#!/usr/bin/env python3
"""
学习层 — 反思·抽象·应用·探索
===============================
小唯的最高认知层，把经验转化为能力。

三层学习:
  1. 事实学习: "xxx模型在yyy时段慢" → 织忆
  2. 技能学习: "做调研的最佳流程是A→B→C" → skill
  3. 元学习: "我缺少yyy能力" → 主动探索

用法:
  learner.py reflect    → 反思近期经验，提取教训
  learner.py learn      → 执行学习循环（产出 skill/配置）
  learner.py plan       → 生成学习计划（下次学什么）
  learner.py status     → 查看学习进度
"""

import json, os, re, sys, time, subprocess
from datetime import datetime, timezone
from collections import defaultdict, Counter

HOME = os.path.expanduser("~")
HERMES = HOME + "/.hermes"
D = HERMES + "/daemon"
LEARNER_DIR = HERMES + "/learner"
STATE_FILE = LEARNER_DIR + "/state.json"
SKILL_HEALTH = HERMES + "/skill-health.json"
OPT_REPORT = HERMES + "/optimization-report.json"


def log(msg):
    ts = datetime.now().strftime("%H:%M:%S")
    print(f"[LEARN] {ts} {msg}", flush=True)


def load_state():
    os.makedirs(LEARNER_DIR, exist_ok=True)
    if os.path.exists(STATE_FILE):
        with open(STATE_FILE) as f:
            return json.load(f)
    return {
        "version": 1,
        "created_at": datetime.now(timezone.utc).isoformat(),
        "total_cycles": 0,
        "skills_created": 0,
        "skills_archived": 0,
        "configs_changed": 0,
        "memories_added": 0,
        "learned_items": [],
        "in_progress": [],
        "tracked_metrics": {
            "avg_skill_score": [],
            "health_score": [],
            "model_stable_rate": [],
        },
    }


def save_state(state):
    os.makedirs(LEARNER_DIR, exist_ok=True)
    with open(STATE_FILE, "w") as f:
        json.dump(state, f, indent=2, ensure_ascii=False)


def shell(cmd, timeout=10):
    try:
        r = subprocess.run(cmd, shell=True, capture_output=True, text=True, timeout=timeout)
        return r.returncode, r.stdout.strip()[:500], r.stderr.strip()[:200]
    except:
        return -1, "", "timeout"


# ====== 采集经验 ======

def collect_experiences():
    """从各数据源采集近期经验"""
    experiences = []
    
    # 1. Daemon journal: 近期事件
    jf = D + "/journal.jsonl"
    if os.path.exists(jf):
        with open(jf) as f:
            for line in f:
                try:
                    entry = json.loads(line)
                    ts = entry.get("timestamp", "")
                    # 只看最近24h
                    if ts:
                        try:
                            t = datetime.fromisoformat(ts)
                            if (datetime.now(timezone.utc) - t).total_seconds() > 86400:
                                continue
                        except:
                            pass
                    experiences.append({
                        "source": "daemon",
                        "type": entry.get("type", "unknown"),
                        "summary": entry.get("summary", ""),
                        "timestamp": ts,
                    })
                except:
                    pass
    
    # 2. Skill health: 技能质量趋势
    if os.path.exists(SKILL_HEALTH):
        with open(SKILL_HEALTH) as f:
            try:
                report = json.load(f)
                s = report.get("summary", {})
                experiences.append({
                    "source": "skill_health",
                    "type": "snapshot",
                    "summary": f"技能: {s.get('active',0)}活跃, 均分{s.get('avg_score',0)}, {s.get('needs_attention',0)}需关注",
                })
            except:
                pass
    
    # 3. Daemon context: 运行状态
    cf = D + "/context.json"
    if os.path.exists(cf):
        with open(cf) as f:
            try:
                ctx = json.load(f)
                experiences.append({
                    "source": "daemon_state",
                    "type": "state",
                    "summary": f"Daemon: {ctx.get('tick_count',0)}ticks, {ctx.get('solved_count',0)}已解决, {ctx.get('learned_count',0)}已学会",
                })
            except:
                pass
    
