app.py

import streamlit as st
import pandas as pd
import transformers
from transformers import  pipeline, TokenClassificationPipeline, BertForTokenClassification , AutoTokenizer , TextClassificationPipeline , AutoModelForSequenceClassification

st.header("Knowledge extraction on Endocrine disruptors")
st.text("This tool lets you extract relation triples concerning interactions between: endocrine disrupting chemicals, hormones, receptors and cancers.")
st.text("It is the result of an end of studies project within ESI school and dedicated to biomedical researchers looking to extract precise information about the subject without digging into long publications.")
x = st.text_area('Entre you text on EDCs:')

if x[-1] not in ".?:":
  x += "."
#model.to("cpu")
tokenizer = AutoTokenizer.from_pretrained("dmis-lab/biobert-large-cased-v1.1", truncation = True, padding=True, model_max_length=512,)
model_checkpoint = BertForTokenClassification.from_pretrained("dexay/Ner2HgF", )


model_re = AutoModelForSequenceClassification.from_pretrained("dexay/reDs3others", )
token_classifier = pipeline("token-classification", tokenizer = tokenizer,model=model_checkpoint,  )

st.text("Knowledge extraction is in progress ...")

biotext = x

#split document or text into sentences

lstbiotext = []

flag = 0
tempsen = ""
for e in biotext:
  tempsen += e
  if e=="(":
      flag = 1
  if e==")":
      flag = 0
  if (e =="." or e =="?" or e ==":" ) and flag == 0 :
      lstbiotext += [tempsen.strip()]
      tempsen = ""

ddata = lstbiotext

#tokenized_dat = tokenize_function(ddata) 

az = token_classifier(ddata)


#code to convert NER output to  RE input compatible format

#tg_inorder are decoding of labels on which the model was fine tuned on 

tg_inorder = ['O',
 'B-HORMONE',
 'B-EXP_PER',
 'I-HORMONE',
 'I-CANCER',
 'I-EDC',
 'B-RECEPTOR',
 'B-CANCER',
 'I-RECEPTOR',
 'B-EDC',
 'PAD']

lstSentEnc = []
lstSentbilbl = []
lstSentEnt = []
for itsent in az:
  
  sentaz = itsent
  ph = []
  phl = []
  for e in sentaz:
    if e["word"][0]=="#" and len(ph)!=0:
      ph[-1]+= e["word"][2:]
    else:
      ph += [e["word"]]
      phl += [e["entity"]]


  phltr = []
  for e in phl:
    phltr += [tg_inorder[int(e[-1])] if len(e)==7 else  tg_inorder[int(e[-2:])]]
  

  nwph = []
  nwphltr = []
  flag = 0
  for i in range(len(phltr)-2):
    if phltr[i]=="O" and flag != 3 :
      nwph += [ph[i]]
      nwphltr += [phltr[i]]
      continue
    elif flag == 3:
      nwph[-1] += " "+ph[i]
      flag = 1
      continue
    elif phltr[i][2:]==phltr[i+1][2:] and phltr[i+1][0]=="I" and flag == 0:
      nwph += [ph[i]]
      nwphltr += [phltr[i]]
      flag = 1
      continue
    elif phltr[i][2:]==phltr[i+1][2:] and phltr[i+1][0]=="I" and flag == 1:
      nwph[-1] += " "+ph[i]
      continue
# xox with flag == 3
    elif phltr[i][2:]==phltr[i+2][2:] and phltr[i+1]=="O" and phltr[i+2][0]=="I" and flag == 0:
      nwph += [ph[i]]
      nwphltr += [phltr[i]]
      flag = 3
      continue
    elif phltr[i][2:]==phltr[i+2][2:] and phltr[i+1]=="O" and phltr[i+2][0]=="I" and flag == 1:
      nwph[-1] += " "+ph[i]
      flag = 3
      continue
#\ xox
    elif flag == 1:
      nwph[-1] += " "+ph[i]
      flag = 0
      continue
    else :
      nwph += [ph[i]]
      nwphltr += [phltr[i]]
      continue
      

