tools/dma_self_assessment.py

# tools/dma_self_assessment.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"dma_self_assessment_{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 = "DMA Self-Assessment Checklist" if language == "en" else "Auto-Évaluation DMA"
    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', 'N/A')}")
        pdf.multi_cell(0, 10, f"Completed by: {metadata.get('name', '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'):
        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, value = 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, value)
        else:
            pdf.multi_cell(0, 10, line)

    pdf.output(output_path)
    return output_path

# === Questions ===
QUESTIONS = prepend_metadata_questions([
    ("gatekeeper_status", "Have you been designated as a gatekeeper under the DMA?"),
    ("user_data_portability", "Have you implemented data portability mechanisms for users?"),
    ("interoperability_measures", "What interoperability measures are in place for third-party services?"),
    ("ad_transparency", "Do you provide transparency for online advertisements as per DMA requirements?"),
    ("self_preference", "Have you eliminated self-preferencing practices?"),
    ("complaint_mechanism", "Is there an effective complaint resolution mechanism for business users?"),
    ("compliance_summary", "Please summarize any other DMA-related compliance actions.")
])

def get_questions():
    return QUESTIONS

# === Run Tool ===
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_q = QUESTIONS[step][1]
            state["step"] += 1
            return next_q, state, None

        content = "\n".join([f"- **{label}**: {answers.get(key, '')}" for key, label in QUESTIONS])
        lang = detect(content) if len(content.strip()) > 3 else "en"
        metadata = {
            "organization": answers.get("organization_name", "N/A"),
            "name": 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 "✅ DMA Self-Assessment complete. Download your PDF below.", {"done": True}, pdf_path

    with gr.Blocks(title="DMA Self-Assessment Tool") as demo:
        chatbot = gr.Chatbot(label="📋 DMA Self-Assessment 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)