# ============================================================
# 数据校验 / 序列化模型层（Pydantic Schema）
# 职责：
#   1. 定义接口请求参数和响应数据的结构，自动做类型校验
#   2. 作为 FastAPI 自动生成 Swagger 文档的依据
#   3. ResponseModel* 系列封装统一响应格式 { code, message, total, total_pages, data }
# ============================================================

from pydantic import BaseModel,ConfigDict  # FastAPI 推荐用 pydantic 做数据校验和序列化
from datetime import date               # 日期类型，用于 birthday、enrollment_date 等字段
from typing import List                 # 类型提示，声明列表

class StudentInfo1(BaseModel):
    model_config = ConfigDict(from_attributes=True)
    student_id: str             # 学号
    student_name: str           # 姓名
    gender: str                 # 性别（男/女）
    id_card: str                # 身份证号
    birthday: date              # 出生日期
    ethnicity: str              # 民族
    region_id: str              # 籍贯编码（关联 region表）
    phone: str                  # 手机号
    major: str                  # 专业
    class_id: str               # 班级编号
    enrollment_date: date       # 入学日期
    graduation_date: date       # 毕业日期
    student_status: str         # 学籍状态（在读/休学/退学/毕业）
    education_level: str        # 学历（大专/本科/硕士/博士）

class ResponseModel(BaseModel):
    """统一响应格式 — 所有接口都返回这个结构，方便前端统一处理"""
    code: int                  # 状态码，200=成功，400=参数错误
    message: str = 'ok'        # 提示信息
    total:int                  #总条数
    total_pages:int            #总页数
    data: List[StudentInfo1] # 真正的数据，这里是学员列表

class StudentInfo2(BaseModel):
    student_id: str
    student_name: str
    score:float

class ResponseModel1(BaseModel):
    """统一响应格式 — 所有接口都返回这个结构,方便前端统一处理"""
    code: int                  # 状态码,200=成功,400=参数错误
    message: str = 'ok'        # 提示信息
    total:int                  #总条数
    total_pages:int            #总页数
    data: List[StudentInfo2] # 真正的数据,这里是学员列表

class StudentInfo3(BaseModel):
    student_id: str
    student_name: str
    fail_count: int

class ResponseModel2(BaseModel):
    code: int
    message: str = 'ok'
    total: int
    total_pages: int
    data: List[StudentInfo3]

class ClassExamAvg(BaseModel):
    course_id: str
    class_id: str
    class_name: str
    avg_score: float

class ResponseModel3(BaseModel):
    code: int
    message: str = 'ok'
    total: int
    total_pages: int
    data: List[ClassExamAvg]

class SalaryTop(BaseModel):
    student_name: str
    class_name: str
    company_name: str
    part_time: date
    salary: float

class ResponseModel4(BaseModel):
    code: int
    message: str = 'ok'
    data: List[SalaryTop]

class TimeSize(BaseModel):
    student_id: str
    student_name: str
    offer_date: date
    resume_open_date: date
    time_size: int  # 就业时长（天数）

class ResponseModel5(BaseModel):
    code: int
    message: str = 'ok'
    total: int
    total_pages: int
    data: List[TimeSize]

class ClassAvgTimeSize(BaseModel):
    class_id: str
    class_name: str
    avg_time_size: float  # 平均就业时长（天数）

class ResponseModel6(BaseModel):
    code: int
    message: str = 'ok'
    total: int
    total_pages: int
    data: List[ClassAvgTimeSize]

class ClassStats(BaseModel):
    class_id: str
    class_name: str
    total_count: int
    male_count: int
    female_count: int

class ResponseModel7(BaseModel):
    code: int
    message: str = 'ok'
    data: ClassStats

class ResponseModel8(BaseModel):
    code: int
    message: str = 'ok'
    data: List[ClassStats]