| 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(share=True) | |