app.py

from predict import run_prediction
from io import StringIO
import json
import gradio as gr
import spacy
from spacy import displacy
from transformers import AutoTokenizer, AutoModelForTokenClassification,RobertaTokenizer,pipeline
import torch
import nltk
from nltk.tokenize import sent_tokenize
from fin_readability_sustainability import BERTClass, do_predict
import pandas as pd
import en_core_web_sm
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'))

nlp = en_core_web_sm.load()
nltk.download('punkt')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

#SUSTAINABILITY STARTS
tokenizer_sus = RobertaTokenizer.from_pretrained('roberta-base')
model_sustain = BERTClass(2, "sustanability")
model_sustain.to(device)
model_sustain.load_state_dict(torch.load('sustainability_model.bin', map_location=device)['model_state_dict'])

def get_sustainability(text):
  df = pd.DataFrame({'sentence':sent_tokenize(text)})
  actual_predictions_sustainability = do_predict(model_sustain, tokenizer_sus, df)
  highlight = []
  for sent, prob in zip(df['sentence'].values, actual_predictions_sustainability[1]):
    if prob>=4.384316:
      highlight.append((sent, 'non-sustainable'))
    elif prob<=1.423736:
      highlight.append((sent, 'sustainable'))
    else:
      highlight.append((sent, '-'))
  return highlight
#SUSTAINABILITY ENDS

#CLAIM STARTS
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'):
          highlight.append(('None', '    '))
          dff = pd.DataFrame([['None', 'None', 'None'])
          headers = ['numeral', 'prediction', 'probability']
          dff.columns = headers
          return highlight, dff
      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


##Summarization
summarizer = pipeline("summarization", model="knkarthick/MEETING_SUMMARY") 
def summarize_text(text):
    resp = summarizer(text)
    stext = resp[0]['summary_text']
    return stext


def split_in_sentences(text):
    doc = nlp(text)
    return [str(sent).strip() for sent in doc.sents]
def make_spans(text,results):
    results_list = []
    for i in range(len(results)):
        results_list.append(results[i]['label'])
    facts_spans = []
    facts_spans = list(zip(split_in_sentences(text),results_list))
    return facts_spans    
##Forward Looking Statement
fls_model = pipeline("text-classification", model="yiyanghkust/finbert-fls", tokenizer="yiyanghkust/finbert-fls")
def fls(text):
    results = fls_model(split_in_sentences(text))
    return make_spans(text,results) 
    
##Company Extraction
ner=pipeline('ner',model='Jean-Baptiste/camembert-ner-with-dates',tokenizer='Jean-Baptiste/camembert-ner-with-dates', aggregation_strategy="simple")
def fin_ner(text):
    replaced_spans = ner(text)
    return replaced_spans  
    
     
#CUAD STARTS    
def load_questions():
    questions = []
    with open('questions.txt') as f:
        questions = f.readlines()
    return questions


def load_questions_short():
    questions_short = []
    with open('questionshort.txt') as f:
        questions_short = f.readlines()
    return questions_short

def quad(query,file):
    with open(file.name) as f:
        paragraph = f.read()
    questions = load_questions()
    questions_short = load_questions_short()
    if (not len(paragraph)==0) and not (len(query)==0):
        print('getting predictions')
    predictions = run_prediction([query], paragraph, 'marshmellow77/roberta-base-cuad',n_best_size=5)
    answer = ""
    if predictions['0'] == "":
        answer = 'No answer found in document'
    else:
        with open("nbest.json") as jf:
            data = json.load(jf)
            for i in range(1):
                raw_answer=data['0'][i]['text']
                answer += f"Answer {i+1}: {data['0'][i]['text']} -- \n"
                answer += f"Probability: {round(data['0'][i]['probability']*100,1)}%\n\n"
    #summarizer = pipeline("summarization", model="knkarthick/MEETING_SUMMARY")
    #resp = summarizer(answer)
    #stext = resp[0]['summary_text']
    
   # highlight,dff=score_fincat(answer)
    return answer,summarize_text(answer),score_fincat(answer),get_sustainability(answer),fls(answer)    
                
                   
# b6 = gr.Button("Get Sustainability")
              #b6.click(get_sustainability, inputs = text, outputs = gr.HighlightedText())
              
              
#iface = gr.Interface(fn=get_sustainability, inputs="textbox", title="CONBERT",description="SUSTAINABILITY TOOL", outputs=gr.HighlightedText(), allow_flagging="never")
#iface.launch()

iface = gr.Interface(fn=quad, inputs=[gr.inputs.Textbox(label='SEARCH QUERY'),gr.inputs.File(label='TXT FILE')], title="CONBERT",description="SUSTAINABILITY TOOL",article='Article', outputs=[gr.outputs.Textbox(label='Answer'),gr.outputs.Textbox(label='Summary'),gr.HighlightedText(label='Claims'),gr.HighlightedText(label='SUSTAINABILITY'),gr.HighlightedText(label='FLS')], allow_flagging="never")


iface.launch()