159 lines
7.9 KiB
Python
Executable File
159 lines
7.9 KiB
Python
Executable File
# /opt/docker/dev/service_finder/backend/app/models/service.py
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import uuid
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from datetime import datetime
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from typing import Any, List, Optional
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from sqlalchemy import Integer, String, Boolean, DateTime, ForeignKey, text, Text, Float, Index, Numeric, BigInteger
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from sqlalchemy.orm import Mapped, mapped_column, relationship
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from sqlalchemy.dialects.postgresql import UUID as PG_UUID, JSONB
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from geoalchemy2 import Geometry
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from sqlalchemy.sql import func
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# MB 2.0: Központi aszinkron adatbázis motorból húzzuk be a Base-t
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from app.database import Base
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class ServiceProfile(Base):
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""" Szerviz szolgáltató adatai (v1.3.1). """
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__tablename__ = "service_profiles"
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__table_args__ = (
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Index('idx_service_fingerprint', 'fingerprint', unique=True),
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{"schema": "data"}
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)
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id: Mapped[int] = mapped_column(Integer, primary_key=True, index=True)
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organization_id: Mapped[Optional[int]] = mapped_column(Integer, ForeignKey("data.organizations.id"), unique=True)
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parent_id: Mapped[Optional[int]] = mapped_column(Integer, ForeignKey("data.service_profiles.id"))
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fingerprint: Mapped[str] = mapped_column(String(255), index=True, nullable=False)
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location: Mapped[Any] = mapped_column(Geometry(geometry_type='POINT', srid=4326, spatial_index=False), index=True)
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status: Mapped[str] = mapped_column(String(20), server_default=text("'ghost'"), index=True)
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last_audit_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), server_default=func.now())
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google_place_id: Mapped[Optional[str]] = mapped_column(String(100), unique=True)
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rating: Mapped[Optional[float]] = mapped_column(Float)
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user_ratings_total: Mapped[Optional[int]] = mapped_column(Integer)
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vibe_analysis: Mapped[Any] = mapped_column(JSONB, server_default=text("'{}'::jsonb"))
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social_links: Mapped[Any] = mapped_column(JSONB, server_default=text("'{}'::jsonb"))
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specialization_tags: Mapped[Any] = mapped_column(JSONB, server_default=text("'{}'::jsonb"))
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trust_score: Mapped[int] = mapped_column(Integer, default=30)
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is_verified: Mapped[bool] = mapped_column(Boolean, default=False)
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verification_log: Mapped[Any] = mapped_column(JSONB, server_default=text("'{}'::jsonb"))
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opening_hours: Mapped[Any] = mapped_column(JSONB, server_default=text("'{}'::jsonb"))
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contact_phone: Mapped[Optional[str]] = mapped_column(String)
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contact_email: Mapped[Optional[str]] = mapped_column(String)
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website: Mapped[Optional[str]] = mapped_column(String)
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bio: Mapped[Optional[str]] = mapped_column(Text)
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# Kapcsolatok
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organization: Mapped["Organization"] = relationship("Organization", back_populates="service_profile")
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expertises: Mapped[List["ServiceExpertise"]] = relationship("ServiceExpertise", back_populates="service")
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created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), server_default=func.now())
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updated_at: Mapped[Optional[datetime]] = mapped_column(DateTime(timezone=True), onupdate=func.now())
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class ExpertiseTag(Base):
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"""
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Szakmai címkék mesterlistája (MB 2.0).
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Ez a tábla vezérli a robotok keresését és a Gamification pontozást is.
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"""
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__tablename__ = "expertise_tags"
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__table_args__ = {"schema": "data"}
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id: Mapped[int] = mapped_column(Integer, primary_key=True)
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# Egyedi azonosító kulcs (pl. 'ENGINE_REBUILD')
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key: Mapped[str] = mapped_column(String(50), unique=True, index=True)
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# Megjelenítendő nevek
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name_hu: Mapped[Optional[str]] = mapped_column(String(100))
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name_en: Mapped[Optional[str]] = mapped_column(String(100))
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# Főcsoport (pl. 'MECHANICS', 'ELECTRICAL', 'EMERGENCY')
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category: Mapped[Optional[str]] = mapped_column(String(30), index=True)
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# --- 🎮 GAMIFICATION ÉS DISCOVERY ---
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# Hivatalos címke (True) vagy júzer/robot által javasolt (False)
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is_official: Mapped[bool] = mapped_column(Boolean, default=True, server_default=text("true"))
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# Ha júzer javasolta, itt tároljuk, ki volt az (XP jóváíráshoz)
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suggested_by_id: Mapped[Optional[int]] = mapped_column(BigInteger, ForeignKey("identity.persons.id"))
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# ÁLLÍTHATÓ PONTÉRTÉK: Az adatbázisból jön, így bármikor módosítható.
