Create app.py

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+import nltk
+import pandas as pd
+nltk.download('punkt')
+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'))
+
+def score_fincat(txt):
+  li = []
+  highlight = []
+  for word in txt.split():
+    if any(char.isdigit() for char in word):
+      if word[-1] in ['.', ',', ';', ":", "-", "!", "?", ")", '"', "'"]:
+        word = word[:-1]
+      st = txt.index(word)
+      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)
+      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'))
+      li.append([word,'    In-claim' if prediction==1 else 'Out-of-Claim', prediction_probability])
+    else:
+      highlight.append((word, '    '))
+  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()