155 lines
6.0 KiB
Python
155 lines
6.0 KiB
Python
"""ORM 모델 — 기능정의서 데이터모델 + lib/types.ts 와 정합.
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테이블:
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- matches 경기 (팀/시각/투표윈도우/상태/결과)
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- ai_predictions GPT/Claude/Gemini 예측 (경기 × 모델)
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- crowd_stats 군중 투표 분포 (경기당 1행, 원자적 증분)
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- user_predictions 유저 픽 (이메일 식별, 채점/알림 플래그 포함)
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"""
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from __future__ import annotations
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from datetime import datetime
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from sqlalchemy import (
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Boolean,
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DateTime,
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ForeignKey,
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Integer,
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String,
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UniqueConstraint,
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func,
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)
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from sqlalchemy.orm import Mapped, mapped_column, relationship
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from .database import Base
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# Outcome: TEAM_A_WIN | DRAW | TEAM_B_WIN
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# ModelName: GPT | Claude | Gemini
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# MatchStatus: scheduled | open | locked | live | finished
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class Match(Base):
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__tablename__ = "matches"
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match_id: Mapped[str] = mapped_column(String, primary_key=True)
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round_label: Mapped[str] = mapped_column(String, default="")
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group: Mapped[str] = mapped_column(String, default="A")
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# 팀 정보 (표시명/약식/코드/이모지) — 분리 컬럼으로 저장
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team_a_name: Mapped[str] = mapped_column(String)
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team_a_short: Mapped[str] = mapped_column(String)
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team_a_code: Mapped[str] = mapped_column(String)
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team_a_flag: Mapped[str] = mapped_column(String, default="")
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team_b_name: Mapped[str] = mapped_column(String)
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team_b_short: Mapped[str] = mapped_column(String)
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team_b_code: Mapped[str] = mapped_column(String)
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team_b_flag: Mapped[str] = mapped_column(String, default="")
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venue: Mapped[str] = mapped_column(String, default="")
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hook_text: Mapped[str] = mapped_column(String, default="")
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# 모든 시각은 timezone-aware (UTC 저장, KST 환산은 표현 계층)
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kickoff_at: Mapped[datetime] = mapped_column(DateTime(timezone=True))
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opens_at: Mapped[datetime] = mapped_column(DateTime(timezone=True))
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lock_at: Mapped[datetime] = mapped_column(DateTime(timezone=True))
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status: Mapped[str] = mapped_column(String, default="scheduled")
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# 결과 (입력 전 None)
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result_score_a: Mapped[int | None] = mapped_column(Integer, nullable=True)
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result_score_b: Mapped[int | None] = mapped_column(Integer, nullable=True)
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result_outcome: Mapped[str | None] = mapped_column(String, nullable=True)
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finished_at: Mapped[datetime | None] = mapped_column(
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DateTime(timezone=True), nullable=True
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)
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results_emailed_at: Mapped[datetime | None] = mapped_column(
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DateTime(timezone=True), nullable=True
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)
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created_at: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), server_default=func.now()
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)
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updated_at: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
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)
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predictions: Mapped[list["AIPrediction"]] = relationship(
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back_populates="match", cascade="all, delete-orphan"
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)
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crowd: Mapped["CrowdStats | None"] = relationship(
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back_populates="match", uselist=False, cascade="all, delete-orphan"
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)
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class AIPrediction(Base):
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__tablename__ = "ai_predictions"
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__table_args__ = (UniqueConstraint("match_id", "model", name="uq_match_model"),)
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id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
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match_id: Mapped[str] = mapped_column(
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ForeignKey("matches.match_id", ondelete="CASCADE")
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)
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model: Mapped[str] = mapped_column(String) # GPT | Claude | Gemini
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outcome: Mapped[str] = mapped_column(String)
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score_a: Mapped[int] = mapped_column(Integer)
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score_b: Mapped[int] = mapped_column(Integer)
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confidence_pct: Mapped[int] = mapped_column(Integer)
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reason_ko: Mapped[str] = mapped_column(String, default="")
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reason_en: Mapped[str] = mapped_column(String, default="")
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generated_at: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), server_default=func.now()
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)
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# 실연동 LLM 출력인지 시드 데이터인지 구분 (재생성 제어용)
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source: Mapped[str] = mapped_column(String, default="seed") # seed | llm
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match: Mapped["Match"] = relationship(back_populates="predictions")
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class CrowdStats(Base):
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__tablename__ = "crowd_stats"
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match_id: Mapped[str] = mapped_column(
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ForeignKey("matches.match_id", ondelete="CASCADE"), primary_key=True
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)
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total: Mapped[int] = mapped_column(Integer, default=0)
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team_a_win: Mapped[int] = mapped_column(Integer, default=0)
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draw: Mapped[int] = mapped_column(Integer, default=0)
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team_b_win: Mapped[int] = mapped_column(Integer, default=0)
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updated_at: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
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)
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match: Mapped["Match"] = relationship(back_populates="crowd")
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class UserPrediction(Base):
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__tablename__ = "user_predictions"
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__table_args__ = (
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UniqueConstraint("match_id", "device_id", name="uq_match_device"),
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)
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id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
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match_id: Mapped[str] = mapped_column(
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ForeignKey("matches.match_id", ondelete="CASCADE")
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)
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device_id: Mapped[str] = mapped_column(String) # 비로그인 식별 (클라 생성 uuid)
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outcome: Mapped[str] = mapped_column(String)
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score_a: Mapped[int] = mapped_column(Integer)
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score_b: Mapped[int] = mapped_column(Integer)
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email: Mapped[str | None] = mapped_column(String, nullable=True)
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notify: Mapped[bool] = mapped_column(Boolean, default=False)
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# 채점 (결과 입력 후 갱신)
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points: Mapped[int | None] = mapped_column(Integer, nullable=True)
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scored_at: Mapped[datetime | None] = mapped_column(
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DateTime(timezone=True), nullable=True
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)
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notified: Mapped[bool] = mapped_column(Boolean, default=False)
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created_at: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), server_default=func.now()
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)
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updated_at: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
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)
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