Update app.py

app.py

@@ -1,36 +1,143 @@
 import os
 import pandas as pd
 import gradio as gr
-from transformers import pipeline
+from datetime import datetime
 
-# Initialize the question-answering pipeline with the 'deepset/roberta-base-squad2' model
-qa_pipeline = pipeline("question-answering", model="deepset/roberta-base-squad2")
+# Function to analyze audit data for GDPR compliance
+def analyze_data_for_gdpr(audit_data):
+    findings = {
+        "system_info": {
+            "os_version": audit_data.get("os_version", "Unknown"),
+            "architecture": audit_data.get("architecture", "Unknown"),
+            "memory": audit_data.get("memory", "Unknown")
+        },
+        "disk_usage": {
+            "usage_percent": audit_data.get("disk_usage", {}).get("usage_percent", "Unknown"),
+        },
+        "network_info": {
+            "interfaces": audit_data.get("network_info", {}).get("interfaces", "Unknown"),
+        },
+        "security_measures": {
+            "encryption": audit_data.get("security_measures", {}).get("encryption", False),
+            "data_anonymization": audit_data.get("security_measures", {}).get("data_anonymization", False)
+        },
+        "running_processes": audit_data.get("running_processes", []),
+        "software_inventory": audit_data.get("software_inventory", [])
+    }
+    
+    recommendations = []
+    if not findings["security_measures"]["encryption"]:
+        recommendations.append("Implement Encryption: Ensure that both stored and transmitted data are encrypted.")
+    if not findings["security_measures"]["data_anonymization"]:
+        recommendations.append("Implement Data Anonymization: Ensure that sensitive data is anonymized during storage.")
+    
+    return findings, recommendations
 
-# GDPR Compliance Expert
-def evaluate_gdpr_compliance(audit_data):
-    # Example question for GDPR compliance
-    question = (
-        "Based on the provided audit data, are there any compliance issues related to the GDPR regulations? "
-        "Evaluate the data processing, storage, and protection practices."
-    )
-    
-    # Apply the question-answering pipeline
-    response = qa_pipeline(question=question, context=audit_data)
-    return response['answer']
+# Function to analyze audit data for PCI compliance
+def analyze_data_for_pci(audit_data):
+    findings = {
+        "pci_security": {
+            "firewall": audit_data.get("pci_security", {}).get("firewall", False),
+            "antivirus": audit_data.get("pci_security", {}).get("antivirus", False),
+            "intrusion_detection": audit_data.get("pci_security", {}).get("intrusion_detection", False),
+        },
+        "card_data_security": {
+            "encryption": audit_data.get("card_data_security", {}).get("encryption", False),
+            "storage_protection": audit_data.get("card_data_security", {}).get("storage_protection", False)
+        },
+        "network_configurations": audit_data.get("network_configurations", [])
+    }
+    
+    recommendations = []
+    if not findings["pci_security"]["firewall"]:
+        recommendations.append("Implement Firewall: Ensure that a firewall is in place to protect the network.")
+    if not findings["card_data_security"]["encryption"]:
+        recommendations.append("Encrypt Card Data: Ensure all cardholder data is encrypted during storage and transmission.")
+    
+    return findings, recommendations
 
-# PCI Compliance Expert
-def evaluate_pci_compliance(audit_data):
-    # Example question for PCI DSS compliance
-    question = (
-        "Based on the provided audit data, are there any compliance issues related to PCI DSS regulations? "
-        "Evaluate the payment card data security, storage, and processing practices."
-    )
-    
-    # Apply the question-answering pipeline
-    response = qa_pipeline(question=question, context=audit_data)
-    return response['answer']
+# Generate GDPR Compliance Report
+def generate_gdpr_report(audit_data, company_name="Company Name", system_name="System Name"):
+    findings, recommendations = analyze_data_for_gdpr(audit_data)
+    
+    report_content = f"""
+    GDPR Compliance Evaluation Report
+    
+    Title: GDPR Compliance Evaluation Report
+    Date: {datetime.now().strftime('%Y-%m-%d')}
+    Prepared by: [Your Name]
+    For: {company_name}
+    
+    Executive Summary:
+    This report evaluates the compliance of {company_name} with the General Data Protection Regulation (GDPR).
+    Based on the system audit and analysis of data handling processes, this report provides findings, identifies compliance gaps,
+    and suggests recommendations to enhance GDPR adherence.
+    
+    Findings:
+    System Information:
+    - OS Version: {findings['system_info']['os_version']}
+    - Architecture: {findings['system_info']['architecture']}
+    - Memory: {findings['system_info']['memory']}
+    
+    Disk Usage:
+    - Usage Percent: {findings['disk_usage']['usage_percent']}
+    
+    Network Info:
+    - Interfaces: {', '.join(findings['network_info']['interfaces'])}
+    
+    Security Measures:
+    - Encryption: {"Yes" if findings['security_measures']['encryption'] else "No"}
+    - Data Anonymization: {"Yes" if findings['security_measures']['data_anonymization'] else "No"}
+    
+    Running Processes:
+    - Processes: {', '.join(findings['running_processes'])}
+    
+    Software Inventory:
+    - Installed Software: {', '.join(findings['software_inventory'])}
+    
+    Recommendations:
+    {''.join(f'- {rec}\n' for rec in recommendations)}
+    """
+    
+    return report_content
+
+# Generate PCI Compliance Report
+def generate_pci_report(audit_data, company_name="Company Name", system_name="System Name"):
+    findings, recommendations = analyze_data_for_pci(audit_data)
+    
+    report_content = f"""
+    PCI Compliance Evaluation Report
+    
+    Title: PCI Compliance Evaluation Report
+    Date: {datetime.now().strftime('%Y-%m-%d')}
+    Prepared by: [Your Name]
+    For: {company_name}
+    
+    Executive Summary:
+    This report evaluates the compliance of {company_name} with the Payment Card Industry Data Security Standard (PCI DSS).
+    Based on the system audit and analysis of data handling processes, this report provides findings, identifies compliance gaps,
+    and suggests recommendations to enhance PCI DSS adherence.
+    
+    Findings:
+    PCI Security Measures:
+    - Firewall: {"Yes" if findings['pci_security']['firewall'] else "No"}
+    - Antivirus: {"Yes" if findings['pci_security']['antivirus'] else "No"}
+    - Intrusion Detection: {"Yes" if findings['pci_security']['intrusion_detection'] else "No"}
+    
+    Card Data Security:
+    - Encryption: {"Yes" if findings['card_data_security']['encryption'] else "No"}
+    - Storage Protection: {"Yes" if findings['card_data_security']['storage_protection'] else "No"}
+    
+    Network Configurations:
+    - Configurations: {', '.join(findings['network_configurations'])}
+    
+    Recommendations:
+    {''.join(f'- {rec}\n' for rec in recommendations)}
+    """
+    
+    return report_content
 
-# Analyze CSV file input
+# Analyze CSV file input and convert it to JSON-like dictionary for processing
 def analyze_csv_file(file_obj):
     # Read the CSV file into a pandas DataFrame
     try:
@@ -41,99 +148,29 @@ def analyze_csv_file(file_obj):
     # Convert DataFrame to dictionary for processing
     audit_data = df.to_dict(orient='records')
     
-    # Convert the dictionary to a string format suitable for the QA model
-    audit_data_str = ""
+    # Convert the dictionary to a JSON-like structure suitable for analysis
+    audit_data_json = {}
     for record in audit_data:
-        audit_data_str += " ".join([f"{key}: {value}" for key, value in record.items()]) + "\n"
+        audit_data_json.update(record)
     
-    return audit_data_str
-
-# Custom CSS for the specified theme
-custom_css = """
-body {
-    background-color: #000000;
-    color: #ffffff;
-    font-family: Arial, sans-serif;
-}
-.gradio-container {
-    max-width: 1000px;
-    margin: 0 auto;
-    padding: 20px;
-    background-color: #000000;
-    border: 1px solid #e0e0e0;
-    border-radius: 8px;
-    box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1);
-}
-.gr-button {
-    background-color: #000000 !important;
-    border-color: #ff0000 !important;
-    color: #ff0000 !important;
-    margin: 5px;
-}
-.gr-button:hover {
-    background-color: #ff0000 !important;
-    border-color: #ff0000 !important;
-    color: #000000 !important;
-}
-textarea.gr-textbox {
-    border-radius: 4px !important;
-    border: 2px solid #ff0000 !important;
-    background-color: #ffffff !important;
-    color: #000000 !important;
-}
-textarea.gr-textbox:focus {
-    border-color: #ff0000 !important;
-    outline: 0 !important;
-    box-shadow: 0 0 0 0.2rem rgba(255, 0, 0, 0.5) !important;
-}
-#flagging-button {
-    display: none;
-}
-footer {
-    display: none;
-}
-.chatbox .chat-container .chat-message {
-    background-color: #000000 !important;
-    color: #ffffff !important;
-}
-.chatbox .chat-container .chat-message-input {
-    background-color: #000000 !important;
-    color: #ffffff !important;
-}
-.gr-markdown {
-    background-color: #000000 !important;
-    color: #ffffff !important;
-}
-.gr-markdown h1, .gr-markdown h2, .gr-markdown h3, .gr-markdown h4, .gr-markdown h5, .gr-markdown h6, .gr-markdown p, .gr-markdown ul, .gr-markdown ol, .gr-markdown li {
-    color: #ffffff !important;
-}
-.score-box {
-    width: 60px;
-    height: 60px;
-    display: flex;
-    align-items: center
-}
-.label-hidden .gr-label {
-    display: none;
-}
-"""
+    return audit_data_json
 
 # Gradio Interface
-with gr.Blocks(css=custom_css) as demo:
+with gr.Blocks() as demo:
     with gr.Column():
         gr.Markdown("# GDPR and PCI Compliance Evaluation\n### Upload Audit Data in CSV Format")
         csv_file = gr.File(label="Upload CSV file")
         
-        gdpr_compliance = gr.Textbox(lines=10, placeholder="GDPR Compliance Analysis...", label="GDPR Compliance Analysis", elem_classes="label-hidden")
-        pci_compliance = gr.Textbox(lines=10, placeholder="PCI Compliance Analysis...", label="PCI Compliance Analysis", elem_classes="label-hidden")
+        gdpr_compliance = gr.Textbox(lines=10, placeholder="GDPR Compliance Analysis...", label="GDPR Compliance Analysis")
+        pci_compliance = gr.Textbox(lines=10, placeholder="PCI Compliance Analysis...", label="PCI Compliance Analysis")
         
         def run_compliance_checks(csv_file):
             if csv_file is None:
                 return "No file uploaded", "No file uploaded"
             audit_data = analyze_csv_file(csv_file)
-            gdpr_analysis = evaluate_gdpr_compliance(audit_data)
-            pci_analysis = evaluate_pci_compliance(audit_data)
-            return gdpr_analysis, pci_analysis
+            gdpr_report = generate_gdpr_report(audit_data)
+            pci_report = generate_pci_report(audit_data)
+            return gdpr_report, pci_report
         
         check_compliance_btn = gr.Button("Run Compliance Checks")
         check_compliance_btn.click(run_compliance_checks, inputs=[csv_file], outputs=[gdpr_compliance, pci_compliance])