"""AI framing service for decision qualification.

Orchestrates a 2-round conversation that clarifies reversibility and urgency
before producing a final QualificationResult.

Currently a rule-based stub — will be replaced by Qwen3.6 (MacStudio) calls
once the local LLM endpoint is available. The interface is stable: callers
always receive AIFrameResponse; the underlying engine is swappable.
"""

from __future__ import annotations

from dataclasses import dataclass


# ---------------------------------------------------------------------------
# Schemas (dataclasses — no Pydantic dependency in the engine layer)
# ---------------------------------------------------------------------------


@dataclass
class AIMessage:
    role: str   # "user" | "assistant"
    content: str


@dataclass
class AIQuestion:
    id: str
    text: str
    options: list[str]


@dataclass
class AIQualifyResult:
    decision_type: str
    process: str
    recommended_modalities: list[str]
    recommend_onchain: bool
    onchain_reason: str | None
    confidence: str
    collective_available: bool
    record_in_observatory: bool
    reasons: list[str]


@dataclass
class AIFrameRequest:
    within_mandate: bool = False
    affected_count: int | None = None
    is_structural: bool = False
    context: str | None = None
    messages: list[AIMessage] | None = None

    def __post_init__(self):
        if self.messages is None:
            self.messages = []


@dataclass
class AIFrameResponse:
    done: bool
    questions: list[AIQuestion]
    result: AIQualifyResult | None
    explanation: str | None


# ---------------------------------------------------------------------------
# Standard clarifying questions (stub — same regardless of context)
# Real Qwen integration will generate context-aware questions
# ---------------------------------------------------------------------------


_CLARIFYING_QUESTIONS: list[AIQuestion] = [
    AIQuestion(
        id="reversibility",
        text="Si cette décision s'avère inappropriée dans 6 mois, peut-on facilement revenir en arrière ?",
        options=[
            "Oui, facilement",
            "Difficilement",
            "Non, c'est irréversible",
        ],
    ),
    AIQuestion(
        id="urgency",
        text="Y a-t-il une contrainte temporelle sur cette décision ?",
        options=[
            "Urgente (< 1 semaine)",
            "Délai raisonnable (quelques semaines)",
            "Pas d'urgence",
        ],
    ),
]


# ---------------------------------------------------------------------------
# Core function
# ---------------------------------------------------------------------------


def ai_frame(request: AIFrameRequest) -> AIFrameResponse:
    """Run one round of AI framing.

    Round 1 (messages=[])  → return 2 clarifying questions, done=False
    Round 2 (messages set) → parse answers, qualify, return result, done=True
    """
    messages = request.messages or []

    # ── Round 1: no conversation yet ────────────────────────────────────────
    if not messages:
        return AIFrameResponse(
            done=False,
            questions=list(_CLARIFYING_QUESTIONS),
            result=None,
            explanation=None,
        )

    # ── Round 2: answers present → qualify ──────────────────────────────────
    answers = _parse_answers(messages)
    result = _build_result(request, answers)
    explanation = _build_explanation(answers)

    return AIFrameResponse(
        done=True,
        questions=[],
        result=result,
        explanation=explanation,
    )


# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------


def _parse_answers(messages: list[AIMessage]) -> dict[str, str]:
    """Extract question answers from the last user message.

    Expected format: "reversibility:<answer>|urgency:<answer>"
    Anything not matching is treated as free text for context.
    """
    answers: dict[str, str] = {}
    for msg in reversed(messages):
        if msg.role == "user" and "|" in msg.content and ":" in msg.content:
            for part in msg.content.split("|"):
                if ":" in part:
                    key, _, val = part.partition(":")
                    answers[key.strip()] = val.strip()
            break
    return answers


def _build_result(request: AIFrameRequest, answers: dict[str, str]) -> AIQualifyResult:
    """Produce a qualification result enriched by the AI answers."""
    from app.engine.qualifier import (
        QualificationConfig,
        QualificationInput,
        qualify,
    )

    config = QualificationConfig()
    inp = QualificationInput(
        within_mandate=request.within_mandate,
        affected_count=request.affected_count,
        is_structural=request.is_structural,
        context_description=request.context,
    )
    base = qualify(inp, config)

    reasons = list(base.reasons)

    # Reversibility adjustment
    reversibility = answers.get("reversibility", "")
    if "irréversible" in reversibility.lower():
        reasons.append("Décision irréversible : consensus élevé recommandé.")
        if not base.recommend_onchain and request.is_structural:
            pass  # already handled by engine

    # Urgency note
    urgency = answers.get("urgency", "")
    if "urgente" in urgency.lower() or "< 1" in urgency:
        reasons.append("Urgence signalée : privilégier un protocole à délai court.")

    return AIQualifyResult(
        decision_type=base.decision_type.value,
        process=base.process,
        recommended_modalities=base.recommended_modalities,
        recommend_onchain=base.recommend_onchain,
        onchain_reason=base.onchain_reason,
        confidence=base.confidence,
        collective_available=base.collective_available,
        record_in_observatory=base.record_in_observatory,
        reasons=reasons,
    )


def _build_explanation(answers: dict[str, str]) -> str:
    parts = []
    rev = answers.get("reversibility", "")
    urg = answers.get("urgency", "")
    if rev:
        parts.append(f"Réversibilité : {rev}.")
    if urg:
        parts.append(f"Urgence : {urg}.")
    return " ".join(parts) if parts else "Qualification basée sur les éléments fournis."