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from transformers import AutoModelForQuestionAnswering, AutoTokenizer
import streamlit as st
import json
from predict import run_prediction
st.set_page_config(layout="wide")
model_list = ['roberta-base-cuad',
'roberta-large-cuad',
'deberta-xlarge-cuad']
st.sidebar.header("Select CUAD Model")
model_checkpoint = st.sidebar.radio("", model_list)
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.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")
question = st.selectbox('Choose one of the 41 queries from the CUAD dataset:', questions)
# paragraph = st.text_area(label="Contract")
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)==0:
prediction = run_prediction(question, contract, 'C:/Users/akden/Desktop/Legal NLP/CUAD/cuad-models/roberta-base/')
st.write("Answer: " + prediction.strip())
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