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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 | |
Model used for this demo: https://huggingface.co/marshmellow77/roberta-base-cuad | |
Related blog posts: | |
- https://towardsdatascience.com/how-to-set-up-a-machine-learning-model-for-legal-contract-review-fe3b48b05a0e | |
- https://towardsdatascience.com/how-to-set-up-a-machine-learning-model-for-legal-contract-review-part-2-6ecbbe680ba | |
""" | |
) | |
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=['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() | |
# 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, 'marshmellow77/roberta-base-cuad', | |
n_best_size=10) | |
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)): | |
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") | |