from collections import defaultdict

from photoflow.core.models import Cluster
from photoflow.immich import pipeline
from app.clustering import cluster_assets, _epoch
from app.coverage import coverage_members

# A tag seeds only if its photos dominate the time window they span: among all
# assets whose taken_at falls in [min, max] of the tag's photos, at least this
# fraction must carry the tag. A name/event tag sprinkled among many untagged
# photos in the same window fails this gate.
SEED_DOMINANCE_MIN = 0.5


def _seed_tags(assets, tags_by_asset, max_span_days: int,
               dominance_min: float = SEED_DOMINANCE_MIN) -> set:
    epochs = {a.immich_id: _epoch(a.taken_at) for a in assets}
    tagged = defaultdict(list)  # tag -> epochs of its assets
    for a in assets:
        for t in tags_by_asset.get(a.immich_id, []):
            if not pipeline.is_pipeline_tag(t):
                tagged[t].append(epochs[a.immich_id])

    seeds = set()
    for tag, ts in tagged.items():
        if len(ts) < 2:
            continue
        lo, hi = min(ts), max(ts)
        # Loose safety cap only — reject absurdly long spans, but keep genuine
        # long trips (this library has one > 60 days), so the default is high.
        if hi - lo > max_span_days * 86400:
            continue
        # Dominance / temporal contiguity gate.
        in_window = sum(1 for e in epochs.values() if lo <= e <= hi)
        if in_window and len(ts) / in_window >= dominance_min:
            seeds.add(tag)
    return seeds


def run_cluster(store, *, gap_factor: float = 6.0, seed_max_span_days: int = 400) -> dict:
    assets = store.all_assets(include_processed=False)
    tags_by_asset = {a.immich_id: store.asset_tags(a.immich_id) for a in assets}
    seed_tags = _seed_tags(assets, tags_by_asset, seed_max_span_days)
    candidates = cluster_assets(assets, tags_by_asset, seed_tags, gap_factor=gap_factor)
    assets_by_id = {a.immich_id: a for a in assets}

    store.clear_clusters()
    n = 0
    for cand in candidates:
        members = coverage_members(cand, assets_by_id)
        store.insert_cluster(Cluster(
            start_at=cand.start_at, end_at=cand.end_at,
            count=sum(1 for m in members if m.included),
            suggested_name=cand.suggested_name, confidence=cand.confidence,
            kind_guess=cand.kind_guess, status="pending"), members)
        n += 1
    return {"clusters": n}