  # nwph,nwphltr,len(nwph),len(nwphltr)
  

  if nwphltr.count("O") <= len(nwphltr)-2:
    for i in range(len(nwph)-1):
      if nwphltr[i] != "O":
        for j in range(i,len(nwph)):
          if nwphltr[j] != "O" and nwphltr[j] != nwphltr[i] and {nwphltr[j], nwphltr[i]} != {"B-CANCER","B-RECEPTOR"}:
            sen2ad = ""
            for g in range(i):
              sen2ad += nwph[g]+" "
            sen2ad += "<e1>"+nwph[i]+"</e1> "

            for t in range(i+1,j):
              sen2ad += nwph[t]+" "
            sen2ad += "<e2>"+nwph[j]+"</e2>"
            if j<len(nwph):
              for l in range(j+1,len(nwph)):
                sen2ad += " "+nwph[l]
            lstSentEnc += [sen2ad]
            lstSentbilbl += [[nwphltr[i],nwphltr[j]]]
            lstSentEnt += [[nwph[i],nwph[j]]]
      


#lstSentEnc,lstSentEnt,lstSentbilbl

st.text("Entities detected, Next: Relation detection ...")


# Relation extraction part

token_classifier = pipeline("text-classification", tokenizer = tokenizer,model=model_re, 
)

rrdata = lstSentEnc



outre = token_classifier(rrdata)


trLABELS = ['INCREASE_RISK(e1,e2)',
 'SPEED_UP(e2,e1)',
 'DECREASE_ACTIVITY(e1,e2)',
 'NO_ASSOCIATION(e1,e2)',
 'DECREASE(e1,e2)',
 'BLOCK(e1,e2)',
 'CAUSE(e1,e2)',
 'ACTIVATE(e2,e1)',
 'DEVELOP(e2,e1)',
 'ALTER(e1,e2)',
 'INCREASE_RISK(e2,e1)',
 'SPEED_UP(e1,e2)',
 'INTERFER(e1,e2)',
 'DECREASE(e2,e1)',
 'NO_ASSOCIATION(e2,e1)',
 'INCREASE(e2,e1)',
 'INTERFER(e2,e1)',
 'ACTIVATE(e1,e2)',
 'INCREASE(e1,e2)',
 'MIMIC(e1,e2)',
 'MIMIC(e2,e1)',
 'BLOCK(e2,e1)',
 'other',
 'BIND(e2,e1)',
 'INCREASE_ACTIVITY(e2,e1)',
 'ALTER(e2,e1)',
 'CAUSE(e2,e1)',
 'BIND(e1,e2)',
 'DEVELOP(e1,e2)',
 'DECREASE_ACTIVITY(e2,e1)']



outrelbl = []
for e in outre:
  outrelbl += [trLABELS[int(e['label'][-1])] if len(e["label"])==7 else trLABELS[int(e['label'][-2:])] ]

for i in range(len(outrelbl)):
  if "(e2,e1)" in outrelbl[i]:
    lstSentbilbl[i][0],lstSentbilbl[i][1] = lstSentbilbl[i][1],lstSentbilbl[i][0]
    lstSentEnt[i][0],lstSentEnt[i][1] = lstSentEnt[i][1],lstSentEnt[i][0]


edccan = []


for i in range(len(outrelbl)):
  if outrelbl[i] != "other":
    edccan += [[lstSentEnc[i],lstSentEnt[i][0], lstSentEnt[i][1],lstSentbilbl[i][0]+" "+outrelbl[i][:-7]+" "+lstSentbilbl[i][1]]]
 
edccandf = pd.DataFrame(edccan, columns= ["Sentence", "Entity 1", "Entity 2", "Relation"] )

if x:
  out = token_classifier(x)
  st.table(edccandf)