from enum import Enum
from typing import List, Optional

from pydantic import BaseModel


class ModelType(str, Enum):
    """模型类型枚举"""

    HOWCUTEAMI = "howcuteami"
    DEEPFACE = "deepface"
    HYBRID = "hybrid"  # 混合模式：颜值性别用howcuteami，年龄情绪用deepface


class ImageScoreItem(BaseModel):
    file_path: str
    score: float
    is_cropped_face: bool = False
    size_bytes: int
    size_str: str
    last_modified: str
    nickname: Optional[str] = None
    hostname: Optional[str] = None
    region: Optional[str] = None
    region_country: Optional[str] = None


class SearchRequest(BaseModel):
    keyword: Optional[str] = ""
    searchType: Optional[str] = "face"
    top_k: Optional[int] = 5
    score_threshold: float = 0.0
    nickname: Optional[str] = None
    region: Optional[str] = None
    platform: Optional[str] = None


class ImageSearchRequest(BaseModel):
    image: Optional[str] = None  # base64编码的图片
    searchType: Optional[str] = "face"
    top_k: Optional[int] = 5
    score_threshold: float = 0.0
    nickname: Optional[str] = None


class ImageFileList(BaseModel):
    results: List[ImageScoreItem]
    count: int

class PagedImageFileList(BaseModel):
    results: List[ImageScoreItem]
    count: int
    page: int
    page_size: int
    total_pages: int

class CelebrityMatchResponse(BaseModel):
    filename: str
    display_name: Optional[str] = None
    distance: float
    similarity: float
    confidence: float
    face_filename: Optional[str] = None


class CategoryStatItem(BaseModel):
  category: str
  display_name: str
  count: int


class CategoryStatsResponse(BaseModel):
  stats: List[CategoryStatItem]
  total: int


class RegionStatItem(BaseModel):
    region: str
    region_country: str
    count: int


class RegionStatsResponse(BaseModel):
    stats: List[RegionStatItem]
    total: int


class UserDeviceItem(BaseModel):
    device_id: str
    device_type: Optional[str] = None
    device_model: Optional[str] = None
    os_version: Optional[str] = None
    app_version: Optional[str] = None
    timezone: Optional[str] = None
    region: Optional[str] = None
    region_country: Optional[str] = None
    language: Optional[str] = None
    detection_count: int = 0
    created_at: str
    updated_at: str


class UserDeviceListResponse(BaseModel):
    results: List[UserDeviceItem]
    count: int
    page: int
    page_size: int
    total_pages: int


class RankResponse(BaseModel):
    success: bool = True
    score: float
    region_code: Optional[str] = None
    region_name: Optional[str] = None
    region_rank: int
    region_total: int
    region_percentile: float
    global_rank: int
    global_total: int
    global_percentile: float
    days_limit: int = 90


class LeaderboardItem(BaseModel):
    record_id: int
    file_path: str = ""
    rank: int
    score: float
    nickname: Optional[str] = None
    display_name: str = ""
    region_code: Optional[str] = None
    region_name: Optional[str] = None
    gender: Optional[str] = None
    age: Optional[str] = None
    avatar_id: int
    created_at: Optional[str] = None
    updated_at: Optional[str] = None
    is_current_user: bool = False


class LeaderboardResponse(BaseModel):
    success: bool = True
    region_code: Optional[str] = None
    region_name: Optional[str] = None
    total_records: int
    items: List[LeaderboardItem]
    days_limit: int = 90