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README.md

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+---
+title: Team Purplle
+emoji: 🐠
+colorFrom: gray
+colorTo: indigo
+sdk: gradio
+sdk_version: 5.12.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import pandas as pd
+import plotly.express as px
+from transformers import pipeline
+
+# Sample data for CVEs
+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 the 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.plot(generate_cve_chart)
+
+    # 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)
+
+# Launch the app
+demo.launch(share=True)

requirements.txt

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+pandas
+matplotlib