import hashlib
import json
from datetime import datetime, timedelta
from typing import Any

import pandas as pd

from src.storage import load_leaderboard, save_leaderboard

# All available metric columns (computed)
ALL_METRIC_COLS = [
    "Recall@1", "Recall@5", "Recall@10", "Recall@20", "Recall@50", "Recall@100",
    "NDCG@1", "NDCG@5", "NDCG@10", "NDCG@20", "NDCG@50", "NDCG@100",
]

# Default columns shown on leaderboard
DEFAULT_DISPLAY_METRICS = [
    "Recall@1", "Recall@5", "Recall@20", "Recall@50",
    "NDCG@1", "NDCG@5", "NDCG@20", "NDCG@50",
]

# Base columns always shown
BASE_COLS = ["rank", "model_name"]

_DEFAULT_SORT = "Recall@10"
_TOP_N = 30
_RETENTION_DAYS = 30


def make_id(email: str, model_name: str) -> str:
    return hashlib.sha256(f"{email}:{model_name}".encode()).hexdigest()[:16]


class LeaderboardManager:
    def __init__(self):
        self._entries: list[dict] = []
        self._load()
        self._cleanup()

    def _load(self):
        raw = load_leaderboard()
        self._entries = raw

    def _save(self):
        save_leaderboard(self._entries)

    def _cleanup(self):
        """Remove non-paper entries older than 30 days that are not in top 30."""
        if not self._entries:
            return

        df = pd.DataFrame(self._entries)
        if _DEFAULT_SORT in df.columns:
            top_ids = set(
                df.sort_values(by=_DEFAULT_SORT, ascending=False)
                .head(_TOP_N)["submission_id"]
                .tolist()
            )
        else:
            top_ids = set()

        cutoff = datetime.utcnow() - timedelta(days=_RETENTION_DAYS)
        kept = []
        for e in self._entries:
            sid = e.get("submission_id", "")
            is_paper = e.get("is_paper_data", False)
            ts_str = e.get("timestamp", "")
            try:
                ts = datetime.fromisoformat(ts_str.replace("Z", "+00:00"))
            except Exception:
                ts = datetime.utcnow()

            if is_paper or sid in top_ids or ts >= cutoff:
                kept.append(e)

        removed = len(self._entries) - len(kept)
        if removed > 0:
            print(f"[CLEANUP] Removed {removed} expired entries")
            self._entries = kept
            self._save()

    def add_result(
        self,
        email: str,
        method: str,
        model_name: str,
        albums: list[str],
        evaluated_queries: int,
        total_gt_queries: int,
        global_metrics: dict,
    ) -> dict | None:
        """Add a new evaluation result. Returns entry if added, None if not eligible."""
        # Must be a full submission (all 3 albums, all queries matched)
        if set(albums) != {"1", "2", "3"}:
            return None
        if evaluated_queries < total_gt_queries:
            return None

        submission_id = make_id(email, model_name)
        entry = {
            "submission_id": submission_id,
            "timestamp": datetime.utcnow().isoformat() + "Z",
            "email": email,
            "method": method,
            "model_name": model_name,
            "albums": ",".join(albums),
            "is_paper_data": False,
            **{k: round(v, 4) for k, v in global_metrics.items() if k in ALL_METRIC_COLS or k in ("Recall", "NDCG")},
        }

        # Keep best score per (email, model_name)
        key = (email, model_name)
        existing_idx = None
        for i, e in enumerate(self._entries):
            if (e.get("email"), e.get("model_name")) == key:
                existing_idx = i
                break

        if existing_idx is not None:
            old = self._entries[existing_idx]
            if global_metrics.get(_DEFAULT_SORT, 0) >= old.get(_DEFAULT_SORT, 0):
                self._entries[existing_idx] = entry
        else:
            self._entries.append(entry)

        self._save()
        return entry

    def get_display_df(
        self,
        method_filter: str | None = None,
        sort_by: str = _DEFAULT_SORT,
        ascending: bool = False,
        top_n: int = _TOP_N,
        metric_cols: list[str] | None = None,
    ) -> pd.DataFrame:
        """Return a pandas DataFrame ready for gr.DataFrame."""
        cols_to_show = BASE_COLS + (metric_cols or DEFAULT_DISPLAY_METRICS)

        if not self._entries:
            return pd.DataFrame(columns=cols_to_show)

        df = pd.DataFrame(self._entries)

        if method_filter and method_filter != "All":
            df = df[df["method"] == method_filter]

        if sort_by not in df.columns:
            sort_by = _DEFAULT_SORT

        df = df.sort_values(by=sort_by, ascending=ascending)
        df = df.head(top_n).reset_index(drop=True)
        df["rank"] = df.index + 1

        available = [c for c in cols_to_show if c in df.columns]
        df = df[available]
        return df

    def remove_entry(self, submission_id: str) -> bool:
        """Remove an entry by submission_id. Returns True if removed."""
        original_len = len(self._entries)
        self._entries = [e for e in self._entries if e.get("submission_id") != submission_id]
        if len(self._entries) < original_len:
            self._save()
            return True
        return False