tools/dora_incident_notification_log.py

# tools/dora_incident_notification_log.py
import datetime
import re
from fpdf import FPDF
from langdetect import detect
import gradio as gr
from tools.common import prepend_metadata_questions

# === PDF Export ===
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"incident_notification_log_{timestamp}.pdf"

    pdf = FPDF()
    pdf.add_page()
    pdf.set_auto_page_break(auto=True, margin=15)

    pdf.set_font("Arial", 'B', 16)
    pdf.set_text_color(0, 51, 102)
    title = "Incident Notification Log - DORA" if language == "en" else "Journal de Notification d'Incident - DORA"
    pdf.cell(0, 15, title, ln=True, align='C')
    pdf.ln(10)

    if metadata:
        pdf.set_font("Arial", '', 12)
        pdf.set_text_color(90, 90, 90)
        pdf.multi_cell(0, 10, f"Organization: {metadata.get('organization_name', 'N/A')}")
        pdf.multi_cell(0, 10, f"Completed by: {metadata.get('user_name', 'N/A')} ({metadata.get('user_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'):
        line = line.strip()
        if line.startswith("## "):
            section = line.replace("## ", "").strip()
            pdf.set_font("Arial", 'B', 13)
            pdf.set_text_color(30, 30, 120)
            pdf.ln(8)
            pdf.cell(0, 10, section, ln=True)
            pdf.set_font("Arial", '', 12)
            pdf.set_text_color(0, 0, 0)
        elif line.startswith("- **"):
            match = re.match(r"- \*\*(.+?)\*\*: (.+)", line)
            if match:
                label, answer = match.groups()
                pdf.set_font("Arial", 'B', 12)
                pdf.cell(0, 10, f"{label}:", ln=True)
                pdf.set_font("Arial", '', 12)
                pdf.multi_cell(0, 10, answer)
        else:
            pdf.multi_cell(0, 10, line)
    pdf.output(output_path)
    return output_path

# === Questions ===
QUESTIONS = prepend_metadata_questions([
    ("incident_date", "When did the incident occur?"),
    ("detected_by", "Who detected the incident?"),
    ("impact_summary", "What was the impact of the incident?"),
    ("reporting_channel", "Which channel was used to notify the incident?"),
    ("notification_timeframe", "Was the notification within the required timeframe?"),
    ("stakeholders_informed", "Who was informed (authorities, clients, etc.)?"),
    ("response_measures", "What immediate response measures were taken?"),
    ("follow_up_actions", "What follow-up actions were planned?")
])

def get_questions():
    return QUESTIONS

def run_tool():
    state = {"step": 0, "answers": {}}

    def step_by_step_agent(user_input, state):
        step = state["step"]
        answers = state["answers"]

        if step > 0:
            key, _ = QUESTIONS[step - 1]
            answers[key] = user_input

        if step < len(QUESTIONS):
            next_question = QUESTIONS[step][1]
            state["step"] += 1
            return next_question, state, None

        try:
            content = "\n".join([f"- **{label}**: {answers.get(key, '')}" for key, label in QUESTIONS])
            detected_lang = detect(content) if len(content.strip()) > 10 else "en"
        except:
            detected_lang = "en"

        metadata = {
            "user_name": answers.get("user_name", "N/A"),
            "user_role": answers.get("user_role", "N/A"),
            "organization_name": answers.get("organization_name", "N/A"),
            "timestamp": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        }

        pdf_path = export_text_to_pdf(content, metadata=metadata, language=detected_lang)
        return "✅ Incident logged successfully. Download your PDF below.", {"done": True}, pdf_path

    with gr.Blocks(title="DORA - Incident Notification Log") as demo:
        chatbot = gr.Chatbot(label="🚨 Incident Notification 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")
        reset_btn = gr.Button("🔁 Restart")

        def chat_logic(msg_in, state_in):
            reply, updated_state, file = step_by_step_agent(msg_in, state_in)
            messages = [{"role": "user", "content": msg_in}]
            if reply:
                messages.append({"role": "assistant", "content": reply})
            return messages, updated_state, file

        def reset():
            return [{"role": "assistant", "content": QUESTIONS[0][1]}], {"step": 0, "answers": {}}, None

        msg.submit(chat_logic, [msg, state_var], [chatbot, state_var, file_output])
        reset_btn.click(reset, outputs=[chatbot, state_var, file_output])

    demo.launch(show_api=False)