app.py

import streamlit as st
import pandas as pd
import transformers
import re

import zipfile
import postt
from postt import postcor , precor
from transformers import  pipeline, TokenClassificationPipeline, BertForTokenClassification , AutoTokenizer , TextClassificationPipeline , AutoModelForSequenceClassification

st.set_page_config(layout="wide")

st.image("persEsi.png",width = 200)

st.title("Knowledge extraction: EDCs")
st.write("This tool lets you extract relation triples concerning interactions between: endocrine disrupting chemicals, hormones, receptors and cancers. 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.")



  
form = st.form(key='my-form')
x = form.text_area('Enter text', height=200)
submit = form.form_submit_button('Submit')





if submit and len(x) != 0:  
  #model.to("cpu")
  st.write("Execution in progress... Results will be displayed below in a while or can be downloaded from the sidebar, please be patient.")
  
  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", )
  st.caption("Downloading models")
  model_re = AutoModelForSequenceClassification.from_pretrained("dexay/reDs3others", )
  
  token_classifier = pipeline("token-classification", tokenizer = tokenizer,model=model_checkpoint,  )
  
  
  
  if x[-1] not in ".?:":
    x += "."
  
  biotext = precor(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.caption("Entities detected.")
  st.caption("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 = []
  edccanbis = []
  
  for i in range(len(outrelbl)):
    if outrelbl[i] != "other":
      edccanbis += [[lstSentEnt[i][0], lstSentEnt[i][1], outrelbl[i][:-7], lstSentEnc[i], lstSentbilbl[i]]]
      #edccan += [[lstSentEnc[i],lstSentEnt[i][0]+" ["+lstSentbilbl[i][0][2:]+"]", lstSentEnt[i][1]+" ["+lstSentbilbl[i][1][2:]+"]",outrelbl[i][:-7]]]
      
  edccanbis = postcor(edccanbis)
  
  
  
  edccann = []
  edchorm = []
  edcrecep = []
  hormrecep = []
  hormcan = []

  for e in edccanbis:
    if e[-1]== ["B-EDC","B-CANCER"] and e[2] in ["CAUSE","INCREASE_RISK","SPEED_UP","NO_ASSOCIATION"]:
      edccann += [[e[0],e[1],e[2]]]
      edccan += [[e[3],e[0]+" ["+e[-1][0][2:]+"]", e[1]+" ["+e[-1][1][2:]+"]",e[2]]]
      
    elif e[-1]== ["B-EDC","B-HORMONE"] and e[2] in ["ALTER", "INCREASE", "DECREASE", "MIMIC", "INCREASE_ACTIVITY","DECREASE_ACTIVITY"]:
      edchorm += [[e[0],e[1],e[2]]]
      edccan += [[e[3],e[0]+" ["+e[-1][0][2:]+"]", e[1]+" ["+e[-1][1][2:]+"]",e[2]]]
      
    elif e[-1]== ["B-EDC","B-RECEPTOR"] and e[2] in ["BLOCK", "ACTIVATE", "INTERFER"] :
      edcrecep += [[e[0],e[1],e[2]]]
      edccan += [[e[3],e[0]+" ["+e[-1][0][2:]+"]", e[1]+" ["+e[-1][1][2:]+"]",e[2]]]
     
    elif e[-1]== ["B-HORMONE","B-RECEPTOR"] and e[2] in ["BIND"] :
      hormrecep += [[e[0],e[1],e[2]]]
      edccan += [[e[3],e[0]+" ["+e[-1][0][2:]+"]", e[1]+" ["+e[-1][1][2:]+"]",e[2]]]
      
    elif e[-1]== ["B-HORMONE","B-CANCER"] and e[2] in ["DEVELOP"]:
      hormcan += [[e[0],e[1],e[2]]]
      edccan += [[e[3],e[0]+" ["+e[-1][0][2:]+"]", e[1]+" ["+e[-1][1][2:]+"]",e[2]]]
  
  
  edcrecepdf  = pd.DataFrame(edcrecep, columns=["EDC", "RECEPTOR", "RELATION"])
  edccanndf = pd.DataFrame(edccann, columns= ["EDC", "CANCER", "RELATION"] )
  edchormdf = pd.DataFrame(edchorm , columns = ["EDC", "HORMONE", "RELATION"])
  hormrecepdf = pd.DataFrame(hormrecep, columns = ["HORMONE", "RECEPTOR", "RELATION"])
  hormcandf = pd.DataFrame(hormcan, columns = ["HORMONE", "CANCER", "RELATION"])
  
  edccancsv = edccanndf.to_csv('edccan.csv') 
  edcrecepcsv = edcrecepdf.to_csv('edcrecep.csv')
  edchormcsv = edchormdf.to_csv('edchorm.csv')
  hormcancsv = hormcandf.to_csv('hormcan.csv')
  hormrecepcsv = hormrecepdf.to_csv('hormrecep.csv')
  
  
  

   
  edccandf = pd.DataFrame(edccan, columns= ["Sentence", "Entity 1", "Entity 2", "Relation"] )
  
  edccandf.to_csv("table.csv")
  
  
  with zipfile.ZipFile("allcsvs.zip", "w") as zipf:
    if len(edccan)!=0:
      zipf.write('table.csv')
    if len(edccann)!=0:
      zipf.write('edccan.csv')
      
    if len(edcrecep)!=0:
       zipf.write('edcrecep.csv')
    if len(edchorm)!=0:
       zipf.write('edchorm.csv')
    if len(hormcan)!=0:
      zipf.write('hormcan.csv')
    if len(hormrecep)!=0:
      zipf.write('hormrecep.csv')
      
      
  zipf.close()
  
  st.table(edccandf)
  csv = edccandf.to_csv(index=False).encode('utf-8')
  with st.sidebar:
    st.subheader("You can only choose one download !!")
    st.caption("we recommed ZIP file.")
    st.write("Download table only :")
    st.download_button(
         label="Download CSV",
         data=csv,
         file_name='Relation_triples_table.csv',
         mime='text/csv',
    )
    st.write("Download table plus separate csvs for each family of pairs :")
    with open("allcsvs.zip", "rb") as fp:
      btn = st.download_button(
          label="Download ZIP",
          data=fp,
          file_name="SeparateCsvs.zip",
          mime="application/zip"
      )