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| import collections | |
| import math | |
| import re | |
| import string | |
| import streamlit as st | |
| from transformers.pipelines import pipeline | |
| import json | |
| import sys | |
| from predict import run_prediction | |
| import random | |
| from io import StringIO | |
| import requests | |
| import boto3 | |
| from transformers import ( | |
| AutoConfig, | |
| AutoModelForQuestionAnswering, | |
| AutoTokenizer, | |
| squad_convert_examples_to_features | |
| ) | |
| from transformers.data.processors.squad import SquadResult, SquadV2Processor, SquadExample | |
| from transformers.data.metrics.squad_metrics import compute_predictions_logits | |
| import gradio as gr | |
| import json | |
| import torch | |
| import time | |
| from torch.utils.data import DataLoader, RandomSampler, SequentialSampler | |
| st.set_page_config(layout="wide") | |
| st.cache(show_spinner=False, persist=True) | |
| def load_questions(): | |
| questions = [] | |
| with open('data/questions.txt') as f: | |
| questions = f.readlines() | |
| # questions = [] | |
| # for i, q in enumerate(data['data'][0]['paragraphs'][0]['qas']): | |
| # question = data['data'][0]['paragraphs'][0]['qas'][i]['question'] | |
| # questions.append(question) | |
| return questions | |
| st.cache(show_spinner=False, persist=True) | |
| def load_contracts(): | |
| with open('data/test.json') as json_file: | |
| data = json.load(json_file) | |
| contracts = [] | |
| for i, q in enumerate(data['data']): | |
| contract = ' '.join(data['data'][i]['paragraphs'][0]['context'].split()) | |
| contracts.append(contract) | |
| return contracts | |
| questions = load_questions() | |
| # contracts = load_contracts() | |
| ### DEFINE SIDEBAR | |
| st.sidebar.title("Interactive Contract Analysis") | |
| st.sidebar.markdown( | |
| """ | |
| Process text with [Huggingface](https://huggingface.co) models and visualize the results. | |
| This model uses a pretrained snapshot trained on the [Atticus](https://www.atticusprojectai.org/) Dataset - CUAD | |
| """ | |
| ) | |
| st.sidebar.header("Contract Selection") | |
| # select contract | |
| contracts_drop = ['contract 1', 'contract 2', 'contract 3'] | |
| contracts_files = ['contract-1.txt', 'contract-2.txt', 'contract-3.txt'] | |
| contract = st.sidebar.selectbox('Please Select a Contract', contracts_drop) | |
| idx = contracts_drop.index(contract) | |
| with open('data/'+contracts_files[idx-1]) as f: | |
| contract_data = f.read() | |
| # upload contract | |
| user_upload = st.sidebar.file_uploader('Please upload your own', type=['docx', 'pdf', 'txt'], accept_multiple_files=False) | |
| print(user_upload) | |
| # 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() | |
| elif extension == 'pdf': | |
| import PyPDF4 | |
| try: | |
| # Extracting Text from PDFs | |
| pdfReader = PyPDF4.PdfFileReader(user_upload) | |
| print(pdfReader.numPages) | |
| contract_data = '' | |
| for i in range(0,pdfReader.numPages): | |
| print(i) | |
| pageobj = pdfReader.getPage(i) | |
| contract_data = contract_data + pageobj.extractText() | |
| except: | |
| st.warning('Unable to read PDF, please try another file') | |
| elif extension == 'docx': | |
| import docx2txt | |
| contract_data = docx2txt.process(user_upload) | |
| 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") | |
| st.write("This demo uses the CUAD dataset for Contract Understanding.") | |
| paragraph = st.text_area(label="Contract",value=contract_data,height=400) | |
| question = st.selectbox('Choose one of the 41 queries from the CUAD dataset:', questions) | |
| 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, '../models/roberta-base/') | |
| data = {} | |
| data['question']=[question] | |
| data['context']=paragraph | |
| print(data) | |
| predictions = run_prediction(data['question'], data['context'], 'akdeniz27/roberta-base-cuad', | |
| n_best_size=int(number_results)) | |
| # print(resp) | |
| # predictions=resp.json() | |
| # print(predictions) | |
| if predictions['0'] == "": | |
| answer = 'No answer found in document' | |
| else: | |
| if number_results == '1': | |
| answer = predictions['0'] | |
| st.text_area(label="Answer", value=f"{answer}") | |
| else: | |
| f = open("nbest.json") | |
| st.success(f.readlines()) | |
| st.success("Successfully processed contract!") | |
| else: | |
| st.write("Unable to call model, please select question and contract") | |