"""Deterministic, watch-history-based recommendation engine.

The scoring model is intentionally simple and explainable (section 8 of the
spec). It is pure: ``build_profile`` and ``score_candidate`` touch no I/O and are
the unit under test. ``build_candidates`` is the only function that calls Emby.
"""

from __future__ import annotations

from dataclasses import dataclass, field

from .emby_collections import ITEM_FIELDS, normalize_item

# Points awarded per matching facet. Genre is the strongest signal.
SCORE_GENRE = 5          # per shared genre
SCORE_SERIES = 4         # same series / franchise
SCORE_DIRECTOR = 3       # per shared director
SCORE_ACTOR = 2          # per shared actor
SCORE_STUDIO = 2         # per shared studio
SCORE_DECADE = 1         # release decade seen in history
SCORE_MEDIA_TYPE = 1     # media type seen in history

DEFAULT_TARGET_SIZE = 25
_CANDIDATE_FETCH_LIMIT = 300
_MAX_GENRE_QUERY = 8


def _decade(year) -> int | None:
    try:
        return (int(year) // 10) * 10
    except (TypeError, ValueError):
        return None


@dataclass
class TasteProfile:
    """Aggregated facets of a user's watch history."""

    genres: set[str] = field(default_factory=set)
    series: set[str] = field(default_factory=set)
    directors: set[str] = field(default_factory=set)
    actors: set[str] = field(default_factory=set)
    studios: set[str] = field(default_factory=set)
    decades: set[int] = field(default_factory=set)
    media_types: set[str] = field(default_factory=set)

    @property
    def is_empty(self) -> bool:
        return not (
            self.genres or self.series or self.directors or self.actors
            or self.studios or self.decades or self.media_types
        )


def build_profile(watched_items: list[dict]) -> TasteProfile:
    """Aggregate normalized watched items into a :class:`TasteProfile`."""
    profile = TasteProfile()
    for item in watched_items:
        profile.genres.update(item.get("genres") or [])
        profile.directors.update(item.get("directors") or [])
        profile.actors.update(item.get("actors") or [])
        profile.studios.update(item.get("studios") or [])
        profile.media_types.add(item.get("media_type") or item.get("type") or "")
        series = item.get("series_name")
        if series:
            profile.series.add(series)
        # A watched series is itself a "franchise" anchor for similar items.
        if (item.get("type") == "Series") and item.get("title"):
            profile.series.add(item["title"])
        decade = _decade(item.get("year"))
        if decade is not None:
            profile.decades.add(decade)
    profile.media_types.discard("")
    return profile


def score_candidate(candidate: dict, profile: TasteProfile) -> int:
    """Score a candidate against the profile. Higher is more similar."""
    score = 0
    score += SCORE_GENRE * len(set(candidate.get("genres") or []) & profile.genres)
    score += SCORE_DIRECTOR * len(set(candidate.get("directors") or []) & profile.directors)
    score += SCORE_ACTOR * len(set(candidate.get("actors") or []) & profile.actors)
    score += SCORE_STUDIO * len(set(candidate.get("studios") or []) & profile.studios)

    series = candidate.get("series_name") or (candidate.get("title") if candidate.get("type") == "Series" else None)
    if series and series in profile.series:
        score += SCORE_SERIES

    decade = _decade(candidate.get("year"))
    if decade is not None and decade in profile.decades:
        score += SCORE_DECADE

    media_type = candidate.get("media_type") or candidate.get("type")
    if media_type and media_type in profile.media_types:
        score += SCORE_MEDIA_TYPE

    return score


def rank_candidates(candidates: list[dict], profile: TasteProfile) -> list[dict]:
    """Score, filter to score > 0, and sort candidates.

    Order: score desc, then community rating desc, then title asc. Each returned
    item carries an added ``score`` key for transparency in the UI/logs.
    """
    scored = []
    for candidate in candidates:
        score = score_candidate(candidate, profile)
        if score <= 0:
            continue
        scored.append({**candidate, "score": score})
    scored.sort(
        key=lambda c: (-c["score"], -(c.get("community_rating") or 0.0), (c.get("title") or "").casefold())
    )
    return scored


async def build_candidates(client, user_id: str, profile: TasteProfile, exclude_ids: set[str]) -> list[dict]:
    """Fetch unplayed library items similar to the profile, minus exclusions.

    Uses the user-scoped ``IsUnplayed`` filter so already-watched items never
    enter the pool. Anything in ``exclude_ids`` (playlist members, defensively
    re-checked watched ids) is dropped.
    """
    if profile.is_empty:
        return []

    genres = sorted(profile.genres)[:_MAX_GENRE_QUERY]
    media_types = ",".join(sorted(t for t in profile.media_types if t)) or "Movie,Series"
    params = {
        "Recursive": "true",
        "Filters": "IsUnplayed",
        "IsPlayed": "false",
        "IncludeItemTypes": media_types if media_types in ("Movie", "Series", "Movie,Series") else "Movie,Series",
        "Fields": ITEM_FIELDS,
        "EnableUserData": "true",
        "SortBy": "CommunityRating",
        "SortOrder": "Descending",
        "Limit": str(_CANDIDATE_FETCH_LIMIT),
    }
    if genres:
        # Emby treats "|" as OR across genre values.
        params["Genres"] = "|".join(genres)

    data = await client.get(f"/Users/{user_id}/Items", params)
    raw_items = data.get("Items", []) if isinstance(data, dict) else (data or [])

    seen: set[str] = set()
    candidates: list[dict] = []
    for raw in raw_items:
        item = normalize_item(raw)
        item_id = item["id"]
        if not item_id or item_id in exclude_ids or item_id in seen:
            continue
        if item.get("watched"):  # defensive: never recommend a watched item
            continue
        seen.add(item_id)
        candidates.append(item)
    return candidates