# tools/ai_technical_doc.py
import datetime
import os
import re
from fpdf import FPDF
from langdetect import detect
import gradio as gr

from tools.common import prepend_metadata_questions  # Shared metadata question helper

# === PDF Export Function ===
def export_text_to_pdf(text, answers, output_path=None, language="en"):
    if output_path is None:
        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
        output_path = f"technical_documentation_{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 = "Technical Documentation - AI Act (Art. 11)" if language == "en" else "Documentation Technique - AI Act"
    pdf.cell(0, 15, title, ln=True, align='C')
    pdf.ln(5)

    # Metadata under title
    pdf.set_font("Arial", 'I', 11)
    pdf.set_text_color(80, 80, 80)
    name = answers.get("user_name", "N/A")
    role = answers.get("user_role", "N/A")
    org = answers.get("organization_name", "N/A")
    timestamp = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
    pdf.multi_cell(0, 10, f"Completed by {name} ({role}) at {org} on {timestamp}", align="C")
    pdf.ln(5)

    # Main body
    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, 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)
        elif line == "---":
            pdf.line(10, pdf.get_y(), 200, pdf.get_y())
            pdf.ln(5)
        else:
            pdf.multi_cell(0, 10, line)

    pdf.output(output_path)
    return output_path

# === Questions ===
CORE_QUESTIONS = [
    ("system_name", "What is the name of your AI system?"),
    ("provider", "Who is the provider or developer of the system?"),
    ("intended_purpose", "What is the intended purpose of the system?"),
    ("architecture", "Describe the system architecture."),
    ("training_data", "What kind of training data is used?"),
    ("testing_methodology", "How was the system tested and validated?"),
    ("performance_metrics", "What are the system's performance metrics?"),
    ("risk_management", "What risk management measures were taken?"),
    ("cybersecurity", "What cybersecurity measures are in place?"),
    ("human_oversight", "How is human oversight implemented?"),
    ("versioning", "How is version control maintained?"),
    ("recordkeeping", "How are logs and records maintained?")
]

QUESTIONS = prepend_metadata_questions(CORE_QUESTIONS)

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

        # Final content for PDF
        content = "\n".join([f"- **{label}**: {answers.get(key, '')}" for key, label in QUESTIONS if key not in ["user_name", "user_role", "organization_name"]])
        detected_lang = detect(content)
        pdf_path = export_text_to_pdf(content, answers, language=detected_lang)
        return "✅ Completed. Download your documentation below.", {"done": True}, pdf_path

    with gr.Blocks(title="AI Technical Documentation Tool") as demo:
        chatbot = gr.Chatbot(
            label="🧠 Technical Doc 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)
        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)