"""Service layer for cache-aware fetching of FINN ads and Eiendom.no units."""

import logging
from typing import Any

from .ad import fetch_ad_details
from .analysis import analyze_search as run_analysis_search
from .cache import (
    get_eiendom_unit as get_cached_eiendom_unit,
    get_finn_ad,
    init_db,
    save_eiendom_unit,
    save_finn_ad,
)
from .config import FINN_CACHE_PATH
from .eiendom_no import (
    build_unit_vector,
    decode_unit_vector,
    get_similar_units,
    get_unit,
    search_unit_from_finn_url,
)
from .feedback import save_feedback as save_feedback_impl
from .models import EiendomUnit, FinnAd, SimilarUnit

logger = logging.getLogger(__name__)


async def get_or_fetch_ad(finnkode: str, force_refresh: bool = False) -> FinnAd:
    """Get FinnAd from cache or fetch fresh. Never returns None."""
    conn = init_db(FINN_CACHE_PATH)
    ad = None if force_refresh else get_finn_ad(conn, finnkode, ttl_hours=24)
    if ad is None:
        ad = await fetch_ad_details(finnkode)
        save_finn_ad(conn, ad)
    return ad


async def get_or_fetch_eiendom_unit(
    unit_code: str, force_refresh: bool = False
) -> EiendomUnit | None:
    """Get EiendomUnit from cache or fetch fresh."""
    conn = init_db(FINN_CACHE_PATH)
    unit = None if force_refresh else get_cached_eiendom_unit(conn, unit_code, ttl_hours=24)
    if unit is None:
        unit = await get_unit(unit_code)
        if unit is not None:
            save_eiendom_unit(conn, unit)
    return unit


async def get_or_fetch_similar_units(
    unit_code: str, listing_status: str = "RECENTLY_SOLD", force_refresh: bool = False
) -> list[SimilarUnit]:
    """Get similar units (comps) from cache or fetch fresh."""
    # Similar units don't have a separate cache table; fetch fresh each time per PRD
    # (or cache them in search_runs if doing diff detection)
    unit = await get_or_fetch_eiendom_unit(unit_code)
    if unit is None:
        return []
    vector = build_unit_vector(unit)
    return await get_similar_units(vector, listing_status=listing_status)


async def get_unit_images(unit_code: str, force_refresh: bool = False) -> dict[str, Any]:
    """Fetch unit images for visual assessment."""
    unit = await get_or_fetch_eiendom_unit(unit_code, force_refresh=force_refresh)
    if unit is None:
        raise ValueError(f"Could not fetch Eiendom.no unit {unit_code}")

    return {
        "unit_code": unit.unit_code,
        "address": unit.address,
        "unit_images": unit.unit_images or [],
        "property_type": unit.property_type,
        "rooms": unit.rooms,
        "usable_area": unit.usable_area,
    }


async def resolve_eiendom_unit_from_finn_url(finn_url: str) -> EiendomUnit | None:
    """Resolve an Eiendom.no unit from a FINN listing URL."""
    return await search_unit_from_finn_url(finn_url)


# ============================================================================
# Orchestration functions — delegate to analysis.py
# ============================================================================


async def analyze_search(
    search_url: str,
    *,
    max_pages: int = 3,
    detail_limit: int = 20,
    include_details: bool = True,
    include_eiendom_no: bool = True,
) -> dict[str, Any]:
    """Analyze a FINN search URL and return a ranked shortlist."""
    return await run_analysis_search(
        search_url,
        max_pages=max_pages,
        fetch_details=include_details,
        detail_limit=detail_limit,
        include_eiendom_no=include_eiendom_no,
    )


async def analyze_ad(
    finnkode: str,
    *,
    include_eiendom_no: bool = True,
    include_similar_units: bool = False,
) -> dict[str, Any]:
    """Fetch and enrich a single FINN ad with analysis."""
    ad = await get_or_fetch_ad(finnkode)
    result: dict[str, Any] = {
        "ad": ad.model_dump(),
    }
    if include_eiendom_no and ad.eiendom_unit_code:
        unit = await get_or_fetch_eiendom_unit(ad.eiendom_unit_code)
        if unit:
            result["eiendom_unit"] = unit.model_dump()
            if include_similar_units:
                similar = await get_or_fetch_similar_units(ad.eiendom_unit_code)
                result["similar_units"] = [s.model_dump() for s in similar]
    return result