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# Ritka szakmáknál magasabb, gyakoriaknál alacsonyabb érték állítható be.
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discovery_points: Mapped[int] = mapped_column(Integer, default=10, server_default=text("10"))
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# Robot kulcsszavak (JSONB): ["fék", "betét", "tárcsa", "fékfolyadék"]
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# A Scout robot ez alapján azonosítja be a szervizt a weboldala alapján.
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search_keywords: Mapped[Any] = mapped_column(JSONB, server_default=text("'[]'::jsonb"))
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# Népszerűségi mutató (hányszor lett felhasználva a rendszerben)
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usage_count: Mapped[int] = mapped_column(Integer, default=0, server_default=text("0"))
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# UI ikon azonosító (pl. 'wrench', 'tire-flat', 'car-electric')
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icon: Mapped[Optional[str]] = mapped_column(String(50))
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# Leírás a szakmáról (Adminisztratív célokra)
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description: Mapped[Optional[str]] = mapped_column(Text)
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# Időbélyegek
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created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), server_default=func.now())
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updated_at: Mapped[Optional[datetime]] = mapped_column(DateTime(timezone=True), onupdate=func.now())
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# --- KAPCSOLATOK ---
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services: Mapped[List["ServiceExpertise"]] = relationship("ServiceExpertise", back_populates="tag")
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# Visszamutatás a beküldőre (ha van)
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suggested_by: Mapped[Optional["Person"]] = relationship("Person")
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class ServiceExpertise(Base):
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"""
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KAPCSOLÓTÁBLA: Ez köti össze a szervizt a szakmáival.
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Itt tároljuk, hogy az adott szerviznél mennyire validált egy szakma.
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"""
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__tablename__ = "service_expertises"
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__table_args__ = {"schema": "data"}
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id: Mapped[int] = mapped_column(Integer, primary_key=True)
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service_id: Mapped[int] = mapped_column(Integer, ForeignKey("data.service_profiles.id", ondelete="CASCADE"))
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expertise_id: Mapped[int] = mapped_column(Integer, ForeignKey("data.expertise_tags.id", ondelete="CASCADE"))
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# Mennyire biztos ez a tudás? (0: robot találta, 1: júzer mondta, 2: igazolt szakma)
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confidence_level: Mapped[int] = mapped_column(Integer, default=0, server_default=text("0"))
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created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), server_default=text("now()"))
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# Kapcsolatok visszafelé
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service = relationship("ServiceProfile", back_populates="expertises")
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tag = relationship("ExpertiseTag", back_populates="services")
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class ServiceStaging(Base):
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""" Hunter (robot) adatok tárolója. """
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__tablename__ = "service_staging"
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__table_args__ = (
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Index('idx_staging_fingerprint', 'fingerprint', unique=True),
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{"schema": "data"}
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)
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id: Mapped[int] = mapped_column(Integer, primary_key=True, index=True)
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name: Mapped[str] = mapped_column(String, index=True, nullable=False)
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postal_code: Mapped[Optional[str]] = mapped_column(String(10), index=True)
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city: Mapped[Optional[str]] = mapped_column(String(100), index=True)
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full_address: Mapped[Optional[str]] = mapped_column(String)
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fingerprint: Mapped[str] = mapped_column(String(255), nullable=False)
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raw_data: Mapped[Any] = mapped_column(JSONB, server_default=text("'{}'::jsonb"))
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status: Mapped[str] = mapped_column(String(20), server_default=text("'pending'"), index=True)
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created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), server_default=func.now())
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class DiscoveryParameter(Base):
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""" Robot vezérlési paraméterek adminból. """
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__tablename__ = "discovery_parameters"
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__table_args__ = {"schema": "data"}
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id: Mapped[int] = mapped_column(Integer, primary_key=True)
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city: Mapped[str] = mapped_column(String(100))
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keyword: Mapped[str] = mapped_column(String(100))
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is_active: Mapped[bool] = mapped_column(Boolean, default=True)
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last_run_at: Mapped[Optional[datetime]] = mapped_column(DateTime(timezone=True)) |