"""Shared data models for the detection pipeline."""

from __future__ import annotations

from enum import Enum
from typing import Any
from pydantic import BaseModel, Field


class Verdict(str, Enum):
    REAL = "REAL"
    AI_GENERATED = "AI_GENERATED"
    MANIPULATED_DEEPFAKE = "MANIPULATED_DEEPFAKE"   # face-swap / reenactment (V1 model)
    UNCERTAIN = "UNCERTAIN"


class DetectionResult(BaseModel):
    """Output of a single detector."""

    detector: str                        # e.g. "hive", "claude_vision"
    p_fake: float                        # P(not real) — combines AI_GENERATED + MANIPULATED_DEEPFAKE
    verdict: Verdict
    confidence: float                    # how confident the detector is (0–1)
    evidence: list[str] = Field(default_factory=list)   # human-readable signals
    raw: dict[str, Any] = Field(default_factory=dict)   # full API response
    error: str | None = None             # set if the detector failed
    # Feature reuse fields
    generator: str | None = None         # detected generator name (e.g., "Stable Diffusion") from Sightengine
    # V1 model extended fields (None for API-based detectors)
    p_ai_generated: float | None = None          # P(AI_GENERATED) from 3-class model
    p_manipulated_deepfake: float | None = None  # P(MANIPULATED_DEEPFAKE) from 3-class model
    face_detected: bool | None = None            # whether a face was found in the image


class EnsembleResult(BaseModel):
    """Final fused result across all detectors."""

    verdict: Verdict
    p_fake: float                        # ensemble P(fake)
    confidence: float
    primary_evidence: str
    supporting_evidence: list[str] = Field(default_factory=list)
    uncertainty_factors: list[str] = Field(default_factory=list)
    detector_results: list[DetectionResult] = Field(default_factory=list)
    fusion_details: dict[str, Any] = Field(default_factory=dict)
    processing_time_ms: float = 0.0
    model_version: str = "latest"        # which hybrid model version was used
    diffusion_suspicion: float = 0.0     # 0–1 score from local_forensics diffusion scorer
    generator: str | None = None         # detected generator name (e.g., "Stable Diffusion"), if any detector identified it