async def analyze_ad_against_comps(
    finnkode: str, listing_status: str = "RECENTLY_SOLD"
) -> dict[str, Any]:
    """Evaluate one listing against recent comparable sales."""
    ad = await get_or_fetch_ad(finnkode)
    result: dict[str, Any] = {
        "ad": ad.model_dump(),
    }
    if ad.eiendom_unit_code:
        unit = await get_or_fetch_eiendom_unit(ad.eiendom_unit_code)
        if unit:
            result["eiendom_unit"] = unit.model_dump()
            comps = await get_or_fetch_similar_units(
                ad.eiendom_unit_code, listing_status=listing_status
            )
            result["comparable_units"] = [c.model_dump() for c in comps]
    return result


async def find_similar_to_liked(
    finnkode: str, *, mode: str = "recommendations", listing_status: str = "FOR_SALE"
) -> dict[str, Any]:
    """Find properties similar to a listing the user has liked."""
    # Requires that feedback.verdict = "liked" exists for this finnkode
    ad = await get_or_fetch_ad(finnkode)
    if not ad.eiendom_unit_code:
        raise ValueError(
            f"Finnkode {finnkode} has no Eiendom.no unit_code; cannot find similar properties"
        )

    # TODO: verify feedback verdict = "liked" exists
    unit = await get_or_fetch_eiendom_unit(ad.eiendom_unit_code)
    if not unit:
        raise ValueError(f"Cannot enrich finnkode {finnkode} with Eiendom.no data")

    similar = await get_or_fetch_similar_units(ad.eiendom_unit_code, listing_status=listing_status)
    return {
        "base_ad": ad.model_dump(),
        "similar_listings": [s.model_dump() for s in similar],
        "mode": mode,
    }


async def compare_ads(
    finnkoder: list[str], *, include_eiendom_no: bool = True, include_comps: bool = True
) -> dict[str, Any]:
    """Compare multiple FINN listings side by side."""
    ads = []
    for finnkode in finnkoder:
        ad = await get_or_fetch_ad(finnkode)
        ad_data = ad.model_dump()

        if include_eiendom_no and ad.eiendom_unit_code:
            unit = await get_or_fetch_eiendom_unit(ad.eiendom_unit_code)
            if unit:
                ad_data["eiendom_unit"] = unit.model_dump()
                if include_comps:
                    comps = await get_or_fetch_similar_units(
                        ad.eiendom_unit_code, listing_status="RECENTLY_SOLD"
                    )
                    ad_data["comps"] = [c.model_dump() for c in comps]

        ads.append(ad_data)

    return {"listings": ads}


# ============================================================================
# Helper functions
# ============================================================================


async def build_unit_vector_for_unit_code(unit_code: str) -> dict[str, Any]:
    """Build a unit_vector for a unit_code by fetching and encoding the unit data."""
    unit = await get_or_fetch_eiendom_unit(unit_code)
    if unit is None:
        raise ValueError(f"Could not fetch Eiendom.no unit {unit_code}")
    vector = build_unit_vector(unit)
    return {"unit_code": unit_code, "unit_vector": vector}


def decode_unit_vector_to_dict(unit_vector: str) -> dict[str, Any]:
    """Decode a unit_vector string to a dict."""
    return decode_unit_vector(unit_vector)


def save_feedback(finnkode: str, verdict: str, notes: str | None = None) -> dict[str, Any]:
    """Store user feedback/verdict for a listing."""
    return save_feedback_impl(finnkode, verdict, notes)


def get_shortlist(run_id: int | None = None, limit: int = 10) -> dict[str, Any]:
    """Fetch stored shortlist from a search run."""
    # TODO: implement via search_runs table in cache.py
    return {"shortlist": [], "run_id": run_id, "limit": limit}


def get_new_ads_since_last_run(search_url: str) -> dict[str, Any]:
    """Detect new/removed/changed listings vs the previous run."""
    # TODO: implement via search_runs table in cache.py
    return {"new_ads": [], "removed_ads": [], "changed_ads": [], "search_url": search_url}