Upload score_fincat.py

score_fincat.py

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+import gradio as gr
+import nltk
+from fincat_utils import extract_context_words
+from fincat_utils import bert_embedding_extract
+import pickle
+lr_clf = pickle.load(open("lr_clf_FiNCAT.pickle",'rb'))
+nltk.download('punkt')
+
+def score_fincat(txt):
+  li = []
+  highlight = []
+  txt = " " + txt + " "
+  k = ''
+  for word in txt.split():
+    if any(char.isdigit() for char in word):
+      if word[-1] in ['.', ',', ';', ":", "-", "!", "?", ")", '"', "'"]:
+        k = word[-1]
+        word = word[:-1]
+      st = txt.find(" " + word + k + " ")+1
+      k = ''
+      ed = st + len(word)
+      x = {'paragraph' : txt, 'offset_start':st, 'offset_end':ed}
+      context_text = extract_context_words(x)
+      features = bert_embedding_extract(context_text, word)
+      if(features[0]=='None'):
+          continue
+      prediction = lr_clf.predict(features.reshape(1, 768))
+      prediction_probability = '{:.4f}'.format(round(lr_clf.predict_proba(features.reshape(1, 768))[:,1][0], 4))
+      highlight.append((word, '    In-claim' if prediction==1 else 'Out-of-Claim'))
+    else:
+      continue
+  if(len(highlight)<1):
+      highlight.append((txt,'None'))     
+  return highlight