"""
game/datastore.py — persistence to the public sightings dataset.

Two responsibilities:
  * leaderboard read/write   (Phase 1)
  * append_sighting()        (Phase 2 — the upload flywheel)

Both write to HomesteaderLabs/forager-sightings via huggingface_hub, authenticated
by the HF_TOKEN Space secret. When no token is present (local dev, or the secret
isn't set yet) everything falls back to in-memory so the game still runs — the
leaderboard just won't persist across restarts until the secret is added.

Concurrency note: the leaderboard is a read-modify-write on one jsonl file, so
simultaneous posts can race (last write wins). Fine at demo scale; revisit with a
queue or per-row append if it ever matters.
"""

import datetime
import json
import os

DATASET_REPO = "HomesteaderLabs/forager-sightings"            # public: in-domain finds
REVIEW_REPO = "HomesteaderLabs/forager-sightings-review"      # private: router-rejected quarantine
LICENSE = "CC-BY-4.0"

_TOKEN = os.environ.get("HF_TOKEN")

# in-memory fallback: contributor -> aggregated score row
_mem_leaderboard: dict[str, dict] = {}


def persistence_enabled() -> bool:
    return bool(_TOKEN)


def _now() -> str:
    return datetime.datetime.now(datetime.timezone.utc).isoformat(timespec="seconds")


def load_leaderboard() -> list[dict]:
    """Return all aggregated score rows (from the dataset if a token is set, else memory)."""
    if _TOKEN:
        try:
            from huggingface_hub import hf_hub_download
            path = hf_hub_download(DATASET_REPO, "leaderboard.jsonl", repo_type="dataset",
                                   token=_TOKEN, force_download=True)
            with open(path) as f:
                return [json.loads(line) for line in f if line.strip()]
        except Exception:
            pass
    return list(_mem_leaderboard.values())


def post_score(contributor: str, you_correct: int, total: int, machine_correct: int) -> list[dict]:
    """Add this session's tally to the contributor's cumulative row; persist; return all rows."""
    rows = {r["contributor"]: r for r in load_leaderboard()}
    r = rows.get(contributor, {
        "contributor": contributor, "skill_correct": 0, "skill_total": 0,
        "machine_correct": 0, "contributions": 0,
    })
    r["skill_correct"] += int(you_correct)
    r["skill_total"] += int(total)
    r["machine_correct"] += int(machine_correct)
    r["updated"] = _now()
    rows[contributor] = r

    if _TOKEN:
        try:
            from huggingface_hub import HfApi
            body = "\n".join(json.dumps(x) for x in rows.values())
            HfApi(token=_TOKEN).upload_file(
                path_or_fileobj=body.encode("utf-8"), path_in_repo="leaderboard.jsonl",
                repo_id=DATASET_REPO, repo_type="dataset",
                commit_message=f"score: {contributor}",
            )
        except Exception:
            _mem_leaderboard.update(rows)
    else:
        _mem_leaderboard.update(rows)
    return list(rows.values())


def load_contributors() -> list[dict]:
    """Count accepted sightings per contributor from metadata.jsonl (the Contributor board)."""
    if not _TOKEN:
        return []
    try:
        from collections import Counter
        from huggingface_hub import hf_hub_download
        path = hf_hub_download(DATASET_REPO, "metadata.jsonl", repo_type="dataset",
                               token=_TOKEN, force_download=True)
        with open(path) as f:
            rows = [json.loads(line) for line in f if line.strip()]
        counts = Counter(r.get("contributor", "?") for r in rows)
        return [{"contributor": k, "count": v} for k, v in counts.most_common()]
    except Exception:
        return []


