from __future__ import annotations

from .shared import *
from .helpers import _utc_now_naive

@router.get("/stats")
async def get_ai_steward_stats(
    current_user: User = Depends(get_current_user),
    db: AsyncSession = Depends(get_db),
    tenant_id: int = Depends(get_tenant_id)
):
    """
    获取 AI Steward 统计数据

    展示 AI 为团队节省的工作量：
    - 自动捕获的信号数
    - 避免的认知冲突数
    - 自动解决的阻塞数
    - 节省的会议时间（小时）
    """
    from ...models import Signal, CognitiveConflict
    from sqlalchemy import func, text

    # 统计信号数量
    week_ago = _utc_now_naive() - timedelta(days=7)
    month_ago = _utc_now_naive() - timedelta(days=30)

    signal_result = await db.execute(
        select(func.count(Signal.id)).where(
            Signal.tenant_id == tenant_id,
            Signal.created_at >= week_ago
        )
    )
    signals_captured = signal_result.scalar() or 0

    # 统计高影响信号
    high_impact_result = await db.execute(
        select(func.count(Signal.id)).where(
            Signal.tenant_id == tenant_id,
            Signal.impact_score >= 0.8,
            Signal.created_at >= week_ago
        )
    )
    high_impact_count = high_impact_result.scalar() or 0

    # 统计认知冲突
    conflict_result = await db.execute(
        select(func.count(CognitiveConflict.id)).where(
            CognitiveConflict.tenant_id == tenant_id,
            CognitiveConflict.status.in_(['detected', 'active'])
        )
    )
    active_conflicts = conflict_result.scalar() or 0

    # 统计已解决的冲突
    resolved_conflict_result = await db.execute(
        select(func.count(CognitiveConflict.id)).where(
            CognitiveConflict.tenant_id == tenant_id,
            CognitiveConflict.status == 'resolved',
            CognitiveConflict.resolved_at >= month_ago
        )
    )
    conflicts_resolved = resolved_conflict_result.scalar() or 0

    # 统计阻塞
    blocker_result = await db.execute(
        text("""
            SELECT COUNT(*) FROM cognitive_conflicts
            WHERE tenant_id = :tenant_id
            AND conflict_type IN ('code_blocker', 'task_blocker')
            AND status = 'detected'
        """),
        {"tenant_id": tenant_id}
    )
    active_blockers = blocker_result.scalar() or 0

    # 活跃项目数
    from ...models import Project
    project_result = await db.execute(
        select(func.count(Project.id)).where(
            Project.tenant_id == tenant_id,
            Project.status == 'active'
        )
    )
    active_projects = project_result.scalar() or 0

    # 估算节省时间（每个冲突/阻塞避免约2小时会议）
    hours_saved = conflicts_resolved * 2

    stats = {
        'signals_captured': signals_captured,
        'high_impact_signals': high_impact_count,
        'active_conflicts': active_conflicts,
        'conflicts_resolved': conflicts_resolved,
        'active_blockers': active_blockers,
        'active_projects': active_projects,
        'hours_saved': hours_saved,
        'last_updated': now_cn_aware().isoformat()
    }

    return stats

@router.get("/config/suggest")
async def get_ai_config_suggestion(
    current_user: User = Depends(get_current_user),
    db: AsyncSession = Depends(get_db),
    tenant_id: int = Depends(get_tenant_id)
):
    """
    AI 建议配置方案

    根据团队现状，智能推荐应该优先配置哪些集成源
    """
    from ...models import Integration, Project, Signal
    from sqlalchemy.future import select
    from sqlalchemy import func

    # 检查已配置的集成
    configured_sources = []

    # 检查飞书配置
    feishu_result = await db.execute(
        select(Integration).where(
            Integration.tenant_id == tenant_id,
            Integration.type == 'feishu',
            Integration.is_active == True
        )
    )
    if feishu_result.scalar_one_or_none():
        configured_sources.append('feishu')

    # 检查活跃项目数
    project_result = await db.execute(
        select(func.count(Project.id)).where(
            Project.tenant_id == tenant_id,
            Project.status == 'active'
        )
    )
    active_projects = project_result.scalar() or 0

    # 检查近期信号数量
    signal_result = await db.execute(
        select(func.count(Signal.id)).where(
            Signal.tenant_id == tenant_id,
            Signal.created_at >= (_utc_now_naive() - timedelta(days=7))
        )
    )
    recent_signals = signal_result.scalar() or 0

    # AI 分析并推荐配置
    recommended = []
    reasoning_parts = []

    # 根据项目数量推荐
    if active_projects > 0:
        if 'feishu' not in configured_sources:
            recommended.append('feishu')
            reasoning_parts.append(f"您有 {active_projects} 个活跃项目，建议配置飞书群聊集成以自动捕获需求讨论")

    # 根据信号量推荐
    if recent_signals < 10:
        if 'hn' not in [s.split('_')[0] for s in configured_sources]:
            recommended.append('hn')
            reasoning_parts.append("近期信号较少，建议启用 HackerNews 监控获取更多技术趋势")

    # 默认推荐
    if not recommended:
        if 'feishu' not in configured_sources:
            recommended.append('feishu')
        if 'hn' not in [s.split('_')[0] for s in configured_sources]:
            recommended.append('hn')
        reasoning_parts.append("建议配置基础集成源以启动 AI 主动捕获能力")

    reasoning = "；".join(reasoning_parts) if reasoning_parts else "所有基础集成已配置，AI 主动捕获能力已全面启用"

    # 估算影响
    impact_levels = {
        0: "暂无明显影响",
        1: "预计每周节省 2 小时手动录入时间",
        2: "预计每周节省 4 小时手动录入时间，捕获 10+ 个需求信号",
        3: "预计每周节省 6 小时会议时间，自动对齐 5+ 个认知冲突",
        4: "预计每周节省 8 小时会议时间，主动捕获 20+ 个需求信号"
    }
    impact = impact_levels.get(len(recommended), impact_levels[4])

    return {
        'recommended_sources': recommended,
        'reasoning': reasoning,
        'estimated_impact': impact,
        'current_config': {
            'feishu_configured': 'feishu' in configured_sources,
            'hn_configured': any('hn' in s for s in configured_sources),
            'github_configured': any('github' in s for s in configured_sources),
            'browser_installed': False  # 需要前端检测
        }
    }