"""FlowPilot Analytics API — MTTR, resolution rates, knowledge coverage.

Endpoints:
  GET /analytics/flowpilot?period=30d — Main dashboard data
  GET /analytics/flowpilot/knowledge-gaps — Knowledge gap report
"""
import logging
from datetime import datetime, timezone, timedelta
from typing import Annotated, Optional

from fastapi import APIRouter, Depends, HTTPException, Query, Request, status
from sqlalchemy import select, func, case, cast, Date, extract
from sqlalchemy.ext.asyncio import AsyncSession

from app.core.rate_limit import limiter
from app.api.deps import get_current_active_user, get_db, require_team_admin
from app.models.user import User
from app.models.tree import Tree
from app.models.ai_session import AISession
from app.models.flow_proposal import FlowProposal
from app.models.psa_post_log import PsaPostLog
from app.schemas.flowpilot_analytics import (
    FlowPilotDashboard,
    MTTRDataPoint,
    DomainBreakdown,
    ConfidenceBreakdown,
    KnowledgeCoverage,
    DomainCoverage,
    PsaMetrics,
)
from app.services.knowledge_gap_service import get_knowledge_gaps, KnowledgeGapReport

logger = logging.getLogger(__name__)

router = APIRouter(prefix="/analytics/flowpilot", tags=["flowpilot-analytics"])


def _get_period_start(period: str) -> datetime:
    days = {"7d": 7, "30d": 30, "90d": 90}.get(period, 30)
    return datetime.now(timezone.utc) - timedelta(days=days)


@router.get("", response_model=FlowPilotDashboard)
@limiter.limit("15/minute")
async def get_dashboard(
    request: Request,
    current_user: Annotated[User, Depends(get_current_active_user)],
    db: Annotated[AsyncSession, Depends(get_db)],
    _: None = Depends(require_team_admin),
    period: str = Query("30d", pattern="^(7d|30d|90d)$"),
):
    """Get FlowPilot analytics dashboard data."""
    if not current_user.account_id:
        raise HTTPException(status_code=status.HTTP_403_FORBIDDEN, detail="No account")

    account_id = current_user.account_id
    period_start = _get_period_start(period)

    # ── Session counts ──
    counts_result = await db.execute(
        select(
            func.count(AISession.id).label("total"),
            func.sum(case((AISession.status == "resolved", 1), else_=0)).label("resolved"),
            func.sum(case((AISession.status.in_(["escalated", "requesting_escalation"]), 1), else_=0)).label("escalated"),
            func.sum(case((AISession.status == "abandoned", 1), else_=0)).label("abandoned"),
            func.avg(case((AISession.status == "resolved", AISession.step_count), else_=None)).label("avg_steps"),
            func.avg(AISession.session_rating).label("avg_rating"),
        )
        .where(
            AISession.account_id == account_id,
            AISession.created_at >= period_start,
        )
    )
    row = counts_result.one()
    total = row.total or 0
    resolved = row.resolved or 0
    escalated = row.escalated or 0
    abandoned = row.abandoned or 0
    avg_steps = float(row.avg_steps or 0)
    avg_rating = float(row.avg_rating) if row.avg_rating else None
    resolution_rate = (resolved / total * 100) if total > 0 else 0.0

    # ── MTTR ──
    mttr_result = await db.execute(
        select(
            func.avg(
                extract("epoch", AISession.resolved_at - AISession.created_at) / 60
            ).label("avg_mttr"),
        )
        .where(
            AISession.account_id == account_id,
            AISession.created_at >= period_start,
            AISession.status == "resolved",
            AISession.resolved_at.isnot(None),
        )
    )
    mttr_row = mttr_result.one()
    mttr_minutes = float(mttr_row.avg_mttr) if mttr_row.avg_mttr else None

    # ── Average duration ──
    duration_result = await db.execute(
        select(
            func.avg(
                extract("epoch", AISession.resolved_at - AISession.created_at) / 60
            ).label("avg_duration"),
        )
        .where(
            AISession.account_id == account_id,
            AISession.created_at >= period_start,
            AISession.resolved_at.isnot(None),
        )
    )
    dur_row = duration_result.one()
    avg_duration = float(dur_row.avg_duration) if dur_row.avg_duration else 0.0

