src/PhenoTagger_Demo.py

# -*- 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)