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| import gradio as gr | |
| import os | |
| from npc_bert_models.gradio_demo import * | |
| from npc_bert_models.mlm_module import NpcBertMLM | |
| from npc_bert_models.cls_module import NpcBertCLS | |
| import json | |
| class main_window(): | |
| def __init__(self): | |
| self.interface = None | |
| self.examples = json.load(open("examples.json", 'r')) | |
| def initialize(self): | |
| #! Initialize MLM | |
| self.npc_mlm = NpcBertMLM() | |
| self.npc_mlm.load() | |
| with gr.Blocks() as self.mlm_interface: | |
| gr.Markdown("# Masked work prediction\n" | |
| "Enter any sentence. Use the token `[MASK]` to see what the model predicts.\n" | |
| "## Our examples:\n" | |
| "|Input masked sequence|Ground-truth masked word|\n" | |
| "|---------------------|------------------------|\n" | |
| + "\n".join([f"|{a}|{b}|" for a, b in zip(self.examples['mlm-inp'], self.examples['mlm-inp-GT'])])) | |
| with gr.Row(): | |
| with gr.Column(): | |
| inp = gr.Textbox("The tumor is confined in the [MASK].", label='mlm-inp') | |
| btn = gr.Button("Run", variant='primary') | |
| with gr.Column(): | |
| out = gr.Label(num_top_classes=5) | |
| gr.Examples(self.examples['mlm-inp'], inputs=inp, label='mlm-inp') | |
| btn.click(fn=self.npc_mlm.__call__, inputs=inp, outputs=out) | |
| inp.submit(fn=self.npc_mlm.__call__, inputs=inp, outputs=out) | |
| #! Initialize report classification | |
| self.npc_cls = NpcBertCLS() | |
| self.npc_cls.load() | |
| with gr.Blocks() as self.cls_interface: | |
| gr.Markdown(""" | |
| # Report discrimination | |
| In this example we explored how the fine-tuned BERT aids downstream task. We further trained it | |
| to do a simple task of discriminating between reports written for non-NPC patients and NPC patients. | |
| # Disclaimer | |
| The examples are mock reports that is created with reference to authentic reports, they do not | |
| represent any real patients. However, it was written to be an authentic representation of NPC or | |
| patient under investigation for suspected NPC but with negative imaging findings. | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| inp = gr.TextArea(placeholder="Use examples at the bottom to load example text reports.") | |
| inf = gr.File(file_types=['.txt'], label="Report file (plaintext)", show_label=True, interactive=True) | |
| inf.upload(fn=self._set_report_file_helper, inputs=inf, outputs=inp) | |
| inf.change(fn=self._set_report_file_helper, inputs=inf, outputs=inp) | |
| btn = gr.Button("Run", variant='primary') | |
| with gr.Column(): | |
| out = gr.Label(num_top_classes=2) | |
| # gr.Examples(examples=list(self.examples['reports'].values()), inputs=inp) | |
| gr.Examples(examples="./report_examples", inputs=inf) | |
| btn.click(fn=self.npc_cls.__call__, inputs=inp, outputs=out) | |
| inp.submit(fn=self.npc_cls.__call__, inputs=inp, outputs=out) | |
| with gr.Blocks() as self.interface: | |
| gr.Markdown(""" | |
| # Introduction | |
| This is a demo for displaying the potential of language models fine tunned using the carefully curated dataset | |
| of structured MRI radiology reports for nasopharyngeal carcinoma (NPC) examination. Our team has an established | |
| track record for researching the role of AI in early detectio for NPC. We have already developed an AI system | |
| with high sensitivty and specificity > 90%. However. we explanability of the system is currently a major challenge | |
| for translation. In fact, this is a general problem for AI's developement in radiology. Therefore, in this pilot | |
| study, we investigate language model in understanding the context of the disease to explore the possibility of incorporating | |
| language model in our existing system for explanability. | |
| # Affliations | |
| * Dr. M.Lun Wong, Dept. Imaging and Interventional Radiology. The Chinese University of Hong Kong | |
| # Disclaimer | |
| This software is provided as is and it is not a clinically validated software. The authors disclaim any responsibility | |
| arising as a consequence from using this demo. | |
| """) | |
| tabs = gr.TabbedInterface([self.mlm_interface, self.cls_interface], tab_names=["Masked Language Model", "Report classification"]) | |
| def lauch(self): | |
| self.interface.launch() | |
| pass | |
| def _set_report_file_helper(self, file_in): | |
| try: | |
| text = open(file_in, 'r').read() | |
| return text | |
| except: | |
| print(f"Cannot read file {file_in}") | |
| # Do nothing | |
| pass | |
| if __name__ == '__main__': | |
| mw = main_window() | |
| mw.initialize() | |
| mw.lauch() | |