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# hacky fix for HF environment issues
import os
os.system("pip uninstall -y spaces")
os.system('pip install spaces==0.17.0')
os.system("pip uninstall -y gradio")
os.system("pip uninstall -y pydantic")
os.system("pip uninstall -y typer")
os.system('pip install typer==0.4.0')
os.system('pip install pydantic==1.8.2 --use-deprecated=legacy-resolver')
import appStore.vulnerability_analysis as vulnerability_analysis
import appStore.doc_processing as processing
from appStore.rag import run_query
from utils.uploadAndExample import add_upload, get_tabs
from utils.vulnerability_classifier import label_dict
import streamlit as st
import pandas as pd
import plotly.express as px
st.set_page_config(page_title = 'Vulnerability Analysis',
initial_sidebar_state='expanded', layout="wide")
with st.sidebar:
# upload and example doc
choice = st.sidebar.radio(label = 'Select the Document',
help = 'You can upload your own documents \
or use the example document',
options = ('Upload Document', 'Try Example'),
horizontal = True)
add_upload(choice)
with st.container():
st.markdown("<h2 style='text-align: center;'> Vulnerability Analysis </h2>", unsafe_allow_html=True)
st.write(' ')
with st.expander("ℹ️ - About this app", expanded=False):
st.write(
"""
The Vulnerability Analysis App is an open-source\
digital tool which aims to assist policy analysts and \
other users in extracting and filtering references \
to different vulnerable groups from public documents.
""")
st.write("""
What Happens in background?
- Step 1: Once the document is provided to app, it undergoes *Pre-processing*.\
In this step the document is broken into smaller paragraphs \
(based on word/sentence count).
- Step 2: The paragraphs are then fed to the **Vulnerability Classifier** which detects if
the paragraph contains any references to vulnerable groups.
""")
st.write("")
# Define the apps used
apps = [processing.app, vulnerability_analysis.app]
multiplier_val = 1 / len(apps)
if st.button("Analyze Documents"):
prg = st.progress(0.0)
for i, func in enumerate(apps):
func()
prg.progress((i + 1) * multiplier_val)
if 'combined_files_df' in st.session_state: # check for existence of processed documents
# get the filenames from the processed docs dataframe so we can use for tab names
uploaded_docs = [value for key, value in st.session_state.items() if key.startswith('filename_')]
tab_titles = get_tabs(uploaded_docs)
if tab_titles:
tabs = st.tabs(tab_titles)
# Render the results (Pie chart, Summary and Table) in indidivual tabs for each doc
for tab, doc in zip(tabs, uploaded_docs):
with tab:
# Main app code
with st.container():
st.write(' ')
# Assign dataframe a name
df_vul = st.session_state['combined_files_df']
df_vul = df_vul[df_vul['filename'] == doc]
col1, col2 = st.columns([1,1])
with col1:
# Header
st.subheader("Explore references to vulnerable groups:")
# Text
num_paragraphs = len(df_vul['Vulnerability Label'])
num_references = len(df_vul[df_vul['Vulnerability Label'] != 'Other'])
st.markdown(f"""<div style="text-align: justify;"> The document contains a
total of <span style="color: red;">{num_paragraphs}</span> paragraphs.
We identified <span style="color: red;">{num_references}</span>
references to vulnerable groups.</div>
<br>
In the pie chart on the right you can see the distribution of the different
groups defined. For a more detailed view in the text, see the paragraphs and
their respective labels in the table below.</div>""", unsafe_allow_html=True)
with col2:
### Pie chart
# Create a df that stores all the labels
df_labels = pd.DataFrame(list(label_dict.items()), columns=['Label ID', 'Label'])
# Count how often each label appears in the "Vulnerability Labels" column
label_counts = df_vul['Vulnerability Label'].value_counts().reset_index()
label_counts.columns = ['Label', 'Count']
# Merge the label counts with the df_label DataFrame
df_labels = df_labels.merge(label_counts, on='Label', how='left')
# Configure graph
fig = px.pie(df_labels,
names="Label",
values="Count",
title='Label Counts',
hover_name="Count",
color_discrete_sequence=px.colors.qualitative.Plotly
)
#Show plot
st.plotly_chart(fig, use_container_width=True)
### Document Summary
st.markdown("----")
st.markdown('**DOCUMENT FINDINGS SUMMARY:**')
# filter out 'Other' cause we don't want that in the table (and it's way too big for the summary)
df_docs = df_vul[df_vul['Vulnerability Label'] != 'Other']
# construct RAG query, send to openai and process response
run_query(df_docs)
st.markdown("----")
with st.expander("ℹ️ - Document Text Classifications", expanded=False):
### Table
st.table(df_docs)