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DAVA / app.py

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Update app.py

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	import streamlit as st
	import gradio as gr
	import shap
	import numpy as np
	import scipy as sp
	import torch
	import tensorflow as tf
	import transformers
	from transformers import pipeline
	from transformers import RobertaTokenizer, RobertaModel
	from transformers import AutoModelForSequenceClassification
	from transformers import TFAutoModelForSequenceClassification
	from transformers import AutoTokenizer, AutoModelForTokenClassification

	import matplotlib.pyplot as plt
	import sys
	import csv

	csv.field_size_limit(sys.maxsize)

	device = "cuda:0" if torch.cuda.is_available() else "cpu"

	tokenizer = AutoTokenizer.from_pretrained("chrisclark1016/ADR_best")
	model = AutoModelForSequenceClassification.from_pretrained("chrisclark1016/ADR_best").to(device)

	# build a pipeline object to do predictions
	pred = transformers.pipeline("text-classification", model=model,
	tokenizer=tokenizer, return_all_scores=True)

	explainer = shap.Explainer(pred)

	##
	# classifier = transformers.pipeline("text-classification", model = "cross-encoder/qnli-electra-base")

	# def med_score(x):
	# label = x['label']
	# score_1 = x['score']
	# return round(score_1,3)

	# def sym_score(x):
	# label2sym= x['label']
	# score_1sym = x['score']
	## return round(score_1sym,3)

	ner_tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all")
	ner_model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all")

	ner_pipe = pipeline("ner", model=ner_model, tokenizer=ner_tokenizer, aggregation_strategy="simple") # pass device=0 if using gpu


	def adr_predict(x):
	encoded_input = tokenizer(x, return_tensors='pt')
	output = model(**encoded_input)
	scores = output[0][0].detach().numpy()
	scores = tf.nn.softmax(scores)

	shap_values = explainer([str(x).lower()])
	# # Find the index of the class you want as the default reference (e.g., 'label_1')
	# label_1_index = np.where(np.array(explainer.output_names) == 'label_1')[0][0]

	# # Plot the SHAP values for a specific instance in your dataset (e.g., instance 0)
	# shap.plots.text(shap_values[label_1_index][0])

	#Set the colors for the labels
	label_colors = ["green", "red"]

	local_plot = shap.plots.text(shap_values[0], display=False, color=label_colors)

	# med = med_score(classifier(x+str(", There is a medication."))[0])
	# sym = sym_score(classifier(x+str(", There is a symptom."))[0])

	res = ner_pipe(x)

	entity_colors = {
	'Severity': 'red',
	'Sign_symptom': 'green',
	'Medication': 'lightblue',
	'Age': 'pink',
	'Sex':'yellow',
	'Diagnostic_procedure':'gray',
	'Biological_structure':'silver'}

	htext = ""
	prev_end = 0

	for entity in res:
	start = entity['start']
	end = entity['end']
	word = entity['word'].replace("##", "")
	color = entity_colors[entity['entity_group']]

	htext += f"{x[prev_end:start]}<mark style='background-color:{color};'>{word}</mark>"
	prev_end = end

	htext += x[prev_end:]

	return {"Severe Reaction": float(scores.numpy()[1]), "Non-severe Reaction": float(scores.numpy()[0])}, local_plot,htext
	# ,{"Contains Medication": float(med), "No Medications": float(1-med)} , {"Contains Symptoms": float(sym), "No Symptoms": float(1-sym)}


	def main(prob1):
	text = str(prob1).lower()
	obj = adr_predict(text)
	return obj[0],obj[1],obj[2]

	title = "🦾 DAVA 🦾"
	description1 = """This app takes text (up to a few sentences) and predicts to what extent the text describes severe (or non-severe) adverse reaction to medications. Please do NOT use for medical diagnosis."""

	with gr.Blocks(title=title) as demo:
	gr.Markdown(f"## {title}")
	gr.Markdown(description1)
	gr.Markdown("""---""")
	prob1 = gr.Textbox(label="Enter Your Text Here:",lines=2, placeholder="Type it here ...")
	submit_btn = gr.Button("Analyze")

	with gr.Row():

	with gr.Column(visible=True) as output_col:
	label = gr.Label(label = "Predicted Label")


	with gr.Column(visible=True) as output_col:
	local_plot = gr.HTML(label = 'Shap:')
	htext = gr.HTML(label="NER")
	# med = gr.Label(label = "Contains Medication")
	# sym = gr.Label(label = "Contains Symptoms")

	submit_btn.click(
	main,
	[prob1],
	[label
	,local_plot, htext
	# , med, sym
	], api_name="adr"
	)

	with gr.Row():
	gr.Markdown("### Click on any of the examples below to see how it works:")
	gr.Examples([["A 35 year-old male had severe headache after taking Aspirin. The lab results were normal."],
	["A 35 year-old female had minor pain in upper abdomen after taking Acetaminophen."]],
	[prob1], [label,local_plot, htext
	# , med, sym
	], main, cache_examples=True)

	demo.launch()