"""Microsoft Teams AI 反馈系统。

在 AI 回复消息中附加 Teams Native 拇指标反馈按钮，
处理 feedback invoke 交互，以及负面反馈反思流水线。
"""

from __future__ import annotations

import json
import re
import time
from pathlib import Path
from typing import Any

from yuxi.utils.logging_config import logger

FEEDBACK_INVOKE_NAME = "message/submitAction"

_REFLECTION_PROMPT_TEMPLATE = (
    "You received a 👎 negative feedback on your last response. "
    "Please think about what went wrong and write a brief, constructive self-reflection "
    "to help you improve future responses. Focus on specific improvements.\n\n"
    "The user's message was:\n{user_message}\n\nYour response was:\n{bot_response}"
)

_CODE_BLOCK_PATTERN = re.compile(r"```(?:json)?\s*([\s\S]*?)```", re.IGNORECASE)
_DEFAULT_REFLECTION_COOLDOWN_MS = 300000
_LEARNINGS_STORE_FILENAME = "msteams-feedback-learnings.json"


def build_feedback_channel_data(
    feedback_enabled: bool = True,
    feedback_reflection: bool = False,
    feedback_reflection_cooldown_ms: int = _DEFAULT_REFLECTION_COOLDOWN_MS,
) -> dict[str, Any] | None:
    if not feedback_enabled:
        return None
    data: dict[str, Any] = {"feedbackLoopEnabled": True}
    if feedback_reflection:
        data["feedbackReflection"] = True
        data["feedbackReflectionCooldownMs"] = feedback_reflection_cooldown_ms
    return data


def build_feedback_activity(text: str, feedback_enabled: bool = True) -> dict[str, Any]:
    activity: dict[str, Any] = {
        "type": "message",
        "text": text,
        "textFormat": "markdown",
    }
    channel_data = build_feedback_channel_data(feedback_enabled=feedback_enabled)
    if channel_data:
        activity["channelData"] = channel_data
    return activity


def is_feedback_invoke(activity: dict[str, Any]) -> bool:
    return activity.get("name", "") == FEEDBACK_INVOKE_NAME


def parse_feedback_value(activity: dict[str, Any]) -> str:
    value = activity.get("value", {}) or {}
    return str(value.get("feedbackValue", ""))


def build_reflection_prompt(user_message: str, bot_response: str) -> str:
    return _REFLECTION_PROMPT_TEMPLATE.format(
        user_message=user_message[:2000],
        bot_response=bot_response[:2000],
    )


def parse_reflection_response(raw: str) -> str:
    """多路径解析反思响应：JSON.parse → 代码块提取 → 安全回退。"""
    json_result = _try_parse_json_reflection(raw)
    if json_result:
        return json_result

    block_result = _try_extract_code_block(raw)
    if block_result:
        return block_result

    return raw.strip()[:1000]


def _try_parse_json_reflection(raw: str) -> str:
    try:
        data = json.loads(raw)
        if isinstance(data, dict):
            return str(data.get("reflection", "") or data.get("thought", "") or "")
        if isinstance(data, list):
            return " ".join(str(item) for item in data)[:1000]
        return str(data)
    except (json.JSONDecodeError, TypeError):
        return ""


def _try_extract_code_block(raw: str) -> str:
    m = _CODE_BLOCK_PATTERN.search(raw)
    if m:
        inner = m.group(1).strip()
        json_result = _try_parse_json_reflection(inner)
        return json_result or inner[:1000]
    return ""


class FeedbackLearningsStore:
    """Session-level 学习存储：持久化用户过反馈的反思结果。"""

    def __init__(self, storage_dir: str | None = None):
        self._storage_dir = Path(storage_dir or str(Path.home() / ".yuxi" / "msteams"))
        self._storage_dir.mkdir(parents=True, exist_ok=True)
        self._learnings: dict[str, list[dict[str, Any]]] = {}
        self._cooldowns: dict[str, float] = {}
        self._load()

    @property
    def file_path(self) -> Path:
        return self._storage_dir / _LEARNINGS_STORE_FILENAME

    def _load(self) -> None:
        if not self.file_path.exists():
            return
        try:
            data = json.loads(self.file_path.read_text(encoding="utf-8"))
            self._learnings = data.get("learnings", {})
            self._cooldowns = data.get("cooldowns", {})
        except (json.JSONDecodeError, OSError):
            pass

    def _save(self) -> None:
        try:
            self.file_path.write_text(
                json.dumps(
                    {"learnings": self._learnings, "cooldowns": self._cooldowns},
                    ensure_ascii=False,
                    indent=2,
                ),
                encoding="utf-8",
            )
        except OSError as e:
            logger.error(f"MSTeams learnings: failed to save: {e}")

    def is_reflection_allowed(self, session_key: str, cooldown_ms: int = _DEFAULT_REFLECTION_COOLDOWN_MS) -> bool:
        last = self._cooldowns.get(session_key, 0)
        elapsed = (time.monotonic() - last) * 1000
        return elapsed >= cooldown_ms

    def store_session_learning(self, session_key: str, user_message: str, bot_response: str, reflection: str) -> None:
        entry = {
            "timestamp": time.time(),
            "user_message": user_message[:500],
            "bot_response": bot_response[:500],
            "reflection": reflection[:1000],
        }
        if session_key not in self._learnings:
            self._learnings[session_key] = []
        self._learnings[session_key].append(entry)
        if len(self._learnings[session_key]) > 50:
            self._learnings[session_key] = self._learnings[session_key][-50:]
        self._cooldowns[session_key] = time.monotonic()
        self._save()

    def load_session_learnings(self, session_key: str) -> list[dict[str, Any]]:
        return self._learnings.get(session_key, [])[-10:]

    def clear_reflection_cooldowns(self, session_key: str | None = None) -> None:
        if session_key:
            self._cooldowns.pop(session_key, None)
        else:
            self._cooldowns.clear()
        self._save()

    def clear(self) -> None:
        self._learnings.clear()
        self._cooldowns.clear()
        self._save()

    @property
    def session_count(self) -> int:
        return len(self._learnings)


def process_feedback(
    activity: dict[str, Any],
    on_positive: Any = None,
    on_negative: Any = None,
) -> dict[str, Any]:
    feedback_value = parse_feedback_value(activity)
    from_info = activity.get("from", {}) or {}
    user_id = from_info.get("aadObjectId", "") or from_info.get("id", "")
    user_name = from_info.get("name", "")

    is_positive = feedback_value == "positive"
    is_negative = feedback_value == "negative"

    result: dict[str, Any] = {
        "feedback_value": feedback_value,
        "is_positive": is_positive,
        "is_negative": is_negative,
        "user_id": user_id,
        "user_name": user_name,
        "reply_to_id": activity.get("replyToId", ""),
    }

    if is_negative:
        logger.info(f"MSTeams negative feedback received from {user_name} ({user_id})")
    elif is_positive:
        logger.info(f"MSTeams positive feedback received from {user_name} ({user_id})")

    return result