    # ── MTTR trend ──
    mttr_trend_result = await db.execute(
        select(
            cast(AISession.resolved_at, Date).label("day"),
            func.avg(
                extract("epoch", AISession.resolved_at - AISession.created_at) / 60
            ).label("mttr"),
            func.count(AISession.id).label("count"),
        )
        .where(
            AISession.account_id == account_id,
            AISession.created_at >= period_start,
            AISession.status == "resolved",
            AISession.resolved_at.isnot(None),
        )
        .group_by(cast(AISession.resolved_at, Date))
        .order_by(cast(AISession.resolved_at, Date))
    )
    mttr_trend = [
        MTTRDataPoint(
            date=str(r.day),
            mttr_minutes=round(float(r.mttr or 0), 1),
            session_count=r.count,
        )
        for r in mttr_trend_result.all()
    ]

    # ── Domain breakdown ──
    domain_result = await db.execute(
        select(
            AISession.problem_domain,
            func.count(AISession.id).label("total"),
            func.sum(case((AISession.status == "resolved", 1), else_=0)).label("resolved"),
            func.sum(case((AISession.status.in_(["escalated", "requesting_escalation"]), 1), else_=0)).label("escalated"),
        )
        .where(
            AISession.account_id == account_id,
            AISession.created_at >= period_start,
            AISession.problem_domain.isnot(None),
        )
        .group_by(AISession.problem_domain)
        .order_by(func.count(AISession.id).desc())
    )
    sessions_by_domain = [
        DomainBreakdown(
            domain=r.problem_domain or "unknown",
            total=r.total,
            resolved=r.resolved or 0,
            escalated=r.escalated or 0,
            resolution_rate=round((r.resolved or 0) / r.total * 100, 1) if r.total > 0 else 0.0,
        )
        for r in domain_result.all()
    ]

    # ── Confidence breakdown ──
    confidence_result = await db.execute(
        select(
            AISession.confidence_tier,
            func.count(AISession.id).label("total"),
            func.sum(case((AISession.status == "resolved", 1), else_=0)).label("resolved"),
        )
        .where(
            AISession.account_id == account_id,
            AISession.created_at >= period_start,
            AISession.status.in_(["resolved", "escalated", "requesting_escalation"]),
        )
        .group_by(AISession.confidence_tier)
    )
    conf_data = {r.confidence_tier: (r.total or 0, r.resolved or 0) for r in confidence_result.all()}

    guided_total, guided_resolved = conf_data.get("guided", (0, 0))
    exploring_total, exploring_resolved = conf_data.get("exploring", (0, 0))
    discovery_total, discovery_resolved = conf_data.get("discovery", (0, 0))

    confidence_breakdown = ConfidenceBreakdown(
        guided_sessions=guided_total,
        guided_resolution_rate=round(guided_resolved / guided_total * 100, 1) if guided_total > 0 else 0.0,
        exploring_sessions=exploring_total,
        exploring_resolution_rate=round(exploring_resolved / exploring_total * 100, 1) if exploring_total > 0 else 0.0,
        discovery_sessions=discovery_total,
        discovery_resolution_rate=round(discovery_resolved / discovery_total * 100, 1) if discovery_total > 0 else 0.0,
    )

    # ── Knowledge coverage ──
    total_flows_result = await db.execute(
        select(func.count(Tree.id)).where(Tree.account_id == account_id)
    )
    total_flows = total_flows_result.scalar() or 0

    ai_flows_result = await db.execute(
        select(func.count(Tree.id)).where(
            Tree.account_id == account_id,
            Tree.origin.in_(["ai_generated", "ai_enhanced"]),
        )
    )
    ai_generated_flows = ai_flows_result.scalar() or 0

    pending_proposals_result = await db.execute(
        select(func.count(FlowProposal.id)).where(
            FlowProposal.account_id == account_id,
            FlowProposal.status == "pending",
        )
    )
    total_proposals_pending = pending_proposals_result.scalar() or 0

    approved_result = await db.execute(
        select(func.count(FlowProposal.id)).where(
            FlowProposal.account_id == account_id,
            FlowProposal.reviewed_at >= period_start,
            FlowProposal.status.in_(["approved", "modified"]),
        )
    )
    proposals_approved = approved_result.scalar() or 0

    rejected_result = await db.execute(
        select(func.count(FlowProposal.id)).where(
            FlowProposal.account_id == account_id,
            FlowProposal.reviewed_at >= period_start,
            FlowProposal.status == "rejected",
        )
    )
    proposals_rejected = rejected_result.scalar() or 0