# Near-duplicate detection via perceptual hash (dHash). Catches the same photo
# re-saved/resized/re-compressed, not just byte-identical files — which is the
# realistic abuse/pollution case (re-uploading a popular web image, gaming the
# contributor board, accidental double-submits). PIL + numpy only, no new dep.
DUP_HAMMING_THRESHOLD = 5   # <=5 of 64 bits differ => treat as the same image


def compute_phash(image) -> str:
    """64-bit dHash as a 16-char hex string (row->row horizontal gradient)."""
    import numpy as np
    from PIL import Image
    small = image.convert("L").resize((9, 8), Image.BILINEAR)
    a = np.asarray(small, dtype=np.int16)
    bits = (a[:, 1:] > a[:, :-1]).flatten()           # 8x8 = 64 bits
    val = 0
    for b in bits:
        val = (val << 1) | int(b)
    return f"{val:016x}"


def _hamming(a: str, b: str) -> int:
    return bin(int(a, 16) ^ int(b, 16)).count("1")


def _store(repo: str, image, base_row: dict, contributor: str, msg: str) -> str:
    """Dedup against `repo`'s metadata.jsonl, then commit image + metadata row.
    Returns "stored" | "duplicate" | "disabled". Used by both the public sightings
    write and the private review-queue write."""
    if not _TOKEN:
        return "disabled"
    from io import BytesIO
    from huggingface_hub import CommitOperationAdd, HfApi, hf_hub_download

    existing, rows = "", []
    try:
        with open(hf_hub_download(repo, "metadata.jsonl", repo_type="dataset",
                                  token=_TOKEN, force_download=True)) as f:
            txt = f.read()
        existing = txt.rstrip("\n")
        rows = [json.loads(line) for line in txt.splitlines() if line.strip()]
    except Exception:
        pass

    ph = compute_phash(image)
    for r in rows:
        h = r.get("phash")
        if h and _hamming(h, ph) <= DUP_HAMMING_THRESHOLD:
            return "duplicate"

    ts = _now()
    fname = f"images/{contributor}_{ts.replace(':', '').replace('-', '')}.jpg"
    buf = BytesIO()
    image.convert("RGB").save(buf, format="JPEG", quality=90)
    row = {"file_name": fname, **base_row, "contributor": contributor,
           "consent": True, "license": LICENSE, "timestamp": ts, "phash": ph}
    new_meta = (existing + "\n" if existing else "") + json.dumps(row) + "\n"
    try:
        HfApi(token=_TOKEN).create_commit(
            repo_id=repo, repo_type="dataset", commit_message=msg,
            operations=[
                CommitOperationAdd(path_in_repo=fname, path_or_fileobj=buf.getvalue()),
                CommitOperationAdd(path_in_repo="metadata.jsonl",
                                   path_or_fileobj=new_meta.encode("utf-8")),
            ],
        )
    except Exception:
        # e.g. the Space token lacks write scope on this repo — degrade gracefully
        # so the UI shows a friendly message instead of crashing the handler.
        return "disabled"
    return "stored"


def append_sighting(image, user_label: str, machine: dict, contributor: str) -> str:
    """In-domain find -> public dataset. Returns stored/duplicate/disabled."""
    return _store(DATASET_REPO, image, {
        "user_label": user_label,
        "machine_prediction": machine.get("species", "unknown"),
        "machine_confidence": round(float(machine.get("confidence", 0.0)), 4),
        "machine_abstained": bool(machine.get("abstained", True)),
        "machine_safety": machine.get("safety", "UNKNOWN"),
        "routed_domain": machine.get("domain", "unknown"),
    }, contributor, f"sighting: {machine.get('species', 'unknown')} by {contributor}")


def append_unrouted(image, user_label: str, router: dict, contributor: str) -> str:
    """Router-rejected (out-of-domain) find -> PRIVATE review queue for later triage.
    Captures the model's blind spots (real forageables the router fumbles). Returns
    stored/duplicate/disabled."""
    return _store(REVIEW_REPO, image, {
        "status": "unrouted",
        "user_label": user_label,
        "router_domain": router.get("domain", "unknown"),
        "router_confidence": round(float(router.get("domain_confidence", 0.0)), 4),
        "reason": router.get("reason", ""),
    }, contributor, f"review: {user_label} by {contributor}")