-- name: LoadRadioCandidates :many
-- Returns all tracks except the seed and any played by the user within
-- the last $3 hours, joined with the stats needed for scoring:
--   is_liked     — boolean from general_likes for this user
--   last_played_at — max(play_events.started_at) for this user/track
--   play_count   — count of play_events for this user/track
--   skip_count   — play_events where was_skipped=true
SELECT
    sqlc.embed(t),
    (l.user_id IS NOT NULL)::bool AS is_liked,
    pe.last_played_at::timestamptz AS last_played_at,
    pe.play_count,
    pe.skip_count
FROM tracks t
LEFT JOIN general_likes l ON l.user_id = $1 AND l.track_id = t.id
LEFT JOIN LATERAL (
    SELECT
        max(started_at) AS last_played_at,
        count(*) AS play_count,
        count(*) FILTER (WHERE was_skipped) AS skip_count
    FROM play_events
    WHERE user_id = $1 AND track_id = t.id
) pe ON true
WHERE t.id <> $2
  AND NOT EXISTS (
    SELECT 1 FROM play_events
    WHERE user_id = $1 AND track_id = t.id
      AND started_at > now() - $3 * interval '1 hour'
  )
  AND NOT EXISTS (
    SELECT 1 FROM lidarr_quarantine q
    WHERE q.user_id = $1 AND q.track_id = t.id
  );

-- name: LoadRadioCandidatesV2 :many
-- M4c: similarity-driven candidate pool. 5-way UNION:
--   $1 user_id, $2 seed_track_id, $3 recently_played_hours,
--   $4 exclude (uuid[]), $5 lb_similar K, $6 similar_artists K,
--   $7 tag_overlap K, $8 likes_overlap K, $9 random_fill K.
-- Returns same shape as LoadRadioCandidates plus similarity_score column.

WITH
seed_artist AS (
    SELECT artist_id
    FROM tracks
    WHERE tracks.id = $2
),
seed_tags AS (
    SELECT trim(g) AS tag
    FROM tracks t
    LEFT JOIN LATERAL regexp_split_to_table(coalesce(t.genre, ''), '[;,]') AS g(tag) ON true
    WHERE t.id = $2 AND trim(g) <> ''
),
excluded_ids AS (
    SELECT unnest($4::uuid[]) AS id
    UNION ALL
    SELECT pe.track_id AS id
    FROM play_events pe
    WHERE pe.user_id = $1 AND pe.started_at > now() - $3 * interval '1 hour'
    UNION ALL
    SELECT q.track_id AS id
    FROM lidarr_quarantine q
    WHERE q.user_id = $1
),
lb_similar AS (
    SELECT ts.track_b_id AS track_id, ts.score AS sim_score
    FROM track_similarity ts
    WHERE ts.track_a_id = $2
      AND ts.source = 'listenbrainz'
      AND ts.track_b_id NOT IN (SELECT id FROM excluded_ids)
    ORDER BY ts.score DESC
    LIMIT $5
),
similar_artists AS (
    SELECT t.id AS track_id, asim.score * 0.5 AS sim_score
    FROM artist_similarity asim
    JOIN tracks t ON t.artist_id = asim.artist_b_id
    JOIN seed_artist sa ON asim.artist_a_id = sa.artist_id
    WHERE asim.source = 'listenbrainz'
      AND t.id NOT IN (SELECT id FROM excluded_ids)
    ORDER BY asim.score DESC, random()
    LIMIT $6
),
tag_overlap AS (
    SELECT t.id AS track_id,
           (count(DISTINCT trim(g))::float8
            / GREATEST((SELECT count(*) FROM seed_tags), 1)) AS sim_score
    FROM tracks t
    LEFT JOIN LATERAL regexp_split_to_table(coalesce(t.genre, ''), '[;,]') AS g_split(g) ON true
    WHERE trim(g_split.g) IN (SELECT tag FROM seed_tags)
      AND t.id NOT IN (SELECT id FROM excluded_ids)
      AND t.id <> $2
    GROUP BY t.id
    HAVING count(DISTINCT trim(g_split.g)) > 0
    ORDER BY sim_score DESC
    LIMIT $7
),
likes_overlap AS (
    SELECT gl.track_id, 0.6::float8 AS sim_score
    FROM general_likes gl
    WHERE gl.user_id = $1
      AND gl.track_id NOT IN (SELECT id FROM excluded_ids)
      AND EXISTS (
          SELECT 1 FROM tracks t
          LEFT JOIN LATERAL regexp_split_to_table(coalesce(t.genre, ''), '[;,]') AS g_overlap(g) ON true
          WHERE t.id = gl.track_id
            AND trim(g_overlap.g) IN (SELECT tag FROM seed_tags)
      )
    ORDER BY random()
    LIMIT $8
),
random_fill AS (
    SELECT t.id AS track_id, 0.0::float8 AS sim_score
    FROM tracks t
    WHERE t.id NOT IN (SELECT id FROM excluded_ids)
      AND t.id <> $2
      AND t.id NOT IN (
          SELECT track_id FROM lb_similar
          UNION SELECT track_id FROM similar_artists
          UNION SELECT track_id FROM tag_overlap
          UNION SELECT track_id FROM likes_overlap
      )
    ORDER BY random()
    LIMIT $9
)
SELECT
    sqlc.embed(t),
    (l.user_id IS NOT NULL)::bool AS is_liked,
    pe.last_played_at::timestamptz AS last_played_at,
    pe.play_count,
    pe.skip_count,
    COALESCE(max(u.sim_score), 0.0) AS similarity_score
FROM (
    SELECT track_id, sim_score FROM lb_similar
    UNION ALL SELECT track_id, sim_score FROM similar_artists
    UNION ALL SELECT track_id, sim_score FROM tag_overlap
    UNION ALL SELECT track_id, sim_score FROM likes_overlap
    UNION ALL SELECT track_id, sim_score FROM random_fill
) u
JOIN tracks t ON t.id = u.track_id
LEFT JOIN general_likes l ON l.user_id = $1 AND l.track_id = t.id
LEFT JOIN LATERAL (
    SELECT max(started_at) AS last_played_at,
           count(*) AS play_count,
           count(*) FILTER (WHERE was_skipped) AS skip_count
    FROM play_events
    WHERE user_id = $1 AND track_id = t.id
) pe ON true
GROUP BY t.id, t.title, t.album_id, t.artist_id, t.duration_ms, t.file_path,
         t.file_format, t.file_size, t.bitrate, t.track_number, t.disc_number,
         t.mbid, t.genre, t.added_at, t.updated_at,
         l.user_id, pe.last_played_at, pe.play_count, pe.skip_count;

