from transformers import AutoModelForQuestionAnswering, AutoTokenizer import streamlit as st import json from predict import run_prediction st.set_page_config(layout="wide") model_list = ['akdeniz27/roberta-base-cuad', 'akdeniz27/roberta-large-cuad', 'akdeniz27/deberta-v2-xlarge-cuad'] st.sidebar.header("Select CUAD Model") model_checkpoint = st.sidebar.radio("", model_list) if model_checkpoint == "akdeniz27/deberta-v2-xlarge-cuad": import sentencepiece st.sidebar.write("Project: https://www.atticusprojectai.org/cuad") st.sidebar.write("Git Hub: https://github.com/TheAtticusProject/cuad") st.sidebar.write("CUAD Dataset: https://huggingface.co/datasets/cuad") st.sidebar.write("License: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/") @st.cache(allow_output_mutation=True) def load_model(): model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint) tokenizer = AutoTokenizer.from_pretrained(model_checkpoint , use_fast=False) return model, tokenizer @st.cache(allow_output_mutation=True) def load_questions(): with open('test.json') as json_file: data = json.load(json_file) 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(allow_output_mutation=True) def load_contracts(): with open('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 model, tokenizer = load_model() questions = load_questions() contracts = load_contracts() contract = contracts[0] st.header("Contract Understanding Atticus Dataset (CUAD) Demo") st.write("Based on https://github.com/marshmellow77/cuad-demo") selected_question = st.selectbox('Choose one of the 41 queries from the CUAD dataset:', questions) question_set = [questions[0], selected_question] contract_type = st.radio("Select Contract", ("Sample Contract", "New Contract")) if contract_type == "Sample Contract": sample_contract_num = st.slider("Select Sample Contract #") contract = contracts[sample_contract_num] with st.expander(f"Sample Contract #{sample_contract_num}"): st.write(contract) else: contract = st.text_area("Input New Contract", "", height=256) Run_Button = st.button("Run", key=None) if Run_Button == True and not len(contract)==0 and not len(question_set)==0: predictions = run_prediction(question_set, contract, 'akdeniz27/roberta-base-cuad') for i, p in enumerate(predictions): if i != 0: st.write(f"Question: {question_set[int(p)]}\n\nAnswer: {predictions[p]}\n\n")