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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()
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