# Project Aura — 主引擎
# 将调度器 + 感知 + 决策 + 记忆 + 生成 + 控制 + 伦理 整合为单一系统

import time
import threading
from dataclasses import dataclass
from typing import Optional, Callable

from .config import AuraConfig
from .perception import PerceptionEngine, UserContext, DeviceMode, TimeOfDay
from .decision import ValueNetwork, AntiSpamFunnel, ValueScore
from .scheduler import HeartbeatScheduler, DualSystemScheduler, FastSystem
from .memory import MemoryGraph, MemoryItem
from .generation import ContentGenerator, GenerationInput, GenerationMode
from .control import ControlPanel, ThermostatMode, SceneMode
from .ethics import PrivacyLedger, RedTeamSimulator
from .interaction import ExpressionLayer, ExpressionOutput, NotificationLevel


class AuraEngine:
    """
    Project Aura 主引擎
    
    整合所有模块，提供统一的主动交互接口。
    使用方式:
        engine = AuraEngine()
        engine.start()          # 启动心跳循环
        engine.on_tick()        # 手动触发一次决策
    """

    def __init__(self, config: AuraConfig = None):
        self.config = config or AuraConfig()
        
        # 子模块
        self.perception = PerceptionEngine()
        self.memory = MemoryGraph()
        self.value_net = ValueNetwork()
        self.antispam = AntiSpamFunnel()
        self.fast = FastSystem()
        self.scheduler = DualSystemScheduler()
        self.generator = ContentGenerator(self.config)
        self.expression = ExpressionLayer()
        self.control = ControlPanel()
        self.privacy = PrivacyLedger()
        
        # 心跳
        self._heartbeat = HeartbeatScheduler()
        self._heartbeat.set_callback(self._on_heartbeat)
        
        # 状态
        self._running = False
        self._on_message: Optional[Callable] = None  # 输出回调

    def set_message_callback(self, callback: Callable[[ExpressionOutput], None]):
        """设置消息输出回调"""
        self._on_message = callback

    def load_model(self):
        """加载 DeepSeek R1"""
        self.generator.load_model()

    def start(self):
        """启动系统"""
        if self._running:
            return
        self._running = True
        self._heartbeat.start()
        print("[Aura] ✅ 系统已启动")

    def stop(self):
        """停止系统"""
        self._running = False
        self._heartbeat.stop()
        self.generator.unload()
        print("[Aura] ⏹ 系统已停止")

    def user_interacted(self, text: str = "", mood: str = ""):
        """用户发生交互时调用"""
        self.perception.update(is_active_session=True)
        if text:
            # 更新情绪
            from .perception import UserMood
            detected = self.perception.detect_mood_from_text(text)
            if detected != UserMood.UNKNOWN:
                self.perception.update(mood=detected.value)
            # 存入记忆
            item = MemoryItem(
                id=f"conv_{int(time.time())}",
                content=text[:200],
                memory_type="conversation",
                tags=[mood] if mood else [],
            )
            self.memory.add(item)

    def user_went_idle(self):
        """用户进入空闲状态"""
        self.perception.update(is_active_session=False)

    def _on_heartbeat(self):
        """心跳回调"""
        self.on_tick()

    def on_tick(self):
        """执行一次完整的决策循环"""
        if not self._running:
            return

        # 1. 感知：获取当前上下文
        ctx = self.perception.tick()
        
        # 确保字段是枚举类型
        if isinstance(ctx.device_mode, str):
            from .perception import DeviceMode
            ctx.device_mode = DeviceMode(ctx.device_mode)
        if isinstance(ctx.time_of_day, str):
            from .perception import TimeOfDay
            try:
                ctx.time_of_day = TimeOfDay(ctx.time_of_day)
            except:
                pass
        if isinstance(ctx.mood, str):
            from .perception import UserMood
            try:
                ctx.mood = UserMood(ctx.mood)
            except:
                pass
        
        # 2. 构建决策上下文
        decision_ctx = {
            "user_id": ctx.user_id,
            "idle_minutes": ctx.idle_minutes,
            "time_of_day": ctx.time_of_day.value,
            "device_mode": ctx.device_mode.value,
            "is_active_session": ctx.is_active_session,
            "mood": ctx.mood.value,
            "last_conversation_topic": ctx.last_conversation_topic or "",
            "upcoming_events": ctx.upcoming_events,
        }

        # 3. 价值评估
        score = self.value_net.evaluate(
            idle_minutes=ctx.idle_minutes,
            time_of_day=ctx.time_of_day.value,
            device_mode=ctx.device_mode.value,
            is_active_session=ctx.is_active_session,
            mood=ctx.mood.value,
            topic_match=0.5 if ctx.last_conversation_topic else 0.0,
            upcoming_events=ctx.upcoming_events,
        )

        # 4. 决策阈值
        threshold = self.control.get_decision_threshold()
        
        # 5. 反骚扰过滤
        topic = ctx.last_conversation_topic or ""
        allowed, reason = self.antispam.filter(score, topic=topic)
        if not allowed:
            return

        # 6. 场景控制
        if not self.control.can_act_on_topic(topic):
            return

        # 7. 生成内容
        if score.score >= threshold:
            mode = self._select_mode(score, ctx)
            gen_input = GenerationInput(
                mode=mode,
                user_context=decision_ctx,
                topic=topic,
                user_mood=ctx.mood.value,
                memory_context=[m.content for m in self.memory.recall_recent(5)],
            )
            result = self.generator.generate(gen_input)

            # 8. 表达
            output = self.expression.render(result.text, result.confidence)
            
            # 9. 隐私记录
            if result.confidence > 0:
                self.privacy.record_access(
                    data_type="model_inference",
                    purpose=f"主动{result.mode}",
                    is_local=True,
                )

            # 10. 记录执行
            self.antispam.record_action(topic=topic)
            self.perception.update(is_active_session=True)

            # 11. 回调
            if self._on_message:
                self._on_message(output)
            else:
                print(f"\n[Aura] 💬 [{mode.value}] {result.text}")

    def _select_mode(self, score: ValueScore, ctx: UserContext) -> GenerationMode:
        """根据场景选择合适的生成模式"""
        if score.urgency > 0.7 and ctx.time_of_day == TimeOfDay.NIGHT:
            return GenerationMode.CARING
        if ctx.last_conversation_topic and score.relevance > 0.5:
            return GenerationMode.CONTINUE_TOPIC
        if ctx.upcoming_events:
            return GenerationMode.REMINDING
        if score.urgency > 0.5:
            return GenerationMode.CARING
        return GenerationMode.CHECK_IN