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| # set path | |
| import glob, os, sys; | |
| sys.path.append('../utils') | |
| #import needed libraries | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| from utils.target_classifier import load_targetClassifier, target_classification | |
| import logging | |
| logger = logging.getLogger(__name__) | |
| from utils.config import get_classifier_params | |
| from utils.preprocessing import paraLengthCheck | |
| from io import BytesIO | |
| import xlsxwriter | |
| import plotly.express as px | |
| from utils.target_classifier import label_dict | |
| from appStore.rag import run_query | |
| # Declare all the necessary variables | |
| classifier_identifier = 'target' | |
| params = get_classifier_params(classifier_identifier) | |
| def to_excel(df,sectorlist): | |
| len_df = len(df) | |
| output = BytesIO() | |
| writer = pd.ExcelWriter(output, engine='xlsxwriter') | |
| df.to_excel(writer, index=False, sheet_name='Sheet1') | |
| workbook = writer.book | |
| worksheet = writer.sheets['Sheet1'] | |
| worksheet.data_validation('S2:S{}'.format(len_df), | |
| {'validate': 'list', | |
| 'source': ['No', 'Yes', 'Discard']}) | |
| worksheet.data_validation('X2:X{}'.format(len_df), | |
| {'validate': 'list', | |
| 'source': sectorlist + ['Blank']}) | |
| worksheet.data_validation('T2:T{}'.format(len_df), | |
| {'validate': 'list', | |
| 'source': sectorlist + ['Blank']}) | |
| worksheet.data_validation('U2:U{}'.format(len_df), | |
| {'validate': 'list', | |
| 'source': sectorlist + ['Blank']}) | |
| worksheet.data_validation('V2:V{}'.format(len_df), | |
| {'validate': 'list', | |
| 'source': sectorlist + ['Blank']}) | |
| worksheet.data_validation('W2:U{}'.format(len_df), | |
| {'validate': 'list', | |
| 'source': sectorlist + ['Blank']}) | |
| writer.save() | |
| processed_data = output.getvalue() | |
| return processed_data | |
| def app(): | |
| ### Main app code ### | |
| with st.container(): | |
| if 'key1' in st.session_state: | |
| # Load the existing dataset | |
| df = st.session_state.key1 | |
| # Filter out all paragraphs that do not have a reference to groups | |
| df = df[df['Vulnerability Label'].apply(lambda x: len(x) > 0 and 'Other' not in x)] | |
| # Load the classifier model | |
| classifier = load_targetClassifier(classifier_name=params['model_name']) | |
| st.session_state['{}_classifier'.format(classifier_identifier)] = classifier | |
| df = target_classification(haystack_doc=df, | |
| threshold= params['threshold']) | |
| # Rename column | |
| df.rename(columns={'Target Label': 'Specific action/target/measure mentioned'}, inplace=True) | |
| st.session_state.key2 = df | |
| def target_display(): | |
| ### TABLE Output ### | |
| # Assign dataframe a name | |
| df = st.session_state['key2'] | |
| # st.write(df) | |
| ### RAG Output by group ## | |
| # Expand the DataFrame | |
| df_expand = df.explode('Vulnerability Label') | |
| # Group by 'Vulnerability Label' and concatenate 'text' | |
| df_agg = df_expand.groupby('Vulnerability Label')['text'].agg('; '.join).reset_index() | |
| st.markdown("----") | |
| st.markdown('**DOCUMENT FINDINGS SUMMARY BY VULNERABILITY LABEL:**') | |
| # construct RAG query for each label, send to openai and process response | |
| for i in range(0,len(df_agg)): | |
| st.write(df_agg['Vulnerability Label'].iloc[i]) | |
| run_query(context = df_agg['text'].iloc[i], label = df_agg['Vulnerability Label'].iloc[i]) | |
| # st.write(df_agg['text'].iloc[i]) | |