    # 4. 优化报告
    if os.path.exists(OPT_REPORT):
        with open(OPT_REPORT) as f:
            try:
                report = json.load(f)
                experiences.append({
                    "source": "optimizer",
                    "type": "health",
                    "summary": f"健康分: {report.get('health_score', '?')}/100, 瓶颈: {len(report.get('bottlenecks', []))}个",
                })
            except:
                pass
    
    return experiences


# ====== 模式提取 ======

def extract_patterns(experiences):
    """从经验中提取重复模式"""
    patterns = []
    
    # 按类型统计
    type_counts = Counter(e["type"] for e in experiences)
    
    # 告警模式
    alerts = [e for e in experiences if e["type"] in ("alert", "process_down", "error")]
    if len(alerts) >= 2:
        patterns.append({
            "type": "recurring_issue",
            "confidence": min(len(alerts) * 20, 90),
            "desc": f"近期出现 {len(alerts)} 次告警/异常",
            "details": [a["summary"] for a in alerts[:3]],
            "suggested_action": "检查看门狗日志，排查根因",
        })
    
    # 技能模式
    skill_exps = [e for e in experiences if e["source"] == "skill_health"]
    for s in skill_exps:
        if "需关注" in s["summary"]:
            # 提取数字
            nums = re.findall(r'\d+', s["summary"])
            if len(nums) >= 3 and int(nums[2]) > 50:
                patterns.append({
                    "type": "skill_quality_gap",
                    "confidence": 80,
                    "desc": f"大量技能需要关注 ({nums[2]}个)",
                    "suggested_action": "运行 skill-manager.py archive 清理低分技能",
                })
    
    # 学习进度
    solved_exps = [e for e in experiences if e["type"] in ("solve_auto", "learn", "solve_start")]
    if solved_exps:
        patterns.append({
            "type": "learning_progress",
            "confidence": 70,
            "desc": f"近期解决了 {len(solved_exps)} 个问题/学会了新方案",
            "suggested_action": "持续监控方案库的命中率",
        })
    
    return patterns


# ====== 差距分析 ======

def analyze_gaps(state):
    """分析能力差距"""
    gaps = []
    learned_names = {item["name"] for item in state.get("learned_items", [])}
    
    # 检查已有系统的覆盖度
    systems = {
        "记忆": os.path.exists(HERMES + "/plugins/zhiyi/__init__.py") or os.path.exists(HERMES + "/skills/zhiyi"),
        "技能管理": os.path.exists(HERMES + "/scripts/skill-manager.py"),
        "优化": os.path.exists(HERMES + "/scripts/optimizer.py"),
        "学习": os.path.exists(HERMES + "/scripts/learner.py"),
        "配置保护": os.path.exists(HERMES + "/scripts/config-protector.sh"),
        "ao团队": "npx" in os.popen("which npx 2>/dev/null || echo ''").read(),
        "持久意识": os.path.exists(D + "/context.json"),
    }
    
    built = sum(1 for v in systems.values() if v)
    total = len(systems)
    coverage = built / total * 100
    
    gaps.append({
        "area": "system_coverage",
        "coverage": f"{coverage:.0f}%",
        "built": built,
        "total": total,
        "missing": [k for k, v in systems.items() if not v],
    })
    
    # 技能层面差距
    if os.path.exists(SKILL_HEALTH):
        with open(SKILL_HEALTH) as f:
            try:
                report = json.load(f)
            except:
                report = {}
        s = report.get("summary", {})
        gaps.append({
            "area": "skill_quality",
            "avg_score": s.get("avg_score", 0),
            "d_count": s.get("grades", {}).get("D", 0),
            "needs_attention": s.get("needs_attention", 0),
        })
    
    # 学习进度
    gaps.append({
        "area": "learning",
        "items_learned": len(learned_names),
        "cycles_completed": state.get("total_cycles", 0),
        "in_progress": len(state.get("in_progress", [])),
    })
    
    return gaps


# ====== 学习计划 ======

def generate_plan(state, experiences, patterns, gaps):
    """生成下一步学习计划"""
    plan = {
        "generated_at": datetime.now(timezone.utc).isoformat(),
        "immediate": [],
        "short_term": [],
        "long_term": [],
    }
    
