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import pandas as pd
import streamlit as st
import re
st.set_page_config(page_icon='🍃', page_title='MRC for Legal Document Dataset checker', layout='wide', initial_sidebar_state="collapsed")
st.markdown("<h2 style='text-align: center;'>Investigation Legal Dataset checker for Machine Reading Comprehension</h2>", unsafe_allow_html=True)
df = pd.read_csv(filepath_or_buffer='./GeneratedLegalData.csv')
if 'idx' not in st.session_state:
st.session_state.idx = 0
st.markdown(f"<h4 style='text-align: center;'>Sample {st.session_state.idx + 1}/{len(df)}</h4>", unsafe_allow_html=True)
col_1, col_2, col_3, col_4, col_5, col_6, col_7, col_8, col_9, col_10 = st.columns([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
btn_previous = col_1.button(label=':arrow_backward: Previous sample', use_container_width=True)
btn_next = col_2.button(label='Next sample :arrow_forward:', use_container_width=True)
btn_save = col_3.button(label=':heavy_check_mark: Save change', use_container_width=True)
if len(df) != 0:
txt_context = st.text_area(height=300, label='Your context:', value=df['context'][st.session_state.idx])
txt_question = st.text_area(height=100, label='Your question:', value=df['question'][st.session_state.idx])
txt_answer = st.text_area(height=100, label='Your answer:', value=df['answer'][st.session_state.idx])
if txt_answer.strip() and txt_context.strip():
highlighted_context = re.sub(re.escape(txt_answer), "<mark>" + txt_answer + "</mark>", txt_context, flags=re.IGNORECASE)
st.markdown(highlighted_context, unsafe_allow_html=True)
if btn_previous:
if st.session_state.idx > 0:
st.session_state.idx -= 1
st.rerun()
else:
pass
if btn_next:
if st.session_state.idx <= (len(df) - 1):
st.session_state.idx += 1
st.rerun()
else:
pass
if btn_save:
df['context'][st.session_state.idx] = txt_context
df['question'][st.session_state.idx] = txt_question
df['answer'][st.session_state.idx] = txt_answer
btn_download = col_4.download_button(data=df.to_csv(), label=':arrow_down_small: Download file', use_container_width=True, file_name="checked.csv", mime="text/csv")
df.to_csv(path_or_buf='./GeneratedLegalData.csv', index=None) |