from sqlalchemy import Column, String, Integer, Float, DateTime, Text, ForeignKey, JSON
from sqlalchemy.dialects.postgresql import UUID, ARRAY
from sqlalchemy.sql import func
from pgvector.sqlalchemy import Vector
import uuid

from app.core.database import Base


class PersonIdentity(Base):
    """
    Unique person identity — grouped by face embedding similarity.
    All face events linked to this identity let us trace where this person has been.
    """
    __tablename__ = "person_identities"

    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
    name = Column(String(255), nullable=True)                # e.g. "Juan Dela Cruz" if registered
    label = Column(String(100), nullable=True)               # e.g. "Unit 3A Resident", "Delivery Rider"
    notes = Column(Text, nullable=True)
    is_registered = Column(Integer, default=0)               # 1 = manually registered with name
    is_watchlisted = Column(Integer, default=0)              # 1 = flagged for alerts
    thumbnail_path = Column(Text, nullable=True)             # Best face shot

    # Face embedding (512-dim for InsightFace buffalo_l)
    embedding = Column(Vector(512), nullable=True)

    # Stats
    first_seen_at = Column(DateTime(timezone=True), nullable=True)
    last_seen_at = Column(DateTime(timezone=True), nullable=True)
    total_sightings = Column(Integer, default=0)
    created_at = Column(DateTime(timezone=True), server_default=func.now())

    def __repr__(self):
        return f"<PersonIdentity {self.name or 'Unknown'} ({self.total_sightings} sightings)>"


class FaceEvent(Base):
    """
    Every face detection event — one row per detected face per frame.
    """
    __tablename__ = "face_events"

    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
    camera_id = Column(UUID(as_uuid=True), ForeignKey("cameras.id"), nullable=False, index=True)
    identity_id = Column(UUID(as_uuid=True), ForeignKey("person_identities.id"), nullable=True, index=True)

    # Detection
    detection_confidence = Column(Float, nullable=True)
    face_bbox = Column(JSON, nullable=True)                  # {"x","y","w","h"}
    face_embedding = Column(Vector(512), nullable=True)      # Per-event embedding for re-clustering
    similarity_score = Column(Float, nullable=True)          # Match score to identity

    # Context — if detected alongside a vehicle event
    vehicle_event_id = Column(UUID(as_uuid=True), ForeignKey("vehicle_events.id"), nullable=True)

    # Storage
    snapshot_path = Column(Text, nullable=True)             # Full frame
    face_snapshot_path = Column(Text, nullable=True)        # Cropped face chip

    # Meta
    captured_at = Column(DateTime(timezone=True), server_default=func.now(), index=True)

    def __repr__(self):
        return f"<FaceEvent identity={self.identity_id} cam={self.camera_id} at={self.captured_at}>"