"""
Observation Models - Layer 6 of IOM
Pydantic models for observations, findings, and media
"""

from datetime import datetime
from typing import List, Optional, Dict, Any
from enum import Enum
from pydantic import BaseModel, Field


class MediaType(str, Enum):
    """Types of media attachments"""
    IMAGE = "image"
    VIDEO = "video"
    DEPTH_MAP = "depth_map"
    THERMAL = "thermal"
    LIDAR = "lidar"


class FindingType(str, Enum):
    """Types of findings/observations"""
    DIRT_ACCUMULATION = "dirt_accumulation"
    COLOR_CHANGE = "color_change"
    SURFACE_DAMAGE = "surface_damage"
    GRAFFITI = "graffiti"
    CRACK = "crack"
    DEFORMATION = "deformation"
    MATERIAL_LOSS = "material_loss"
    MISSING_PART = "missing_part"
    BROKEN_PART = "broken_part"
    LOOSE_PART = "loose_part"
    PHYSICAL_BLOCK = "physical_block"
    VEGETATION = "vegetation"
    WATER_DAMAGE = "water_damage"
    ICE_DAMAGE = "ice_damage"
    CORROSION = "corrosion"
    OTHER = "other"


class GeoTag(BaseModel):
    """Geographic location tag"""
    lat: float = Field(..., ge=-90, le=90, description="Latitude")
    lng: float = Field(..., ge=-180, le=180, description="Longitude")
    elevation: Optional[float] = Field(None, description="Elevation in meters")
    accuracy: Optional[float] = Field(None, description="GPS accuracy in meters")
    
    class Config:
        json_schema_extra = {
            "example": {
                "lat": 59.3293,
                "lng": 18.0686,
                "elevation": 12.5,
                "accuracy": 3.2
            }
        }


class Media(BaseModel):
    """Media attachment (image, video, etc.)"""
    type: MediaType
    url: str = Field(..., description="URL to media file")
    timestamp: datetime = Field(default_factory=datetime.utcnow)
    geotag: Optional[GeoTag] = None
    
    class Config:
        json_schema_extra = {
            "example": {
                "type": "image",
                "url": "https://storage.quixzoom.com/obs/img_2847.jpg",
                "timestamp": "2026-06-26T09:15:03Z",
                "geotag": {
                    "lat": 59.3293,
                    "lng": 18.0686,
                    "accuracy": 2.1
                }
            }
        }


class Finding(BaseModel):
    """Individual finding within an observation"""
    type: FindingType
    code: str = Field(..., pattern=r"^[0-9]{4}$", description="Defect code from Layer 7")
    description: str = Field(..., min_length=1, max_length=500)
    measurement: Optional[str] = Field(None, description="Measurement (e.g., '3 mm', '15% coverage')")
    location: Optional[str] = Field(None, description="Location on object (e.g., 'northeast_corner')")
    confidence: float = Field(..., ge=0.0, le=1.0, description="AI confidence score")
    
    class Config:
        json_schema_extra = {
            "example": {
                "type": "dirt_accumulation",
                "code": "2100",
                "description": "Smuts på fönster",
                "measurement": "30% coverage",
                "confidence": 0.94
            }
        }


class AIAnalysis(BaseModel):
    """AI-generated analysis of observation"""
    model: str = Field(..., description="AI model version")
    overall_condition: int = Field(..., ge=1, le=5, description="Overall condition 1-5")
    recommended_action: Optional[str] = Field(None, description="Recommended action")
    next_observation_due: Optional[datetime] = None
    
    class Config:
        json_schema_extra = {
            "example": {
                "model": "infrastructure-v3.2",
                "overall_condition": 3,
                "recommended_action": "schedule_cleaning",
                "next_observation_due": "2026-09-26"
            }
        }


class Weather(BaseModel):
    """Weather conditions during observation"""
    temperature: Optional[float] = Field(None, description="Temperature in Celsius")
    conditions: Optional[str] = Field(None, description="Weather conditions")
    wind_speed: Optional[float] = Field(None, description="Wind speed in m/s")
    precipitation: Optional[str] = Field(None, description="Precipitation type")
    
    class Config:
        json_schema_extra = {
            "example": {
                "temperature": 22,
                "conditions": "sunny",
                "wind_speed": 3.5
            }
        }


class Observation(BaseModel):
    """
    Main observation model - Layer 6 of IOM
    