-- name: SuggestArtistsForUser :many
-- M5c: per-user artist suggestions ranked by signal x similarity. The
-- seeds CTE collects the user's likes (x5) plus recency-decayed plays
-- (exp(-age_days / $2)). The contributions CTE joins those seeds against
-- artist_similarity_unmatched and filters out candidates already in
-- library or already in a non-terminal lidarr_request. The outer SELECT
-- aggregates per candidate, returning the top-3 contributing seeds for
-- attribution. $1=user_id, $2=half_life_days, $3=limit.
WITH seeds AS (
    SELECT a.id AS artist_id,
           5.0 * (CASE WHEN gla.artist_id IS NOT NULL THEN 1 ELSE 0 END)
           + COALESCE(SUM(EXP(- EXTRACT(epoch FROM now() - pe.started_at) / ($2::float8 * 86400.0))), 0)
           AS signal,
           (gla.artist_id IS NOT NULL) AS is_liked,
           COUNT(pe.id)::bigint AS play_count
    FROM artists a
    LEFT JOIN general_likes_artists gla ON gla.artist_id = a.id AND gla.user_id = $1
    LEFT JOIN tracks t ON t.artist_id = a.id
    LEFT JOIN play_events pe ON pe.track_id = t.id AND pe.user_id = $1
    WHERE gla.artist_id IS NOT NULL OR pe.id IS NOT NULL
    GROUP BY a.id, gla.artist_id
),
contributions AS (
    SELECT u.candidate_mbid,
           u.candidate_name,
           seeds.artist_id AS seed_id,
           seeds.is_liked,
           seeds.play_count,
           seeds.signal * u.score AS contribution
    FROM artist_similarity_unmatched u
    JOIN seeds ON seeds.artist_id = u.seed_artist_id
    WHERE NOT EXISTS (SELECT 1 FROM artists WHERE mbid = u.candidate_mbid)
      AND NOT EXISTS (
          SELECT 1 FROM lidarr_requests r
           WHERE r.user_id = $1
             AND r.lidarr_artist_mbid = u.candidate_mbid
             AND r.status NOT IN ('rejected', 'failed')
      )
)
SELECT candidate_mbid,
       candidate_name,
       SUM(contribution)::float8 AS total_score,
       ((array_agg(seed_id      ORDER BY contribution DESC))[1:3])::uuid[]    AS top_seed_ids,
       ((array_agg(contribution ORDER BY contribution DESC))[1:3])::float8[]  AS top_contributions,
       ((array_agg(is_liked     ORDER BY contribution DESC))[1:3])::boolean[] AS top_is_liked,
       ((array_agg(play_count   ORDER BY contribution DESC))[1:3])::bigint[]  AS top_play_counts
FROM contributions
GROUP BY candidate_mbid, candidate_name
ORDER BY total_score DESC
LIMIT $3;