app.py

# 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

# 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)