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| import gradio as gr # Ensure Gradio is correctly imported | |
| import pandas as pd | |
| import plotly.express as px | |
| from transformers import pipeline | |
| from datasets import load_dataset | |
| # Load the additional datasets | |
| deepseek_prover_v1 = load_dataset('deepseek-ai/DeepSeek-Prover-V1', split='train') | |
| cybersecurity_kg = load_dataset('CyberPeace-Institute/Cybersecurity-Knowledge-Graph', split='train') | |
| codesearchnet_pep8 = load_dataset('kejian/codesearchnet-python-pep8-v1', split='train') | |
| code_text_python = load_dataset('semeru/code-text-python', split='train') | |
| # Sample CVE data (for visualization) | |
| cve_data = { | |
| 'CVE ID': ['CVE-2023-0001', 'CVE-2023-0002', 'CVE-2023-0003', 'CVE-2023-0004', 'CVE-2023-0005'], | |
| 'Severity': ['High', 'Medium', 'Low', 'High', 'Medium'], | |
| 'Description': [ | |
| 'A critical vulnerability in the web application framework.', | |
| 'A medium-severity vulnerability in the database management system.', | |
| 'A low-severity vulnerability in the network firewall.', | |
| 'A critical vulnerability in the operating system kernel.', | |
| 'A medium-severity vulnerability in the web server.' | |
| ], | |
| 'Published Date': ['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04', '2023-01-05'] | |
| } | |
| # Convert CVE data to a DataFrame | |
| cve_df = pd.DataFrame(cve_data) | |
| # Function to filter CVEs by severity | |
| def filter_cves(severity): | |
| filtered_df = cve_df[cve_df['Severity'] == severity] | |
| return filtered_df | |
| # Function to generate a bar chart of CVEs by severity | |
| def generate_cve_chart(): | |
| fig = px.bar(cve_df, x='Severity', y='CVE ID', color='Severity', title='CVEs by Severity') | |
| return fig | |
| # Function to analyze the sentiment of a CVE description | |
| def analyze_sentiment(description): | |
| sentiment_pipeline = pipeline('sentiment-analysis') | |
| result = sentiment_pipeline(description) | |
| return result | |
| # Create the Gradio app | |
| with gr.Blocks() as demo: | |
| # Title and description | |
| gr.Markdown("# Purple Teaming Cyber Security Dashboard") | |
| gr.Markdown("This dashboard provides threat intelligence and CVEs for purple teaming.") | |
| # CVE Filter | |
| with gr.Row(): | |
| severity_filter = gr.Dropdown(choices=['High', 'Medium', 'Low'], label='Filter by Severity') | |
| cve_table = gr.Dataframe(label='CVEs', value=cve_df) | |
| # Event listener for severity filter | |
| severity_filter.change(fn=filter_cves, inputs=severity_filter, outputs=cve_table) | |
| # CVE Chart | |
| with gr.Row(): | |
| cve_chart = gr.Plot(label='CVEs by Severity') | |
| cve_chart.value = generate_cve_chart() # Directly assign the figure to the Plot component | |
| # Sentiment Analysis | |
| with gr.Row(): | |
| description_input = gr.Textbox(label='CVE Description') | |
| sentiment_output = gr.JSON(label='Sentiment Analysis') | |
| analyze_btn = gr.Button('Analyze Sentiment') | |
| # Event listener for sentiment analysis | |
| analyze_btn.click(fn=analyze_sentiment, inputs=description_input, outputs=sentiment_output) | |
| # Display additional datasets in the dashboard | |
| with gr.Tab("Datasets Overview"): | |
| gr.Markdown("## Overview of Additional Datasets") | |
| # Display datasets as dataframes | |
| with gr.Row(): | |
| gr.Dataframe(label="DeepSeek-Prover-V1", value=deepseek_prover_v1) | |
| gr.Dataframe(label="Cybersecurity Knowledge Graph", value=cybersecurity_kg) | |
| gr.Dataframe(label="Code SearchNet Python PEP8", value=codesearchnet_pep8) | |
| gr.Dataframe(label="Code Text Python", value=code_text_python) | |
| # Launch the app | |
| demo.launch(share=True) |