    # 从差距生成学习项
    for g in gaps:
        if g["area"] == "skill_quality" and g.get("d_count", 0) > 20:
            plan["short_term"].append({
                "task": "清理D级技能",
                "action": "skill-manager.py archive 批量归档低分技能",
                "reason": f"{g['d_count']}个D级技能降低整体质量",
                "effort": "20min",
            })
        
        if g["area"] == "learning" and g.get("items_learned", 0) == 0 and g.get("cycles_completed", 0) == 0:
            plan["immediate"].append({
                "task": "完成首次学习循环",
                "action": "运行 learner.py learn 完成首次学习闭环",
                "reason": "学习层刚建立，需要完成第一个循环验证",
                "effort": "2min",
            })
    
    # 从模式生成学习项
    for p in patterns:
        if p["type"] == "skill_quality_gap" and not any(t["task"].startswith("清理") for t in plan["short_term"]):
            plan["short_term"].append({
                "task": "提升技能库质量",
                "action": p["suggested_action"],
                "reason": p["desc"],
                "effort": "15min",
            })
    
    # 长期学习目标
    long_term_topics = [
        ("家庭服务器互联", "连上192.168.123.11的Gitea/影音/照片服务"),
        ("语音交互", "部署STT模型实现语音输入"),
        ("本地LLM推理", "安装llama.cpp或vLLM跑本地模型"),
        ("持久意识增强", "让daemon能调用更多工具自主行动"),
    ]
    
    learned_names = {item["name"] for item in state.get("learned_items", [])}
    for topic, desc in long_term_topics:
        if topic not in learned_names:
            plan["long_term"].append({
                "topic": topic,
                "desc": desc,
                "status": "not_started",
            })
    
    return plan


# ====== 执行学习 ======

def apply_learning(state, plan):
    """执行学习计划中的即时/短期项"""
    results = []
    
    for item in plan.get("immediate", []):
        log(f"  ▶ 执行: {item['task']}")
        # 记录到学习记录
        entry = {
            "name": item["task"],
            "type": "immediate",
            "learned_at": datetime.now(timezone.utc).isoformat(),
            "status": "completed",
            "detail": item["action"],
        }
        state["learned_items"].append(entry)
        state["total_cycles"] += 1
        results.append({"task": item["task"], "result": "recorded"})
    
    for item in plan.get("short_term", []):
        log(f"  📋 计划: {item['task']} ({item['effort']})")
        entry = {
            "name": item["task"],
            "type": "short_term",
            "learned_at": datetime.now(timezone.utc).isoformat(),
            "status": "planned",
            "detail": item["action"],
        }
        state["in_progress"].append(entry)
        results.append({"task": item["task"], "result": "planned"})
    
    return results


# ====== 指标追踪 ======

def update_metrics(state):
    """更新跟踪指标"""
    metrics = state.setdefault("tracked_metrics", {})
    
    # 技能平均分趋势
    if os.path.exists(SKILL_HEALTH):
        with open(SKILL_HEALTH) as f:
            try:
                report = json.load(f)
                metrics["avg_skill_score"].append({
                    "timestamp": datetime.now(timezone.utc).isoformat(),
                    "value": report.get("summary", {}).get("avg_score", 0),
                })
            except:
                pass
    
    # 健康分趋势
    if os.path.exists(OPT_REPORT):
        with open(OPT_REPORT) as f:
            try:
                report = json.load(f)
                metrics["health_score"].append({
                    "timestamp": datetime.now(timezone.utc).isoformat(),
                    "value": report.get("health_score", 0),
                })
            except:
                pass
    
    # 模型稳定率趋势
    mh = HERMES + "/model-health.json"
    if os.path.exists(mh):
        with open(mh) as f:
            try:
                data = json.load(f)
                stable = data.get("stable", 0)
                total = data.get("total_models", 1)
                metrics["model_stable_rate"].append({
                    "timestamp": datetime.now(timezone.utc).isoformat(),
                    "value": round(stable / max(total, 1) * 100, 1),
                })
            except:
                pass
    
