Upload app.py

app.py

@@ -0,0 +1,153 @@
+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()