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| Onpremise_LLM_Normal_Detection | |
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| app.py | |
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| junhyun01 | |
| Update app.py | |
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| import gradio as gr | |
| import torch | |
| from transformers import AutoTokenizer, BertForSequenceClassification, AutoModel | |
| from torch import nn | |
| import re | |
| def paragraph_leveling(text): | |
| model_name = "./trained_model/fine_tunning_encoder_v2" | |
| model = AutoModel.from_pretrained(model_name) | |
| tokenizer = AutoTokenizer.from_pretrained('zzxslp/RadBERT-RoBERTa-4m') | |
| class MLP(nn.Module): | |
| def __init__(self, target_size=3, input_size=768): | |
| super(MLP, self).__init__() | |
| self.num_classes = target_size | |
| self.input_size = input_size | |
| self.fc1 = nn.Linear(input_size, target_size) | |
| def forward(self, x): | |
| out = self.fc1(x) | |
| return out | |
| classifier = MLP(target_size=3, input_size=768) | |
| classifier.load_state_dict(torch.load('./trained_model/fine_tunning_classifier', map_location=torch.device('cpu'))) | |
| classifier.eval() | |
| output_list = [] | |
| text_list = text.split(".") | |
| result = [] | |
| output_list.append(("\n", None)) | |
| for idx_sentence in text_list: | |
| train_encoding = tokenizer( | |
| idx_sentence, | |
| return_tensors='pt', | |
| padding='max_length', | |
| truncation=True, | |
| max_length=120) | |
| output = model(**train_encoding) | |
| output = classifier(output[1]) | |
| output = output[0] | |
| if output.argmax(-1) == 0: | |
| output_list.append((idx_sentence, 'abnormal')) | |
| result.append(0) | |
| elif output.argmax(-1) == 1: | |
| output_list.append((idx_sentence, 'normal')) | |
| result.append(1) | |
| else: | |
| output_list.append((idx_sentence, 'uncertain')) | |
| result.append(2) | |
| output_list.append(('\n', None)) | |
| if 0 in result: | |
| output_list.append(('FINAL LABEL: ', None)) | |
| output_list.append(('ABNORMAL', 'abnormal')) | |
| else: | |
| output_list.append(('FINAL LABEL: ', None)) | |
| output_list.append(('NORMAL', 'normal')) | |
| return output_list | |
| demo = gr.Interface( | |
| paragraph_leveling, | |
| [ | |
| gr.Textbox( | |
| label="Medical Report", | |
| info="You can put any types of medical report", | |
| lines=20, | |
| value=" ", | |
| ), | |
| ], | |
| gr.HighlightedText( | |
| label="labeling", | |
| show_legend = True, | |
| show_label = True, | |
| color_map={"abnormal": "violet", "normal": "lightgreen", "uncertain": "lightgray"}), | |
| theme=gr.themes.Base() | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |