Update tools/post_market_monitoring.py

tools/post_market_monitoring.py

@@ -1,12 +1,15 @@
-# tools/post_market_monitoring.py
+#!/usr/bin/env python
+# coding=utf-8
 import datetime
 import re
 from fpdf import FPDF
 from langdetect import detect
 import gradio as gr
 
+from tools.common import prepend_metadata_questions  # ✅ Shared metadata logic
+
 # === PDF Export ===
-def export_text_to_pdf(text, output_path=None, language="en"):
+def export_text_to_pdf(text, metadata=None, output_path=None, language="en"):
     if output_path is None:
         timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
         output_path = f"post_market_monitoring_{timestamp}.pdf"
@@ -21,6 +24,15 @@ def export_text_to_pdf(text, output_path=None, language="en"):
     pdf.cell(0, 15, title, ln=True, align='C')
     pdf.ln(10)
 
+    # Metadata block
+    if metadata:
+        pdf.set_font("Arial", '', 12)
+        pdf.set_text_color(90, 90, 90)
+        pdf.multi_cell(0, 10, f"Organization: {metadata.get('organization', 'N/A')}")
+        pdf.multi_cell(0, 10, f"Completed by: {metadata.get('completed_by', 'N/A')} ({metadata.get('role', 'N/A')})")
+        pdf.multi_cell(0, 10, f"Timestamp: {metadata.get('timestamp', 'N/A')}")
+        pdf.ln(5)
+
     pdf.set_font("Arial", '', 12)
     pdf.set_text_color(0, 0, 0)
     for line in text.strip().split('\n'):
@@ -49,8 +61,8 @@ def export_text_to_pdf(text, output_path=None, language="en"):
     pdf.output(output_path)
     return output_path
 
-# === Questions ===
-QUESTIONS = [
+# === Base Questions ===
+BASE_QUESTIONS = [
     ("system_name", "What is the name of the AI system being monitored?"),
     ("monitoring_objectives", "What are the main objectives of post-market monitoring?"),
     ("monitoring_process", "Describe the process for collecting post-market data."),
@@ -63,6 +75,8 @@ QUESTIONS = [
     ("update_strategy", "How will monitoring findings be used to update the system?")
 ]
 
+QUESTIONS = prepend_metadata_questions(BASE_QUESTIONS)
+
 def get_questions():
     return QUESTIONS
 
@@ -86,8 +100,10 @@ def run_tool():
         # Final format
         content = f"""
 # Post-Market Monitoring Plan
+
 ## System Overview
 - **System Name**: {answers.get('system_name', '')}
+
 ## Monitoring Strategy
 - **Objectives**: {answers.get('monitoring_objectives', '')}
 - **Process**: {answers.get('monitoring_process', '')}
@@ -95,15 +111,25 @@ def run_tool():
 - **Data Sources**: {answers.get('data_sources', '')}
 - **Trigger Events**: {answers.get('trigger_events', '')}
 - **Frequency**: {answers.get('frequency', '')}
+
 ## Response & Documentation
 - **Corrective Actions**: {answers.get('corrective_actions', '')}
 - **Documentation Approach**: {answers.get('documentation', '')}
 - **Update Strategy**: {answers.get('update_strategy', '')}
+
 ---
 Generated by AI Act Assistant.
 """
+
         lang = detect(content)
-        pdf_path = export_text_to_pdf(content, language=lang)
+        metadata = {
+            "organization": answers.get("organization_name", "N/A"),
+            "completed_by": answers.get("user_name", "N/A"),
+            "role": answers.get("user_role", "N/A"),
+            "timestamp": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        }
+
+        pdf_path = export_text_to_pdf(content, metadata=metadata, language=lang)
         return "✅ Plan completed. Download your PDF below.", {"done": True}, pdf_path
 
     with gr.Blocks(title="Post-Market Monitoring Tool") as demo: