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import streamlit as st
from predict import run_prediction
from io import StringIO
import json
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
def load_questions_short():
questions_short = []
with open('data/questions_short.txt') as f:
questions_short = 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_short
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()
questions_short = load_questions_short()
# 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]) 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=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]
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!")
st.success(question)
else:
st.write("Unable to call model, please select question and contract")
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