Update tools/gdpr_data_record.py

tools/gdpr_data_record.py

@@ -4,6 +4,7 @@ import re
 from fpdf import FPDF
 from langdetect import detect
 import gradio as gr
+from tools.common import prepend_metadata_questions  # 👈 import metadata logic
 
 # === PDF Export Function ===
 def export_text_to_pdf(text, metadata=None, output_path=None, language="en"):
@@ -22,7 +23,7 @@ def export_text_to_pdf(text, metadata=None, output_path=None, language="en"):
     pdf.cell(0, 15, title, ln=True, align='C')
     pdf.ln(10)
 
-    # Metadata
+    # Metadata section
     if metadata:
         pdf.set_font("Arial", '', 12)
         pdf.set_text_color(90, 90, 90)
@@ -31,7 +32,7 @@ def export_text_to_pdf(text, metadata=None, output_path=None, language="en"):
         pdf.multi_cell(0, 10, f"Timestamp: {metadata.get('timestamp', 'N/A')}")
         pdf.ln(5)
 
-    # Content
+    # Main content
     pdf.set_font("Arial", '', 12)
     pdf.set_text_color(0, 0, 0)
     for line in text.strip().split('\n'):
@@ -58,11 +59,8 @@ def export_text_to_pdf(text, metadata=None, output_path=None, language="en"):
     pdf.output(output_path)
     return output_path
 
-# === Questions (with metadata) ===
-QUESTIONS = [
-    ("organization", "What is the name of your organization?"),
-    ("completed_by", "What is your full name?"),
-    ("role", "What is your role in the organization?"),
+# === GDPR-Specific Questions (excluding metadata) ===
+BASE_QUESTIONS = [
     ("purpose", "What is the purpose of the data processing activity?"),
     ("data_categories", "What categories of personal data are processed?"),
     ("data_subjects", "What types of data subjects are affected?"),
@@ -73,6 +71,9 @@ QUESTIONS = [
     ("dpo", "Who is the Data Protection Officer (if any)?"),
 ]
 
+# Inject metadata
+QUESTIONS = prepend_metadata_questions(BASE_QUESTIONS)
+
 def get_questions():
     return QUESTIONS
 
@@ -92,7 +93,6 @@ def run_tool():
             state["step"] += 1
             return next_q, state, None
 
-        # Compile content and metadata
         content = "\n".join([f"- **{label}**: {answers.get(key, '')}" for key, label in QUESTIONS])
         detected_lang = detect(content)
 
@@ -104,14 +104,14 @@ def run_tool():
         }
 
         pdf_path = export_text_to_pdf(content, metadata=metadata, language=detected_lang)
-        return "✅ Record completed. Download your GDPR data processing record below.", {"done": True}, pdf_path
+        return "✅ Record complete. Download your GDPR processing record below.", {"done": True}, pdf_path
 
-    # === Gradio UI ===
+    # Gradio UI
     with gr.Blocks(title="GDPR Data Processing Record Tool") as demo:
         chatbot = gr.Chatbot(label="🔐 GDPR Assistant", value=[{"role": "assistant", "content": QUESTIONS[0][1]}], type="messages")
         msg = gr.Textbox(label="Your answer")
         state_var = gr.State(state)
-        file_output = gr.File(label="Download PDF", visible=True)
+        file_output = gr.File(label="Download PDF")
         reset_btn = gr.Button("🔁 Restart")
 
         def chat_logic(msg_in, state_in):