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# -*- coding: utf-8 -*-
"""
Created on Mon Nov 21 15:17:49 2022
@author: luol2
"""
import streamlit as st
import argparse
from nn_model import bioTag_CNN,bioTag_BERT,bioTag_Bioformer
from dic_ner import dic_ont
from tagging_text import bioTag
import os
import time
import json
import sys
st.set_page_config(
page_title="PhenoTagger",
page_icon=":shark:",
# layout="wide",
initial_sidebar_state="expanded",
menu_items={
'Get Help': 'https://www.extremelycoolapp.com/help',
'Report a bug': "https://www.extremelycoolapp.com/bug",
'About': "# This is a header. This is an *extremely* cool app!"
}
)
st.title('PhenoTagger Demo')
# with st.spinner('Model is being loaded..'):
# print('load model done!')
with st.form(key="my_form"):
@st.cache(allow_output_mutation=True)
def load_model():
ontfiles={'dic_file':'../dict_new/noabb_lemma.dic',
'word_hpo_file':'../dict_new/word_id_map.json',
'hpo_word_file':'../dict_new/id_word_map.json'}
# if para_set['model_type']=='cnn':
# vocabfiles={'w2vfile':'../vocab/bio_embedding_intrinsic.d200',
# 'charfile':'../vocab/char.vocab',
# 'labelfile':'../dict_new/lable.vocab',
# 'posfile':'../vocab/pos.vocab'}
# modelfile='../models/cnn_p5n5_b128_95_hponew1.h5'
# elif para_set['model_type']=='bioformer':
vocabfiles={'labelfile':'../dict_new/lable.vocab',
'config_path':'../vocab/bioformer-cased-v1.0/bert_config.json',
'checkpoint_path':'../vocab/bioformer-cased-v1.0/bioformer-cased-v1.0-model.ckpt-2000000',
'vocab_path':'../vocab/bioformer-cased-v1.0/vocab.txt'}
modelfile='../models/bioformer_p5n5_b64_1e-5_95_hponew3.h5'
# else:
# print('Model type is wrong, please select cnn or bioformer.')
# sys.exit()
biotag_dic=dic_ont(ontfiles)
# if para_set['model_type']=='cnn':
# nn_model=bioTag_CNN(vocabfiles)
# nn_model.load_model(modelfile)
# elif para_set['model_type']=='bioformer':
nn_model=bioTag_Bioformer(vocabfiles)
session=nn_model.load_model(modelfile)
test_tag='1232'
return nn_model,biotag_dic,test_tag,session
#hyper-parameter
st.sidebar.header("Hyperparameter Settings")
sbform = st.sidebar.form("Hyper-paramiters")
# para_model=sbform.selectbox('Model', ['cnn', 'bioformer'])
para_overlap=sbform.selectbox('Return overlapping concepts', ['True', 'False'])
para_abbr=sbform.selectbox('Identify abbreviations', ['True', 'False'])
para_threshold = sbform.slider('Threshold:', min_value=0.5, max_value=0.95, value=0.95, step=0.05)
sbform.form_submit_button("Setting")
st.write('parameters:', para_overlap,para_abbr,para_threshold)
nn_model,biotag_dic,test_tag,session=load_model()
input_text = st.text_area(
"Paste your text below (max 500 words)",
height=510,
)
MAX_WORDS = 500
import re
res = len(re.findall(r"\w+", input_text))
if res > MAX_WORDS:
st.warning(
"⚠️ Your text contains "
+ str(res)
+ " words."
+ " Only the first 500 words will be reviewed. Stay tuned as increased allowance is coming! 😊"
)
input_text = input_text[:MAX_WORDS]
submit_button = st.form_submit_button(label="✨ Get me the data!")
if para_overlap=='True':
para_overlap=True
else:
para_overlap=False
if para_abbr=='True':
para_abbr=True
else:
para_abbr=False
para_set={
#model_type':para_model, # cnn or bioformer
'onlyLongest':para_overlap, # False: return overlap concepts, True only longgest
'abbrRecog':para_abbr,# False: don't identify abbr, True: identify abbr
'ML_Threshold':para_threshold,# the Threshold of deep learning model
}
if not submit_button:
st.stop()
st.markdown(f"""**Results:**\n""")
# print('dic...........:',biotag_dic.keys())
print('........:',test_tag)
print('........!!!!!!:',input_text)
print('...input:',input_text)
tag_result=bioTag(session,input_text,biotag_dic,nn_model,onlyLongest=para_set['onlyLongest'], abbrRecog=para_set['abbrRecog'],Threshold=para_set['ML_Threshold'])
for ele in tag_result:
start = ele[0]
last = ele[1]
mention = input_text[int(ele[0]):int(ele[1])]
type='Phenotype'
id=ele[2]
score=ele[3]
output=start+"\t"+last+"\t"+mention+"\t"+id+"\n"
st.info(output)
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