129 lines
3.9 KiB
Python
129 lines
3.9 KiB
Python
# ============================================================
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# 数据校验 / 序列化模型层（Pydantic Schema）
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# 职责：
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# 1. 定义接口请求参数和响应数据的结构，自动做类型校验
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# 2. 作为 FastAPI 自动生成 Swagger 文档的依据
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# 3. ResponseModel* 系列封装统一响应格式 { code, message, total, total_pages, data }
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# ============================================================
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from pydantic import BaseModel,ConfigDict # FastAPI 推荐用 pydantic 做数据校验和序列化
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from datetime import date # 日期类型，用于 birthday、enrollment_date 等字段
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from typing import List # 类型提示，声明列表
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class StudentInfo1(BaseModel):
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model_config = ConfigDict(from_attributes=True)
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student_id: str # 学号
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student_name: str # 姓名
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gender: str # 性别（男/女）
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id_card: str # 身份证号
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birthday: date # 出生日期
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ethnicity: str # 民族
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region_id: str # 籍贯编码（关联 region表）
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phone: str # 手机号
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major: str # 专业
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class_id: str # 班级编号
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enrollment_date: date # 入学日期
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graduation_date: date # 毕业日期
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student_status: str # 学籍状态（在读/休学/退学/毕业）
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education_level: str # 学历（大专/本科/硕士/博士）
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class ResponseModel(BaseModel):
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"""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"""
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code: int # 状态码，200=成功，400=参数错误
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message: str = 'ok' # 提示信息
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total:int #总条数
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total_pages:int #总页数
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data: List[StudentInfo1] # 真正的数据，这里是学员列表
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class StudentInfo2(BaseModel):
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student_id: str
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student_name: str
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score:float
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class ResponseModel1(BaseModel):
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"""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"""
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code: int # 状态码,200=成功,400=参数错误
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message: str = 'ok' # 提示信息
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total:int #总条数
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total_pages:int #总页数
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data: List[StudentInfo2] # 真正的数据,这里是学员列表
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class StudentInfo3(BaseModel):
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student_id: str
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student_name: str
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fail_count: int
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class ResponseModel2(BaseModel):
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code: int
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message: str = 'ok'
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total: int
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total_pages: int
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data: List[StudentInfo3]
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class ClassExamAvg(BaseModel):
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course_id: str
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class_id: str
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class_name: str
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avg_score: float
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class ResponseModel3(BaseModel):
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code: int
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message: str = 'ok'
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total: int
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total_pages: int
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data: List[ClassExamAvg]
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class SalaryTop(BaseModel):
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student_name: str
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class_name: str
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company_name: str
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part_time: date
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salary: float
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class ResponseModel4(BaseModel):
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code: int
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message: str = 'ok'
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data: List[SalaryTop]
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class TimeSize(BaseModel):
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student_id: str
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student_name: str
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offer_date: date
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resume_open_date: date
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time_size: int # 就业时长（天数）
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class ResponseModel5(BaseModel):
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code: int
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message: str = 'ok'
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total: int
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total_pages: int
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data: List[TimeSize]
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class ClassAvgTimeSize(BaseModel):
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class_id: str
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class_name: str
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avg_time_size: float # 平均就业时长（天数）
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class ResponseModel6(BaseModel):
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code: int
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message: str = 'ok'
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total: int
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total_pages: int
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data: List[ClassAvgTimeSize]
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class ClassStats(BaseModel):
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class_id: str
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class_name: str
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total_count: int
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male_count: int
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female_count: int
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class ResponseModel7(BaseModel):
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code: int
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message: str = 'ok'
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data: ClassStats
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class ResponseModel8(BaseModel):
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code: int
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message: str = 'ok'
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data: List[ClassStats] |