app.py

import streamlit as st
from predict import run_prediction
from io import StringIO
import json
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
nlp = en_core_web_sm.load()
nltk.download('punkt')
#nlp = spacy.load("en_core_web_sm")

st.set_page_config(layout="wide")
st.cache(show_spinner=False, persist=True)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#SUSTAIN 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

#SUSTAIN ENDS

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

##Forward Looking Statement
#def fls(text):
   # fls_model = pipeline("text-classification", model="yiyanghkust/finbert-fls", tokenizer="yiyanghkust/finbert-fls")
  #  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  
    
     
      
        
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


st.cache(show_spinner=False, persist=True)


questions = load_questions()
questions_short = load_questions_short()

### DEFINE SIDEBAR
st.sidebar.title("Interactive Contract Analysis")

st.sidebar.header('CONTRACT UPLOAD')

# upload contract
user_upload = st.sidebar.file_uploader('Please upload your contract', type=['txt'],
                                       accept_multiple_files=False)


# process upload
if user_upload is not None:
    print(user_upload.name, user_upload.type)
    extension = user_upload.name.split('.')[-1].lower()
    if extension == 'txt':
        print('text file uploaded')
         # To convert to a string based IO:
        stringio = StringIO(user_upload.getvalue().decode("utf-8"))

        # To read file as string:
        contract_data = stringio.read()
    else:
        st.warning('Unknown uploaded file type, please try again')

results_drop = ['1', '2', '3']
number_results = st.sidebar.selectbox('Select number of results', results_drop)

### DEFINE MAIN PAGE
st.header("Legal Contract Review Demo")
paragraph = st.text_area(label="Contract", value=contract_data, height=300)

questions_drop = questions_short
question_short = st.selectbox('Choose one of the 41 queries from the CUAD dataset:', questions_drop)
idxq = questions_drop.index(question_short)
question = questions[idxq]


raw_answer=""
if st.button('Analyze'):
    if (not len(paragraph)==0) and not (len(question)==0):
        print('getting predictions')
        with st.spinner(text='Analysis in progress...'):
            predictions = run_prediction([question], paragraph, 'marshmellow77/roberta-base-cuad',
                                         n_best_size=5)
        answer = ""
        if predictions['0'] == "":
            answer = 'No answer found in document'
        else:
            # if number_results == '1':
            #     answer = f"Answer: {predictions['0']}"
            #     # st.text_area(label="Answer", value=f"{answer}")
            # else:
            answer = ""
            with open("nbest.json") as jf:
                data = json.load(jf)
                for i in range(int(number_results)):
                    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"
        st.success(answer)

    else:
        st.write("Unable to call model, please select question and contract")
        
if st.button('Check Sustainability'):
    if(raw_answer==""):
        st.write("Unable to call model, please select question and contract")
    else:
        st.write(get_sustainability(raw_answer))
if st.button('Summarize'):
    if(raw_answer==""):
        st.write("Unable to call model, please select question and contract")
    else:
        st.write(summarize_text(raw_answer))
        
if st.button('NER'):
    if(raw_answer==""):
        st.write("Unable to call model, please select question and contract")
    else:
        doc = nlp(raw_answer)
        st.write(displacy.render(doc, style="ent"))