]:indices[-1]+1]) + f'<span style="{css["entity_tag"].replace("#xxxxxx",background_colors_entity_tag[en])}">' + en + '</span></span> '
        indices_prev = indices
      output += tokenizer.decode(inputs[indices_prev[-1]+1:])
    else:
      output = input_text

    # output
    output = output.replace('[CLS]','').replace(' [SEP]','').replace('##','')
    output = "<div style='max-width:100%; max-height:360px; overflow:auto'>" + output + "</div>"

    if num == 0: 
      output_large = output
      num += 1
    else: output_base = output

  return output_large, output_base

# interface gradio
iface = gr.Interface(
    title=title,
    description=description,
    article=article,
    allow_screenshot=allow_screenshot,
    allow_flagging=allow_flagging,
    fn=ner,
    inputs=gr.inputs.Textbox(placeholder="Digite uma frase aqui ou clique em um exemplo:", lines=5),
    outputs=[gr.outputs.HTML(label="NER1"),gr.outputs.HTML(label="NER2")],
    examples=examples
)

iface.launch()