# scheme/statistics_scheme.py
from pydantic import BaseModel, Field, EmailStr
from datetime import datetime, date
from typing import Optional, List


# ---------- 请求模型 ----------
class StatsScoreRequest(BaseModel):
    choose: int = Field(..., ge=0, le=3)

    email: EmailStr
    full_name: Optional[str] = Field(None, max_length=100)

# ---------- 响应模型 ----------
# 多维度班级统计
class StatsResponse(BaseModel):
    cls_id: str
    total_cnt: int
    man_cnt: int
    female_cnt: int

class ExamScoreItem(BaseModel):
    exam_attempt: int
    exam_score: float

# 查询每次考试成绩都在输入分数线（如80分）以上的学生的编号、姓名和成绩。
class StuInfoAllExamAboveScore(BaseModel):
    stu_id: str
    stu_name: str
    stu_score: List[ExamScoreItem]

# 查询有输入指定次数（如两次）以上不及格的学生的姓名、班级和不及格成绩明细。
class FailingStudentItem(BaseModel):
    stu_name: str
    stu_cls_id: str
    stu_fail_cnt: int
    stu_score: List[ExamScoreItem]

# 查询有输入指定次数（如两次）以上不及格的学生的姓名、班级和不及格成绩明细。
class ClassExamAvgScoreItem(BaseModel):
    exam_attempt: int
    cls_id: str
    avg_score: float
    stu_cnt: int

class TopSalaryStudentItem(BaseModel):
    stu_name: str
    cls_id: str
    offer_time: date
    emp_company_name: str
    salary: float

class StudentEmpDurationItem(BaseModel):
    stu_id: str
    stu_name: str
    cls_id: str
    offer_time: int

class ClassAvgEmpDurationItem(BaseModel):
    cls_id: str
    avg_duration_days: float
    employed_cnt: int

class StudentAgeDetailItem(BaseModel):
    stu_id: str
    stu_name: str
    cls_id: str
    gender: str
    age: int