Update app.py

app.py

@@ -1,8 +1,48 @@
+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