Update app.py

app.py

@@ -8,13 +8,27 @@ import pickle
 lr_clf = pickle.load(open("lr_clf_FiNCAT.pickle",'rb'))
 
 def score_fincat(txt):
+  '''
+  Extracts numerals from financial texts and checks if they are in-claim or out-of claim
+
+    Parameters:
+      txt (str): Financial Text. This is to be given as input. Numerals present in this text will be evaluated.
+
+    Returns:
+      highlight (list): A list each element of which is a tuple. Each tuple has two elements i) word ii) whether the word is in-claim or out-of-claim.
+      dff (pandas dataframe): A pandas dataframe having three columns 'numeral', 'prediction' (whether the word is in-claim or out-of-claim) and 'probability' (probabilty of the prediction).
+  '''
   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.index(word)
+      st = txt.index(" " + word + k + " ")+1
+      k = ''
       ed = st + len(word)
       x = {'paragraph' : txt, 'offset_start':st, 'offset_end':ed}
       context_text = extract_context_words(x)
@@ -28,8 +42,8 @@ def score_fincat(txt):
   headers = ['numeral', 'prediction', 'probability']
   dff = pd.DataFrame(li)
   dff.columns = headers
-  
   return highlight, dff
 
+
 iface = gr.Interface(fn=score_fincat, inputs=gr.inputs.Textbox(lines=5, placeholder="Enter Financial Text here..."), title="FiNCAT-2",description="Financial Numeral Claim Analysis Tool (Enhanced)", outputs=["highlight", "dataframe"], allow_flagging="never", examples=["In the year 2021, the markets were bullish. We expect to boost our sales by 80% this year.", "Last year our profit was $2.2M. This year it will increase to $3M"])
 iface.launch()