    # Domain coverage
    domain_coverage_result = await db.execute(
        select(
            AISession.problem_domain,
            func.count(AISession.id).label("session_count"),
            func.sum(case((AISession.confidence_tier == "guided", 1), else_=0)).label("guided_count"),
        )
        .where(
            AISession.account_id == account_id,
            AISession.created_at >= period_start,
            AISession.problem_domain.isnot(None),
        )
        .group_by(AISession.problem_domain)
    )
    domain_flow_counts_result = await db.execute(
        select(
            Tree.tree_type,  # Reuse as domain proxy — not ideal but workable
            func.count(Tree.id),
        )
        .where(Tree.account_id == account_id)
        .group_by(Tree.tree_type)
    )
    # For now, flow_count per domain isn't directly available since Tree doesn't have problem_domain.
    # Use match_keywords or just report 0. We'll improve this in Phase 4 with better flow categorization.
    domain_cov_data = {}
    for r in domain_coverage_result.all():
        domain = r.problem_domain or "unknown"
        sc = r.session_count or 0
        gc = r.guided_count or 0
        domain_cov_data[domain] = DomainCoverage(
            domain=domain,
            flow_count=0,  # TODO: match via category/tags in Phase 4
            session_count=sc,
            guided_rate=round(gc / sc * 100, 1) if sc > 0 else 0.0,
        )

    knowledge_coverage = KnowledgeCoverage(
        total_flows=total_flows,
        ai_generated_flows=ai_generated_flows,
        total_proposals_pending=total_proposals_pending,
        proposals_approved_this_period=proposals_approved,
        proposals_rejected_this_period=proposals_rejected,
        coverage_by_domain=list(domain_cov_data.values()),
    )

    # ── PSA metrics ──
    psa_metrics = None
    psa_linked = await db.execute(
        select(func.count(AISession.id)).where(
            AISession.account_id == account_id,
            AISession.created_at >= period_start,
            AISession.psa_ticket_id.isnot(None),
        )
    )
    psa_linked_count = psa_linked.scalar() or 0

    if psa_linked_count > 0 and total > 0:
        psa_push_result = await db.execute(
            select(
                func.count(PsaPostLog.id).label("total_pushes"),
                func.sum(case((PsaPostLog.status == "success", 1), else_=0)).label("first_success"),
                func.sum(case(
                    ((PsaPostLog.status == "success") & (PsaPostLog.retry_count > 0), 1),
                    else_=0
                )).label("retry_success"),
            )
            .join(AISession, PsaPostLog.ai_session_id == AISession.id)
            .where(
                AISession.account_id == account_id,
                PsaPostLog.ai_session_id.isnot(None),
                PsaPostLog.posted_at >= period_start,
            )
        )
        push_row = psa_push_result.one()
        total_pushes = push_row.total_pushes or 0
        first_success = push_row.first_success or 0
        retry_success = push_row.retry_success or 0

        psa_metrics = PsaMetrics(
            ticket_link_rate=round(psa_linked_count / total * 100, 1),
            auto_push_success_rate=round(first_success / total_pushes * 100, 1) if total_pushes > 0 else 0.0,
            auto_push_retry_success_rate=round(retry_success / total_pushes * 100, 1) if total_pushes > 0 else 0.0,
            total_time_entries_logged=0,  # TODO: track from CW time entries
            total_hours_logged=0.0,
        )

    return FlowPilotDashboard(
        period=period,
        total_sessions=total,
        resolved_sessions=resolved,
        escalated_sessions=escalated,
        abandoned_sessions=abandoned,
        resolution_rate=round(resolution_rate, 1),
        avg_steps_to_resolution=round(avg_steps, 1),
        avg_session_duration_minutes=round(avg_duration, 1),
        avg_rating=round(avg_rating, 2) if avg_rating else None,
        mttr_minutes=round(mttr_minutes, 1) if mttr_minutes else None,
        mttr_trend=mttr_trend,
        sessions_by_domain=sessions_by_domain,
        confidence_breakdown=confidence_breakdown,
        knowledge_coverage=knowledge_coverage,
        psa_metrics=psa_metrics,
    )


@router.get("/knowledge-gaps", response_model=KnowledgeGapReport)
@limiter.limit("10/minute")
async def get_knowledge_gaps_endpoint(
    request: Request,
    current_user: Annotated[User, Depends(get_current_active_user)],
    db: Annotated[AsyncSession, Depends(get_db)],
    _: None = Depends(require_team_admin),
    period: str = Query("30d", pattern="^(7d|30d|90d)$"),
):
    """Get knowledge gap analysis report."""
    if not current_user.account_id:
        raise HTTPException(status_code=status.HTTP_403_FORBIDDEN, detail="No account")

    days = {"7d": 7, "30d": 30, "90d": 90}.get(period, 30)
    return await get_knowledge_gaps(current_user.account_id, db, period_days=days)