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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 transformers
from transformers import pipeline
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("MiyunKim/Mod4Team5")
model = AutoModelForSequenceClassification.from_pretrained("MiyunKim/Mod4Team5").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()
scores = toech.nn.functional.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])
# Plot the SHAP values using custom color mapping (red for negative, blue for positive)
local_plot = shap.plots.text(shap_values[0], display=False, color=lambda v: 'blue' if v >= 0 else 'red')
# 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': 'yellow',
'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 = "Welcome to **communicADR** 📈"
description1 = """This app takes user inputs and predicts adverse reactions 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 65 year-old male had severe headache after taking Aspirin. The lab results were normal."],
["A 65 year-old female had minor pain in upper abdomen after taking Warfarin."]],
[prob1], [label,local_plot, htext
# , med, sym
], main, cache_examples=True)
demo.launch() |