"""
candidate_filter.py — CPU-only candidate retrieval from a precomputed
arXiv pool.

The arxiv_pool.parquet (built daily by scripts/update_arxiv_pool.py)
contains for each paper: arxiv_id, title, abstract, published_at,
abstract_embedding (384-dim, all-MiniLM-L6-v2). Embedding the pool
is the expensive step — done once per day, off the Space.

Per-request work: embed the team-context query (~50ms on CPU), do a
cosine top-k against the cached pool embeddings (numpy, ~5ms for 500
candidates). No GPU touched.
"""
from __future__ import annotations

import os
from functools import lru_cache
from pathlib import Path

import numpy as np
import pandas as pd
from huggingface_hub import hf_hub_download
from sentence_transformers import SentenceTransformer

EMBEDDER_NAME = "sentence-transformers/all-MiniLM-L6-v2"
# TODO: replace with the actual HF dataset once arxiv_pool cron is wired.
ARXIV_POOL_REPO = os.environ.get("ARXIV_POOL_REPO", "remyxai/arxiv_pool_daily")
ARXIV_POOL_FILE = os.environ.get("ARXIV_POOL_FILE", "arxiv_pool.parquet")

_embedder: SentenceTransformer | None = None
_pool: pd.DataFrame | None = None
_pool_embeddings: np.ndarray | None = None


def load_embedder() -> SentenceTransformer:
    global _embedder
    if _embedder is None:
        print(f"[candidate_filter] Loading {EMBEDDER_NAME}…")
        # device="cpu" is required, not just intentional: SentenceTransformer's
        # default auto-detects CUDA and moves the model there at __init__,
        # which initializes CUDA in the parent process — fatal on ZeroGPU
        # (worker_init then fails with "No CUDA GPUs are available").
        _embedder = SentenceTransformer(EMBEDDER_NAME, device="cpu")
    return _embedder


def load_pool() -> pd.DataFrame:
    """Load the precomputed arxiv pool. Cached for the container lifetime."""
    global _pool, _pool_embeddings
    if _pool is not None:
        return _pool

    hf_token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_TOKEN")
    print(f"[candidate_filter] Downloading {ARXIV_POOL_REPO}/{ARXIV_POOL_FILE}…")
    try:
        path = hf_hub_download(
            repo_id=ARXIV_POOL_REPO,
            filename=ARXIV_POOL_FILE,
            repo_type="dataset",
            token=hf_token,
        )
        _pool = pd.read_parquet(path)
    except Exception as e:
        # Fall back to local file for development
        local = Path(__file__).parent / "arxiv_pool.parquet"
        if local.exists():
            print(f"[candidate_filter] Hub fetch failed ({e}); using local {local}")
            _pool = pd.read_parquet(local)
        else:
            raise RuntimeError(
                f"No arxiv pool available. Set ARXIV_POOL_REPO or place "
                f"arxiv_pool.parquet at {local}."
            ) from e

    # Materialize embeddings as a contiguous float32 ndarray for fast cosine
    embs = np.stack(_pool["abstract_embedding"].values).astype(np.float32)
    # L2-normalize so cosine sim == dot product
    norms = np.linalg.norm(embs, axis=1, keepdims=True) + 1e-12
    _pool_embeddings = embs / norms
    print(f"[candidate_filter] Pool loaded: {len(_pool):,} papers, {embs.shape[1]}-dim embeddings")
    return _pool


def top_k_candidates(team_ctx: str, k: int = 50) -> pd.DataFrame:
    """Embed `team_ctx` and return the top-k papers from the pool by
    cosine similarity. Pure CPU; no GPU contention."""
    pool = load_pool()
    embedder = load_embedder()

    query = embedder.encode(team_ctx, normalize_embeddings=True).astype(np.float32)
    sims = _pool_embeddings @ query              # (N,) cosine sims
    top_idx = np.argpartition(-sims, kth=min(k, len(sims) - 1))[:k]
    # Sort the top-k by similarity (argpartition isn't ordered)
    top_idx = top_idx[np.argsort(-sims[top_idx])]

    result = pool.iloc[top_idx].copy()
    result["similarity"] = sims[top_idx]
    return result.reset_index(drop=True)