| 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 |
