"""Check a video against the dataset to guess whether it matches something already stored."""

import imagehash

import storage
from fingerprint import extract_fingerprint

# hash_size=8 -> 64-bit phash, so distances range 0-64.
DEFAULT_HAMMING_THRESHOLD = 10
DEFAULT_MATCH_RATIO = 0.5


def _hex_to_hash(h: str):
    return imagehash.hex_to_hash(h)


def compare_to_video(query_hashes: list, stored_hashes: list, hamming_threshold: int) -> float:
    """Fraction of query frames that have a close match somewhere in the stored video."""
    if not query_hashes or not stored_hashes:
        return 0.0

    stored_decoded = [_hex_to_hash(f["hash"]) for f in stored_hashes]
    hits = 0
    for qf in query_hashes:
        qh = _hex_to_hash(qf["hash"])
        best = min(qh - sh for sh in stored_decoded)
        if best <= hamming_threshold:
            hits += 1
    return hits / len(query_hashes)


def check_video(
    query_path: str,
    hamming_threshold: int = DEFAULT_HAMMING_THRESHOLD,
    match_ratio_threshold: float = DEFAULT_MATCH_RATIO,
    samples_per_second: float = 1.0,
) -> dict:
    query_hashes = extract_fingerprint(query_path, samples_per_second=samples_per_second)

    candidates = []
    for meta in storage.list_videos():
        video_id = meta["id"]
        stored_hashes = storage.load_all_hashes(video_id)
        ratio = compare_to_video(query_hashes, stored_hashes, hamming_threshold)
        candidates.append({"id": video_id, "title": meta["title"], "match_ratio": round(ratio, 3)})

    candidates.sort(key=lambda c: c["match_ratio"], reverse=True)
    best = candidates[0] if candidates else None
    is_match = bool(best and best["match_ratio"] >= match_ratio_threshold)

    return {
        "query_path": query_path,
        "is_likely_copyrighted": is_match,
        "best_match": best,
        "top_candidates": candidates[:5],
    }