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app.py

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+import streamlit as st
+import pandas as pd
+import re
+
+st.set_page_config(layout='wide')
+
+def load_data():
+    return pd.read_csv(filepath_or_buffer='./train.csv')
+
+df = load_data()
+
+if 'idx' not in st.session_state:
+    st.session_state.idx = 0
+
+st.markdown("<h1 style='text-align: center;'>Investigation Legal Documents Dataset Checker</h1>", 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_prev = col_1.button(label='Previous sample', use_container_width=True)
+btn_next = col_2.button(label='Next sample', use_container_width=True)
+btn_save = col_3.button(label='Save changes', use_container_width=True)
+
+if btn_prev:
+    if st.session_state.idx > 0:
+        st.session_state.idx -= 1
+
+if btn_next:
+    if st.session_state.idx < len(df) - 1:
+        st.session_state.idx += 1
+
+st.markdown(f"<h3 style='text-align: center;'>Sample: {st.session_state.idx+1}/{len(df)}</h3>", unsafe_allow_html=True)
+
+context = st.text_area(label='Your context: ', value=df['relevant_articles'][st.session_state.idx], height=300)
+question = st.text_area(label='Your question: ', value=df['text'][st.session_state.idx], height=100)
+answer = st.text_area(label='Your answer: ', value=df['answer'][st.session_state.idx], height=100)
+
+if answer.strip() and context.strip():
+    highlighted_context = re.sub(re.escape(answer), "<mark>" + answer + "</mark>", context, flags=re.IGNORECASE)
+    st.markdown(highlighted_context, unsafe_allow_html=True)
+
+if btn_save:
+    df.loc[st.session_state.idx, 'relevant_articles'] = context
+    df.loc[st.session_state.idx, 'text'] = question
+    df.loc[st.session_state.idx, 'answer'] = answer
+    
+    df.to_csv('./train.csv', index=False)