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Create dora_backup_strategy_summary.py

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  1. tools/dora_backup_strategy_summary.py +125 -0
tools/dora_backup_strategy_summary.py ADDED
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+ # dora_backup_strategy_summary.py
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+ import datetime
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+ import re
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+ from fpdf import FPDF
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+ from langdetect import detect
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+ import gradio as gr
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+ from tools.common import prepend_metadata_questions
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+
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+ # === PDF Export ===
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+ def export_text_to_pdf(text, metadata=None, output_path=None, language="en"):
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+ if output_path is None:
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+ timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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+ output_path = f"backup_strategy_summary_{timestamp}.pdf"
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+
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+ pdf = FPDF()
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+ pdf.add_page()
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+ pdf.set_auto_page_break(auto=True, margin=15)
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+
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+ pdf.set_font("Arial", 'B', 16)
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+ pdf.set_text_color(0, 51, 102)
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+ title = "Backup Strategy Summary - DORA (Optional)" if language == "en" else "Résumé de la stratégie de sauvegarde - DORA"
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+ pdf.cell(0, 15, title, ln=True, align='C')
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+ pdf.ln(10)
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+
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+ if metadata:
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+ pdf.set_font("Arial", '', 12)
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+ pdf.set_text_color(90, 90, 90)
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+ pdf.multi_cell(0, 10, f"Organization: {metadata.get('organization_name', 'N/A')}")
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+ pdf.multi_cell(0, 10, f"Completed by: {metadata.get('user_name', 'N/A')} ({metadata.get('user_role', 'N/A')})")
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+ pdf.multi_cell(0, 10, f"Timestamp: {metadata.get('timestamp', 'N/A')}")
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+ pdf.ln(5)
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+
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+ pdf.set_font("Arial", '', 12)
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+ pdf.set_text_color(0, 0, 0)
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+ for line in text.strip().split('\n'):
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+ line = line.strip()
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+ if line.startswith("## "):
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+ section = line.replace("## ", "").strip()
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+ pdf.set_font("Arial", 'B', 13)
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+ pdf.set_text_color(30, 30, 120)
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+ pdf.ln(8)
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+ pdf.cell(0, 10, section, ln=True)
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+ pdf.set_font("Arial", '', 12)
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+ pdf.set_text_color(0, 0, 0)
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+ elif line.startswith("- **"):
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+ match = re.match(r"- \*\*(.+?)\*\*: (.+)", line)
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+ if match:
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+ label, value = match.groups()
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+ pdf.set_font("Arial", 'B', 12)
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+ pdf.cell(0, 10, f"{label}:", ln=True)
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+ pdf.set_font("Arial", '', 12)
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+ pdf.multi_cell(0, 10, value)
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+ elif line == "---":
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+ pdf.line(10, pdf.get_y(), 200, pdf.get_y())
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+ pdf.ln(5)
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+ else:
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+ pdf.multi_cell(0, 10, line)
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+ pdf.output(output_path)
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+ return output_path
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+
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+ # === Questions ===
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+ QUESTIONS = prepend_metadata_questions([
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+ ("backup_frequency", "How often are backups performed?"),
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+ ("backup_types", "What types of data and systems are included in backups?"),
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+ ("storage_locations", "Where are backups stored (e.g. cloud, offsite, on-premises)?"),
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+ ("encryption_measures", "Are backups encrypted? If so, describe the method."),
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+ ("testing_recovery", "How often are backup recovery procedures tested?"),
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+ ("retention_policy", "What is the backup retention policy?"),
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+ ("responsibility", "Who is responsible for managing and verifying backups?")
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+ ])
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+
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+
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+ def get_questions():
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+ return QUESTIONS
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+
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+
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+ def run_tool():
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+ state = {"step": 0, "answers": {}}
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+
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+ def step_by_step_agent(user_input, state):
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+ step = state["step"]
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+ answers = state["answers"]
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+
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+ if step > 0:
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+ key, _ = QUESTIONS[step - 1]
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+ answers[key] = user_input
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+
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+ if step < len(QUESTIONS):
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+ next_question = QUESTIONS[step][1]
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+ state["step"] += 1
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+ return next_question, state, None
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+
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+ metadata = {
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+ "user_name": answers.get("user_name", "N/A"),
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+ "user_role": answers.get("user_role", "N/A"),
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+ "organization_name": answers.get("organization_name", "N/A"),
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+ "timestamp": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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+ }
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+
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+ content = "\n".join([f"- **{label}**: {answers.get(key, '')}" for key, label in QUESTIONS])
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+ lang = detect(content if len(content.strip()) > 3 else "Placeholder content")
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+ pdf_path = export_text_to_pdf(content, metadata=metadata, language=lang)
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+ return "✅ Backup Strategy Summary ready.", {"done": True}, pdf_path
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+
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+ with gr.Blocks(title="DORA Backup Strategy Summary") as demo:
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+ chatbot = gr.Chatbot(label="💾 Backup Strategy Assistant", value=[{"role": "assistant", "content": QUESTIONS[0][1]}], type="messages")
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+ msg = gr.Textbox(label="Your answer")
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+ state_var = gr.State(state)
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+ file_output = gr.File(label="Download PDF")
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+ reset_btn = gr.Button("🔁 Restart")
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+
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+ def chat_logic(msg_in, state_in):
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+ reply, updated_state, file = step_by_step_agent(msg_in, state_in)
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+ messages = [{"role": "user", "content": msg_in}]
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+ if reply:
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+ messages.append({"role": "assistant", "content": reply})
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+ return messages, updated_state, file
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+
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+ def reset():
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+ return [{"role": "assistant", "content": QUESTIONS[0][1]}], {"step": 0, "answers": {}}, None
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+
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+ msg.submit(chat_logic, [msg, state_var], [chatbot, state_var, file_output])
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+ reset_btn.click(reset, outputs=[chatbot, state_var, file_output])
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+
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+ demo.launch(show_api=False)