Update app.py

app.py

@@ -1,48 +1,8 @@
-import gradio as gr
-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
@@ -60,7 +20,7 @@ with gr.Blocks() as demo:
     # CVE Chart
     with gr.Row():
         cve_chart = gr.Plot(label='CVEs by Severity')
-        cve_chart.update(generate_cve_chart())
+        cve_chart.value = generate_cve_chart()  # Directly assign the figure to the Plot component
 
     # Sentiment Analysis
     with gr.Row():