    # 限制历史长度
    for key in metrics:
        metrics[key] = metrics[key][-50:]  # 保留最近50个


# ====== 命令入口 ======

def cmd_reflect():
    experiences = collect_experiences()
    patterns = extract_patterns(experiences)
    
    print(f"\n{'='*50}")
    print(f"  学习反思  |  {len(experiences)}条经验")
    print(f"{'='*50}")
    
    print(f"\n📋 近期经验 ({len(experiences)}条):")
    for e in experiences[-10:]:
        print(f"  [{e['source']}] {e['summary'][:80]}")
    
    if patterns:
        print(f"\n🔍 发现 {len(patterns)} 个模式:")
        for p in patterns:
            bar = "█" * (p["confidence"] // 10) + "░" * (10 - p["confidence"] // 10)
            print(f"  {bar} {p['confidence']}% {p['desc'][:60]}")
            print(f"     → {p['suggested_action']}")
    else:
        print(f"\n✅ 未发现明显模式")
    
    return experiences, patterns


def cmd_learn():
    state = load_state()
    experiences = collect_experiences()
    patterns = extract_patterns(experiences)
    gaps = analyze_gaps(state)
    plan = generate_plan(state, experiences, patterns, gaps)
    
    log(f"开始学习循环 #{state['total_cycles'] + 1}")
    
    results = apply_learning(state, plan)
    update_metrics(state)
    save_state(state)
    
    log(f"完成: {len(results)} 项")
    for r in results:
        print(f"  {r['task']}: {r['result']}")
    
    return state


def cmd_plan():
    state = load_state()
    experiences = collect_experiences()
    patterns = extract_patterns(experiences)
    gaps = analyze_gaps(state)
    plan = generate_plan(state, experiences, patterns, gaps)
    
    print(f"\n{'='*50}")
    print(f"  学习计划")
    print(f"{'='*50}")
    
    learned = len({item["name"] for item in state.get("learned_items", [])})
    in_progress = len(state.get("in_progress", []))
    print(f"\n📊 进度: 已学{learned}项 / 进行中{in_progress}项 / 共{state['total_cycles']}轮")
    
    if plan["immediate"]:
        print(f"\n⚡ 立即执行:")
        for i in plan["immediate"]:
            print(f"  {i['task']}: {i['reason']} ({i['effort']})")
    
    if plan["short_term"]:
        print(f"\n📋 短期计划:")
        for i in plan["short_term"]:
            print(f"  {i['task']}: {i['reason']} ({i['effort']})")
    
    if plan["long_term"]:
        print(f"\n🎯 长期目标:")
        for i in plan["long_term"]:
            icon = "✅" if i["status"] == "completed" else "⬜"
            print(f"  {icon} {i['topic']}: {i['desc']}")
    
    return plan


def cmd_status():
    state = load_state()
    
    print(f"\n{'='*50}")
    print(f"  学习状态")
    print(f"{'='*50}")
    
    print(f"\n📊 统计:")
    print(f"  学习循环: {state['total_cycles']} 轮")
    print(f"  已学技能: {state['skills_created']} 个")
    print(f"  归档技能: {state['skills_archived']} 个")
    print(f"  配置变更: {state['configs_changed']} 次")
    print(f"  记忆添加: {state['memories_added']} 条")
    
    print(f"\n📈 趋势:")
    metrics = state.get("tracked_metrics", {})
    for key, values in metrics.items():
        if values:
            latest = values[-1]["value"]
            trend = ""
            if len(values) > 1:
                prev = values[-2]["value"]
                diff = latest - prev
                trend = f" ({'+' if diff > 0 else ''}{diff:.1f})"
            print(f"  {key}: {latest}{trend} (共{len(values)}个采样)")
    
    learned = state.get("learned_items", [])
    if learned:
        print(f"\n📚 已学 ({len(learned)}项):")
        for item in learned[-5:]:
            print(f"  [{item['type']}] {item['name']} ({item['status']})")
    
    in_progress = state.get("in_progress", [])
    if in_progress:
        print(f"\n🔄 进行中:")
        for item in in_progress:
            print(f"  {item['name']}")
    
    long_term = ["家庭服务器互联", "语音交互", "本地LLM推理", "持久意识增强"]
    learned_names = {item["name"] for item in learned}
    not_learned = [t for t in long_term if t not in learned_names]
    if not_learned:
        print(f"\n🎯 待探索:")
        for t in not_learned:
            print(f"  ⬜ {t}")


if __name__ == "__main__":
    cmd = sys.argv[1] if len(sys.argv) > 1 else "status"
    
    if cmd == "reflect":
        cmd_reflect()
    elif cmd == "learn":
        cmd_learn()
    elif cmd == "plan":
        cmd_plan()
    elif cmd == "status":
        cmd_status()
    else:
        print(f"未知: {cmd}")
        print("可用: reflect, learn, plan, status")