import os
from pathlib import Path
from typing import Any

from pydantic import BaseModel, Field


def _load_dotenv_file() -> None:
    """环境加载：轻量读取项目根目录 `.env`，避免强依赖 python-dotenv。"""
    env_path = next(
        (parent / ".env" for parent in Path(__file__).resolve().parents if (parent / ".env").exists()),
        None,
    )
    if env_path is None:
        return
    for line in env_path.read_text(encoding="utf-8").splitlines():
        if not line or line.strip().startswith("#") or "=" not in line:
            continue
        key, value = line.split("=", 1)
        os.environ.setdefault(key.strip(), value.strip())


_load_dotenv_file()


def _env_first(*keys: str, default: str = "") -> str:
    """环境读取：按优先级读取多个环境变量。"""
    for key in keys:
        value = os.getenv(key)
        if value:
            return value
    return default


def _normalize_sync_database_url(url: str) -> str:
    """数据库连接：将异步 MySQL URL 转换为当前同步 ORM 可用的 URL。"""
    if url.startswith("mysql+aiomysql://"):
        return url.replace("mysql+aiomysql://", "mysql+pymysql://", 1)
    if url.startswith("mysql://"):
        return url.replace("mysql://", "mysql+pymysql://", 1)
    return url


def _env_bool(key: str, default: bool) -> bool:
    """布尔配置：统一解析环境变量中的 true/false 开关。"""
    return os.getenv(key, str(default)).lower() == "true"


def _env_csv(key: str, default: str = "") -> list[str]:
    """列表配置：把逗号分隔的环境变量转换为去空白列表。"""
    return [item.strip() for item in os.getenv(key, default).split(",") if item.strip()]


class Settings(BaseModel):
    """系统配置：集中管理数据库、DeepSeek、报告和短期 memory 配置。"""

    app_name: str = Field(default_factory=lambda: os.getenv("APP_NAME", "Medical Consultation Agent Demo"))
    app_env: str = Field(default_factory=lambda: os.getenv("APP_ENV", "local"))
    app_debug: bool = Field(default_factory=lambda: _env_bool("APP_DEBUG", True))
    app_root_path: str = Field(default_factory=lambda: os.getenv("APP_ROOT_PATH", ""))
    api_v1_prefix: str = Field(default_factory=lambda: os.getenv("API_V1_PREFIX", "/api/v1"))
    cors_allow_origins: list[str] = Field(
        default_factory=lambda: _env_csv(
            "CORS_ALLOW_ORIGINS",
            "http://127.0.0.1:5173,http://localhost:5173,http://127.0.0.1:5174,http://localhost:5174",
        )
    )
    cors_allow_origin_regex: str = Field(
        default_factory=lambda: os.getenv(
            "CORS_ALLOW_ORIGIN_REGEX",
            r"^http://(127\.0\.0\.1|localhost|192\.168\.\d+\.\d+):\d+$",
        )
    )

    mysql_url: str = Field(default_factory=lambda: os.getenv("MYSQL_URL", ""))
    database_url: str = Field(
        default_factory=lambda: _normalize_sync_database_url(
            _env_first("DATABASE_URL", "MYSQL_URL", default="sqlite:///./storage/demo.db")
        )
    )

    llm_api_key: str = Field(default_factory=lambda: _env_first("LLM_API_KEY", "DEEPSEEK_API_KEY", default=""))
    llm_base_url: str = Field(
        default_factory=lambda: _env_first("LLM_BASE_URL", "DEEPSEEK_BASE_URL", default="https://api.deepseek.com")
    )
    llm_model: str = Field(default_factory=lambda: _env_first("LLM_MODEL", "DEEPSEEK_FAST_MODEL", default="deepseek-chat"))
    llm_fast_model: str = Field(default_factory=lambda: _env_first("LLM_FAST_MODEL", "LLM_MODEL", "DEEPSEEK_FAST_MODEL", default="deepseek-chat"))
    llm_reason_model: str = Field(
        default_factory=lambda: _env_first("LLM_REASON_MODEL", "LLM_MODEL", "DEEPSEEK_REASON_MODEL", default="deepseek-reasoner")
    )
    llm_timeout_seconds: int = Field(default_factory=lambda: int(os.getenv("LLM_TIMEOUT_SECONDS", "45")))
    llm_chat_timeout_seconds: int = Field(default_factory=lambda: int(os.getenv("LLM_CHAT_TIMEOUT_SECONDS", "20")))
    llm_stream_first_token_timeout_seconds: int = Field(
        default_factory=lambda: int(os.getenv("LLM_STREAM_FIRST_TOKEN_TIMEOUT_SECONDS", "15"))
    )
    llm_stream_total_timeout_seconds: int = Field(default_factory=lambda: int(os.getenv("LLM_STREAM_TOTAL_TIMEOUT_SECONDS", "45")))
    llm_stream_enabled: bool = Field(default_factory=lambda: _env_bool("LLM_STREAM_ENABLED", True))
    llm_mock_enabled: bool = Field(default_factory=lambda: _env_bool("LLM_MOCK_ENABLED", True))
    llm_fallback_to_mock: bool = Field(default_factory=lambda: _env_bool("LLM_FALLBACK_TO_MOCK", True))
    llm_fast_thinking_enabled: bool = Field(default_factory=lambda: _env_bool("LLM_FAST_THINKING_ENABLED", False))
    llm_reason_thinking_enabled: bool = Field(default_factory=lambda: _env_bool("LLM_REASON_THINKING_ENABLED", False))
    llm_reasoning_effort: str = Field(default_factory=lambda: os.getenv("LLM_REASONING_EFFORT", "low"))
    llm_fast_max_tokens: int = Field(default_factory=lambda: int(os.getenv("LLM_FAST_MAX_TOKENS", "512")))
    llm_hint_max_tokens: int = Field(default_factory=lambda: int(os.getenv("LLM_HINT_MAX_TOKENS", "1200")))
    llm_scoring_json_response: bool = Field(default_factory=lambda: _env_bool("LLM_SCORING_JSON_RESPONSE", True))
    llm_scoring_max_tokens: int = Field(default_factory=lambda: int(os.getenv("LLM_SCORING_MAX_TOKENS", "4096")))

