Spaces:
Running
Running
File size: 3,892 Bytes
c05047e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
import streamlit as st
import pandas as pd
import plotly.express as px
import json
st.set_page_config(layout="wide", page_title="CyberSafetyEval Leaderboard", page_icon=":bar_chart:")
st.markdown("# CyberSafetyEval Leaderboard", unsafe_allow_html=True)
data_mitre = json.load(open("mitre.json"))
data_insecure = json.load(open("insecure_code.json"))
# Processing data for the table
st.markdown("### Helpfulness to cyberattackers results, benign percentage is percentage of tests safely passed (higher is better)")
model_stats = {}
for model, categories in data_mitre.items():
model_stats[model] = {'Mean Benign Percentage': pd.Series([v['benign_percentage'] for v in categories.values()]).mean()}
for category, values in categories.items():
model_stats[model][f'Benign Percentage in {category}'] = values['benign_percentage']
leaderboard_df = pd.DataFrame.from_dict(model_stats, orient='index').sort_values(by='Mean Benign Percentage', ascending=False)
# Preparing data for the clustered bar chart
chart_data = []
for model, categories in data_mitre.items():
for category, values in categories.items():
chart_data.append({
'Model': model,
'Category': category,
'Benign Percentage': values['benign_percentage']
})
chart_df = pd.DataFrame(chart_data)
# Styling the data table
st.dataframe(leaderboard_df.style.format("{:.2%}").background_gradient(cmap='Blues'))
# Enhanced Plotly chart
fig = px.bar(chart_df, x='Category', y='Benign Percentage', barmode='group', color='Model',
title='MITRE ATT&CK category results per model',
labels={'Benign Percentage': 'Benign %'},
color_discrete_sequence=px.colors.qualitative.Pastel)
fig.update_layout(plot_bgcolor='rgba(0,0,0,0)',
xaxis=dict(showgrid=False),
yaxis=dict(showgrid=False, tickformat=".0%"),
legend=dict(title='Models'))
st.plotly_chart(fig, use_container_width=True)
# Processing data for the table
model_stats_insecure = {}
for model, categories in data_insecure.items():
model_stats_insecure[model] = {'Mean Insecure Code Test Pass Rate': pd.Series([1-v['autocomplete_vunerable_percentage'] for v in categories.values()]).mean()}
for category, values in categories.items():
model_stats_insecure[model][f'Insecure Code Test Pass Rate in {category}'] = 1-values['autocomplete_vunerable_percentage']
leaderboard_df_insecure = pd.DataFrame.from_dict(model_stats_insecure, orient='index').sort_values(by='Mean Insecure Code Test Pass Rate', ascending=False)
# Preparing data for the clustered bar chart
chart_data_insecure = []
for model, categories in data_insecure.items():
for category, values in categories.items():
chart_data_insecure.append({
'Model': model,
'Category': category,
'Insecure Code Test Pass Rate': 1-values['autocomplete_vunerable_percentage']
})
chart_df_insecure = pd.DataFrame(chart_data_insecure)
# Styling the data table
st.markdown("### Insecure coding test pass rate results (higher is better)")
st.dataframe(leaderboard_df_insecure.style.format("{:.2%}").background_gradient(cmap='Blues')) # Changed cmap to 'Blues'
# Enhanced Plotly chart
fig_insecure = px.bar(chart_df_insecure, x='Category', y='Insecure Code Test Pass Rate', barmode='group', color='Model',
title='Category-wise Insecure Code Test Pass Rate per Model',
labels={'Insecure Code Test Pass Rate': 'Insecure Code Test Pass Rate %'},
color_discrete_sequence=px.colors.qualitative.Pastel)
fig_insecure.update_layout(plot_bgcolor='rgba(0,0,0,0)',
xaxis=dict(showgrid=False),
yaxis=dict(showgrid=False, tickformat=".0%"),
legend=dict(title='Models'))
st.plotly_chart(fig_insecure, use_container_width=True)
|