"""Phase 12: Risk Intelligence model — per-technique risk scoring."""

import uuid
from datetime import datetime

from sqlalchemy import (
    Boolean, Column, DateTime, Float, ForeignKey,
    Index, Integer, String, UniqueConstraint,
)
from sqlalchemy.dialects.postgresql import UUID, JSONB
from sqlalchemy.orm import relationship

from app.database import Base


class TechniqueRiskProfile(Base):
    """
    Aggregated risk profile for one technique.

    Combines four weighted factors:
      • detection_gap      (35 %) — 0=fully covered → 1=no coverage
      • threat_actor_rel   (30 %) — normalised actor count
      • osint_signals      (20 %) — normalised recent OSINT items (30 d)
      • test_failure_rate  (15 %) — proportion of tests where blue didn't detect

    risk_score = weighted sum × 100  → 0–100
    risk_level: critical ≥75 | high ≥50 | medium ≥25 | low ≥10 | info
    """

    __tablename__ = "technique_risk_profiles"

    id            = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
    technique_id  = Column(
        UUID(as_uuid=True),
        ForeignKey("techniques.id", ondelete="CASCADE"),
        nullable=False,
    )

    # ── Computed scores ───────────────────────────────────────────────────────
    risk_score    = Column(Float, nullable=False, default=0.0)   # 0–100
    likelihood    = Column(Float, nullable=False, default=0.0)   # 0–100
    impact        = Column(Float, nullable=False, default=0.0)   # 0–100
    risk_level    = Column(String(16), nullable=False, default="info")

    # ── Raw factor values ─────────────────────────────────────────────────────
    detection_gap       = Column(Float, nullable=False, default=1.0)  # 0–1
    threat_actor_count  = Column(Integer, nullable=False, default=0)
    osint_signal_count  = Column(Integer, nullable=False, default=0)  # last 30 d
    test_fail_count     = Column(Integer, nullable=False, default=0)
    test_total_count    = Column(Integer, nullable=False, default=0)
    test_failure_rate   = Column(Float, nullable=False, default=0.0)  # 0–1
    confidence_level    = Column(Float, nullable=False, default=0.0)  # DLC 0–1

    # ── Rich detail ──────────────────────────────────────────────────────────
    scoring_breakdown   = Column(JSONB, nullable=True)  # per-factor contributions
    recommendations     = Column(JSONB, nullable=True)  # list[str]

    # ── Meta ─────────────────────────────────────────────────────────────────
    computed_at   = Column(DateTime, default=datetime.utcnow)
    is_stale      = Column(Boolean, default=True)

    technique = relationship("Technique", foreign_keys=[technique_id])

    __table_args__ = (
        UniqueConstraint("technique_id", name="uq_risk_profile_technique"),
        Index("ix_risk_profiles_risk_score", "risk_score"),
        Index("ix_risk_profiles_risk_level", "risk_level"),
        Index("ix_risk_profiles_stale",      "is_stale"),
    )