    report_storage_dir: str = Field(default_factory=lambda: os.getenv("REPORT_STORAGE_DIR", "./storage/reports"))
    runtime_memory_ttl_seconds: int = Field(default_factory=lambda: int(os.getenv("RUNTIME_MEMORY_TTL_SECONDS", "7200")))
    runtime_memory_backend: str = Field(default_factory=lambda: os.getenv("RUNTIME_MEMORY_BACKEND", "memory"))
    runtime_memory_fallback_enabled: bool = Field(
        default_factory=lambda: _env_bool("RUNTIME_MEMORY_FALLBACK_ENABLED", True)
    )
    redis_url: str = Field(default_factory=lambda: os.getenv("REDIS_URL", "redis://redis:6379/0"))
    auth_validate_enabled: bool = Field(default_factory=lambda: _env_bool("AUTH_VALIDATE_ENABLED", True))
    auth_user_me_url: str = Field(default_factory=lambda: os.getenv("AUTH_USER_ME_URL", ""))
    auth_timeout_seconds: int = Field(default_factory=lambda: int(os.getenv("AUTH_TIMEOUT_SECONDS", "5")))
    auth_cache_ttl_seconds: int = Field(default_factory=lambda: int(os.getenv("AUTH_CACHE_TTL_SECONDS", "300")))

    @property
    def is_production(self) -> bool:
        """环境判断：标识当前是否运行在生产环境。"""
        return self.app_env.lower() == "production"

    def deployment_config_errors(self) -> list[str]:
        """部署检查：返回会导致生产核心链路不可用的配置问题。"""
        errors: list[str] = []
        if self.database_url.startswith("sqlite"):
            errors.append("DATABASE_URL must use MySQL")
        if "CHANGE_ME" in self.database_url:
            errors.append("DATABASE_URL still contains a placeholder")
        if self.runtime_memory_backend.lower() != "redis":
            errors.append("RUNTIME_MEMORY_BACKEND must be redis")
        if not self.redis_url:
            errors.append("REDIS_URL is required")
        if not self.auth_user_me_url:
            errors.append("AUTH_USER_ME_URL is required")
        if self.llm_api_key in {"", "CHANGE_ME"} and not self.llm_mock_enabled:
            errors.append("LLM_API_KEY is required when mock mode is disabled")
        return errors

    def as_public_dict(self) -> dict[str, Any]:
        """配置展示：返回允许暴露给 Demo 前端的功能开关。"""
        mock_enabled = self.llm_mock_enabled or not self.llm_api_key
        return {
            "stream_chat": self.llm_stream_enabled,
            "score_types": ["percentage", "five_point"],
            "pdf_export": True,
            "knowledge_search": True,
            "llm_mock_enabled": mock_enabled,
            "llm_mode": "mock" if mock_enabled else "real",
            "llm_fallback_to_mock": self.llm_fallback_to_mock,
            "llm_fast_model": self.llm_fast_model,
            "llm_reason_model": self.llm_reason_model,
            "llm_fast_thinking_enabled": self.llm_fast_thinking_enabled,
            "llm_reason_thinking_enabled": self.llm_reason_thinking_enabled,
            "llm_reasoning_effort": self.llm_reasoning_effort,
            "llm_fast_max_tokens": self.llm_fast_max_tokens,
            "runtime_memory_backend": self.runtime_memory_backend,
            "auth_validate_enabled": True,
            "auth_source": "django_user_center",
        }


settings = Settings()