    Core principle: The object never changes. Only observations change.
    """
    id: Optional[str] = Field(None, pattern=r"^OBS-[0-9]{4}-[0-9]{7}$")
    timestamp: datetime = Field(default_factory=datetime.utcnow)
    object_goid: str = Field(..., pattern=r"^[A-Z]{3}(-[A-Z]{3})+-[0-9]+$", description="GOID of observed object")
    observer: str = Field(..., description="Observer ID (zoomer:id or sensor:id)")
    
    # Findings
    findings: List[Finding] = Field(default_factory=list)
    
    # Media
    media: List[Media] = Field(default_factory=list)
    
    # Context
    weather: Optional[Weather] = None
    
    # AI Analysis
    ai_analysis: Optional[AIAnalysis] = None
    
    # Metadata
    created_at: datetime = Field(default_factory=datetime.utcnow)
    updated_at: Optional[datetime] = None
    
    class Config:
        json_schema_extra = {
            "example": {
                "id": "OBS-2026-0012847",
                "timestamp": "2026-06-26T09:15:00Z",
                "object_goid": "BYG-FAC-WIN-GLA-2847",
                "observer": "zoomer:anna_k",
                "findings": [
                    {
                        "type": "dirt_accumulation",
                        "code": "2100",
                        "description": "Smuts på fönster",
                        "measurement": "30% coverage",
                        "confidence": 0.94
                    }
                ],
                "media": [
                    {
                        "type": "image",
                        "url": "https://storage.quixzoom.com/obs/img_2847.jpg",
                        "timestamp": "2026-06-26T09:15:03Z",
                        "geotag": {
                            "lat": 59.3293,
                            "lng": 18.0686,
                            "accuracy": 2.1
                        }
                    }
                ],
                "ai_analysis": {
                    "model": "infrastructure-v3.2",
                    "overall_condition": 3,
                    "recommended_action": "schedule_cleaning"
                }
            }
        }


class ObservationSummary(BaseModel):
    """Summary of observations for an object"""
    object_goid: str
    observation_count: int
    latest_condition: Optional[int] = None
    latest_observation_date: Optional[datetime] = None
    finding_types: List[str] = Field(default_factory=list)
    risk_trend: Optional[str] = None  # "improving", "stable", "degrading"
    
    class Config:
        json_schema_extra = {
            "example": {
                "object_goid": "BYG-FAC-WIN-GLA-2847",
                "observation_count": 5,
                "latest_condition": 3,
                "latest_observation_date": "2026-06-26T09:15:00Z",
                "finding_types": ["dirt_accumulation", "surface_damage"],
                "risk_trend": "stable"
            }
        }


class ObservationTrend(BaseModel):
    """Trend analysis for an object over time"""
    object_goid: str
    period_months: int
    data_points: List[Dict[str, Any]]
    
    class Config:
        json_schema_extra = {
            "example": {
                "object_goid": "BYG-FAC-WIN-GLA-2847",
                "period_months": 6,
                "data_points": [
                    {"month": "2026-01", "condition": 2, "observations": 1},
                    {"month": "2026-02", "condition": 2, "observations": 0},
                    {"month": "2026-03", "condition": 3, "observations": 1},
                    {"month": "2026-04", "condition": 3, "observations": 0},
                    {"month": "2026-05", "condition": 3, "observations": 1},
                    {"month": "2026-06", "condition": 3, "observations": 2}
                ]
            }
        }


if __name__ == '__main__':
    # Example usage
    obs = Observation(
        id="OBS-2026-0012847",
        timestamp=datetime(2026, 6, 26, 9, 15, 0),
        object_goid="BYG-FAC-WIN-GLA-2847",
        observer="zoomer:anna_k",
        findings=[
            Finding(
                type=FindingType.DIRT_ACCUMULATION,
                code="2100",
                description="Smuts på fönster",
                measurement="30% coverage",
                confidence=0.94
            )
        ],
        media=[
            Media(
                type=MediaType.IMAGE,
                url="https://storage.quixzoom.com/obs/img_2847.jpg",
                geotag=GeoTag(lat=59.3293, lng=18.0686, accuracy=2.1)
            )
        ],
        ai_analysis=AIAnalysis(
            model="infrastructure-v3.2",
            overall_condition=3,
            recommended_action="schedule_cleaning"
        )
    )
    
    print("Observation created:")
    print(obs.model_dump_json(indent=2))