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()