"""
Proposals Router - Phase 6.4h 真實大腦植入
==========================================

POST /api/v1/incidents/{incident_id}/propose

整合真實 ProposalService + OpenClaw LLM 實現決策提案生成。
"""


import structlog
from fastapi import APIRouter, Depends, HTTPException, status
from pydantic import BaseModel, Field

from src.models.approval import RiskLevel as ApprovalRiskLevel
from src.services.proposal_service import ProposalService, get_proposal_service

logger = structlog.get_logger(__name__)

router = APIRouter(prefix="/api/v1/incidents", tags=["Proposals"])


class ProposalCreateRequest(BaseModel):
    require_dry_run: bool = Field(
        default=True,
        description="強制要求演練模式，此參數將直接餵給 Guardrails 進行驗證"
    )


class ProposalResponse(BaseModel):
    proposal_id: str = Field(..., description="決策書唯一識別碼")
    incident_id: str = Field(..., description="關聯的事件 ID")
    actions: list[str] = Field(..., description="生成的具體作戰指令清單")
    tier: int = Field(..., description="判定之授權級別 (1: 自主, 2: 授權, 3: 親核)")
    guardrails_passed: bool = Field(..., description="是否完全通過防爆圈檢測")
    rejection_reason: str | None = Field(default=None, description="若未通過防爆圈，顯示阻擋原因")
    # Phase 6.4h: 額外回傳 LLM 決策資訊
    llm_provider: str | None = Field(default=None, description="LLM 提供者 (ollama/gemini/claude)")
    llm_confidence: float | None = Field(default=None, description="LLM 信心度 (0.0-1.0)")
    kubectl_command: str | None = Field(default=None, description="生成的 kubectl 指令")


def get_real_proposal_service() -> ProposalService:
    """
    Phase 6.4h 真實依賴注入: 返回 ProposalService 單例

    ProposalService 整合:
    - OpenClaw LLM (Ollama → Gemini → Claude fallback)
    - Redis Working Memory
    - PostgreSQL Episodic Memory
    - TrustEngine 風險評估
    """
    return get_proposal_service()

@router.post(
    "/{incident_id}/propose",
    response_model=ProposalResponse,
    status_code=status.HTTP_201_CREATED,
    summary="生成決策提案 (Phase 6.4h)",
    description="使用真實 OpenClaw LLM + TrustEngine 生成決策提案",
)
async def generate_decision_proposal(
    incident_id: str,
    request: ProposalCreateRequest,
    service: ProposalService = Depends(get_real_proposal_service),  # noqa: B008
):
    """
    Phase 6.4h: 真實 LLM 決策提案生成

    流程:
    1. Guardrails 前置檢查 (require_dry_run 必須為 True)
    2. 從 Redis/PostgreSQL 載入 Incident
    3. 呼叫 OpenClaw LLM 生成提案 (Ollama → Gemini → Claude fallback)
    4. TrustEngine 風險評估與 Tier 判定
    5. 建立 ApprovalRequest (向下相容前端)
    6. 返回結構化 ProposalResponse
    """
    try:
        # 1. Guardrails 檢查: require_dry_run 必須為 True
        if not request.require_dry_run:
            logger.warning(
                "guardrails_rejected",
                incident_id=incident_id,
                reason="require_dry_run must be True",
            )
            raise HTTPException(
                status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,
                detail="Guardrail triggered: require_dry_run must be True"
            )

        logger.info(
            "proposal_generation_start",
            incident_id=incident_id,
        )

        # 2. 呼叫真實 ProposalService 生成提案
        approval, message = await service.generate_proposal(incident_id)

        if approval is None:
            logger.warning(
                "proposal_generation_failed",
                incident_id=incident_id,
                message=message,
            )
            raise HTTPException(
                status_code=status.HTTP_400_BAD_REQUEST,
                detail=message
            )

        # 3. 計算 tier 基於 risk_level
        tier_map = {
            ApprovalRiskLevel.LOW: 1,       # 自主 (AI 可直接執行)
            ApprovalRiskLevel.MEDIUM: 2,    # 授權 (需 1 人簽核)
            ApprovalRiskLevel.CRITICAL: 3,  # 親核 (需 2 人簽核)
        }
        tier = tier_map.get(approval.risk_level, 2)

        # 4. 提取 LLM 資訊 (Phase 6.4h 新增)
        metadata = approval.metadata or {}
        kubectl_command = metadata.get("kubectl_command", "")
        llm_provider = metadata.get("llm_provider")
        llm_confidence = metadata.get("llm_confidence")

        # 5. 組裝 actions 清單
        actions = [approval.action]
        if kubectl_command and kubectl_command != approval.action:
            actions.append(kubectl_command)

        logger.info(
            "proposal_generation_complete",
            incident_id=incident_id,
            proposal_id=str(approval.id),
            tier=tier,
            llm_provider=llm_provider,
        )

        return ProposalResponse(
            proposal_id=str(approval.id),
            incident_id=incident_id,
            actions=actions,
            tier=tier,
            guardrails_passed=True,  # 通過 TrustEngine 評估
            rejection_reason=None,
            llm_provider=llm_provider,
            llm_confidence=llm_confidence,
            kubectl_command=kubectl_command if kubectl_command else None,
        )

    except HTTPException:
        raise
    except Exception as e:
        logger.exception(
            "proposal_generation_error",
            incident_id=incident_id,
            error=str(e),
        )
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=f"Internal Error: {str(e)}"
        ) from e