"""
Location Intelligence service — estimates where an image was captured.

Combines evidence from:
  - GPS (from EXIF metadata, via cores.metadata)
  - OCR text (language + keyword detection)
  - License plate format
  - Scene labels
  - Detected objects (vehicle presence = street/road context)

Returns a LocationEstimate with candidate countries, cities, and
conflicting evidence.  No heavy geolocation models — pure heuristics.
"""

from __future__ import annotations

import time
from typing import Optional

from cores.location import LocationEvidenceCollector, LocationEstimator
from cores.metadata import extract_all
from models.jobs import JobRequest
from models.reports import LocationEstimate
from pipeline import InputValidator, ImagePreprocessor, ImageHasher
from utils.logging import execution_context, new_execution_id


class LocationIntelligenceService:
    """Estimates image capture location from multiple evidence sources."""

    def __init__(
        self,
        validator: InputValidator,
        preprocessor: ImagePreprocessor,
        hasher: ImageHasher,
    ) -> None:
        self._validator = validator
        self._preprocessor = preprocessor
        self._hasher = hasher

    async def analyze(self, request: JobRequest) -> dict:
        """Estimate location from an image."""
        eid = new_execution_id()
        with execution_context(execution_id=eid, provider_id="location_intelligence_service"):
            t0 = time.perf_counter()

            vr = self._validator.validate(
                image_url=request.image_url,
                image_base64=request.image_base64,
            )
            if not vr.valid:
                return {"success": False, "error": vr.error, "error_type": "ValidationError"}

            if vr.source == "url":
                pre = self._preprocessor.from_url(request.image_url)
            else:
                pre = self._preprocessor.from_bytes(vr.image_bytes, vr.source)

            original_bytes = pre.original_bytes or vr.image_bytes

            collector = LocationEvidenceCollector()
            gps: Optional[dict] = None

            # 1. GPS from EXIF
            if original_bytes:
                meta = extract_all(original_bytes)
                if meta.get("gps_coords"):
                    gps = meta["gps_coords"]
                    collector.add_gps(gps["lat"], gps["lon"])

                # 2. OCR text — if the caller passed it in options
                # (We don't run OCR here; the caller can pass OCR results via options)
            ocr_text = request.options.get("ocr_text", "")
            if ocr_text:
                collector.add_ocr_text(ocr_text)

            # 3. License plate — if passed in options
            plate_text = request.options.get("plate_text", "")
            if plate_text:
                collector.add_plate(plate_text)

            # 4. Scene label — if passed in options
            scene_label = request.options.get("scene_label", "")
            if scene_label:
                collector.add_scene(scene_label)

            # 5. Detected objects — if passed in options
            objects = request.options.get("objects", [])
            if objects:
                collector.add_detected_objects(objects)

            # Estimate
            estimator = LocationEstimator()
            estimate = estimator.estimate(collector.evidence, gps=gps)

            elapsed = (time.perf_counter() - t0) * 1000.0
            estimate.elapsed_ms = round(elapsed, 3)

            return {
                "success": True,
                "location_estimate": estimate.model_dump(),
                "elapsed_ms": round(elapsed, 3),
            }