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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") | |