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")