app.py

import numpy as np
import pandas as pd
import pickle

import gradio as gr

import torch

import math

from transformers import AutoTokenizer, AutoModel

import transformers

import re

mlp = pickle.load(open("MLP_over_embeddings.pickle", "rb"))

tokenizer = AutoTokenizer.from_pretrained("nlpaueb/sec-bert-num")
model = AutoModel.from_pretrained('nlpaueb/sec-bert-num')

"""# Input here"""

def convert_actual_to_num(text, number, offset):
  length = len(str(number))
  offset = int(offset)
  new_text= text[:offset] + " [NUM] " + text[offset+length:]
  return new_text

def num_detector_highlighter_adv(text):
  num_posn = []
  posn = -1
  num = ""
  text = text + "  "
  others = ""
  for i in range(len(text)-2):
      if (text[i].isdigit() and text[i+1].isdigit()) or (text[i].isdigit() and text[i+1]=="." and text[i+2].isdigit()):
        num = num + str(text[i])
        if posn == -1:
          posn = i
        if others!="":
          num_posn.append((others,""))
          others = ""
      elif (text[i].isdigit() and text[i+1].isdigit()==False and text[i+1]!=".") or (text[i].isdigit() and text[i+1]=="." and text[i].isdigit() and text[i+2].isdigit()==False):
        num = num + str(text[i])
        if len(num)==1:
          posn = i
        if others!="":
          num_posn.append((others,""))
          others = ""  
        num_posn.append((str(num), "@POSITION " + str(posn)))
        num = ""
        posn = -1
      elif text[i] == ".":
        if text[i+1].isdigit():
          num = num + str(text[i])
        else:
          others = others + str(text[i])
      elif text[i]!=' ':
        others = others + str(text[i])
      elif text[i]==" ":
         if others!="" and others!=" ":
          num_posn.append((others,""))
          others = ""
  if others!="":
    num_posn.append((others,""))
  #print(num_posn)
  return num_posn

def exnum_evaluator(df):
  df['preprocessed_text'] = df.apply(lambda x: convert_actual_to_num(x.text, x.number, x.position), axis = 1)
  df['number_processed'] = df['number'].apply(lambda x: str(x)[0:str(x).index(".")+2] if "." in str(x) else str(x))
  #preprocessed_text = convert_actual_to_num(raw_text,number,offset)
  all_preds = []
  for preprocessed_text in df["preprocessed_text"].values:
    tokenized_text = tokenizer.tokenize(preprocessed_text)

    indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
    index = tokenized_text.index('[NUM]')
    tokens_tensor = torch.tensor([indexed_tokens])

    model.eval()
    with torch.no_grad():
      last_hidden_states = model(tokens_tensor)[0] 

    embedding_of_num = last_hidden_states[:,index,:]
    embedding_of_num_use = list(embedding_of_num[0].cpu().detach().numpy())
    pred = mlp.predict([embedding_of_num_use])[0]
    all_preds.append(pred)
  df['pred'] = all_preds
  df['calculated_magnitude'] = df['number_processed'].apply(lambda x : min(6,int(math.log10(float(x)))+1)) # restric upto 2 dp in x if decimal
  df["prediction"] =  np.where((df['calculated_magnitude'] != df['pred']), "Exaggerated", "Non-Exaggerated") #df.apply(lambda x : "Exaggerated" if x.calculated_magnitude!=x.prediction else "Non-Exaggerated", axis=1)
  return df[["number", "position", "prediction"]]#, "text", "preprocessed_text",'number_processed', "pred", "calculated_magnitude"]]

def change_checkbox_group(text2):
  num_posn_inp = [(num, posn) for (num,posn) in eval(text2) if posn!=""]
  num_posn_dislay = [str(num) + " " + str(posn) for (num,posn) in num_posn_inp]
  return gr.CheckboxGroup.update(choices = num_posn_dislay, label="Numerals", visible=True, value=num_posn_dislay)

def combined_fns(text, text2, choices=[]):
  num_posn_inp =  [(num, posn) for (num,posn) in eval(text2) if posn!=""]#[(num, posn) for (num,posn) in num_detector_highlighter_adv(text) if posn!=""]
  #num_posn_dislay = [str(num) + " " + str(posn) for (num,posn) in num_posn]
  df = pd.DataFrame({"text": [text]*len(num_posn_inp), "number" : [i[0] for i in num_posn_inp], "position" : [i[1].replace("@POSITION ", "") for i in num_posn_inp]})
  df['num_position'] = [str(num) + " " + str(posn) for (num,posn) in num_posn_inp]
  if len(choices)>0:
    df = df[df['num_position'].isin(choices)]
  return exnum_evaluator(df)

#examples
def set_example_text(example_text):
    return gr.Textbox.update(value=example_text[0])

demo = gr.Blocks(theme=gr.themes.Soft())

with demo:
    gr.Markdown("# **Financial Exaggerated Numeral ClassifiEr (FENCE)**")
    with gr.Row():
      with gr.Column():
        text = gr.components.Textbox(label="Enter financial text here", lines=2, placeholder="Enter Financial Text here...")
        b1 = gr.Button("Get numerals present in the entered text")
        b1.click(num_detector_highlighter_adv, inputs = text, outputs = gr.HighlightedText(label='Numerals present in the text'))
        text2 = gr.components.Textbox(visible=False)
        b1.click(num_detector_highlighter_adv, inputs = text, outputs =text2)
        with gr.Row():
          with gr.Tabs():
            with gr.TabItem("All numerals"):
              b2 = gr.Button("Predict for all numerals")
              b2.click(combined_fns, inputs = [text, text2], outputs = gr.DataFrame())
            with gr.TabItem("Specific numerals"):
              b3 = gr.Button("Get option to select numerals")
              num_posn_inp_ckbx = gr.CheckboxGroup(choices = [], interactive=True, label='Specific Numerals')
              b3.click(change_checkbox_group, inputs=text2, outputs=num_posn_inp_ckbx)
              b4 = gr.Button("Predict for specific numerals")
              b4.click(combined_fns, inputs = [text, text2, num_posn_inp_ckbx], outputs = gr.DataFrame())
        example_text = gr.Dataset(components=[text], samples=[["Get 30% off Gap denim whilst recycling your old denim for communities in need"], ["	Matthew Perry puts Malibu mansion on the market for $13.5 million"], ["Anton Art Center in Mt. Clemens hosts 19th Annual ArtParty Fundraiser - Twilight in the Tropics"], ["Black Friday Sales! - Vegas hotel packages 50% savings from Southwest Vacations"]])
        example_text.click(fn=set_example_text,
                             inputs=example_text,
                             outputs=example_text.components)
        gr.Markdown("<sub><sup>How to use? [link](https://github.com/sohomghosh/FENCE_Financial_Exaggerated_Numeral_ClassifiEr/blob/main/README.md), Warning: User discretion is advised.</sup></sub>")
demo.launch()