Update app.py

app.py

@@ -1,12 +1,15 @@
 import streamlit as st
 import transformers
-from transformers import  pipeline, TokenClassificationPipeline, BertForTokenClassification , AutoTokenizer
+from transformers import  pipeline, TokenClassificationPipeline, BertForTokenClassification , AutoTokenizer , TextClassificationPipeline , AutoModelForSequenceClassification
 
 x = st.text_area('enter')
 
 #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", truncation = True, padding=True, model_max_length=512,)
 token_classifier = pipeline("token-classification", tokenizer = tokenizer,model=model_checkpoint,  )
 
 
@@ -140,9 +143,71 @@ for itsent in az:
 
 #lstSentEnc,lstSentEnt,lstSentbilbl
 
+# 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]]]
+
 if x:
   out = token_classifier(x)
-  st.markdown(lstSentEnc)
+  st.markdown(edccan)