"""Workflow: Library search (scaffold).

High-level workflow to perform library similarity search over spectral
datasets and produce ranked matches and optional artifacts.
"""

from __future__ import annotations

from dataclasses import dataclass
from typing import Any, Dict, List, Optional

import numpy as np
import pandas as pd


@dataclass
class LibrarySearchWorkflow:
    """Run a library search workflow (scaffold).

    Parameters
    ----------
    metric : str
        Similarity metric ('cosine', 'sid', 'sam').
    top_k : int
        Number of top matches to return.

    Methods
    -------
    run(library_df, query, wavenumbers)
        Return a DataFrame with columns ['index', 'label', 'score', 'confidence', 'metric'].
    validate()
        Validate configuration.
    to_dict()
        JSON-friendly dict of configuration.
    __hash__()
        Hash of configuration for reproducibility.
    """

    metric: str = "cosine"
    top_k: int = 5

    def run(
        self, library_df: pd.DataFrame, query: np.ndarray, wavenumbers: Optional[np.ndarray] = None
    ) -> pd.DataFrame:
        """Execute the library search (placeholder).

        Parameters
        ----------
        library_df : pd.DataFrame
            Library spectra in wide format (metadata + spectral columns).
        query : np.ndarray
            Query spectrum as 1-D array.
        wavenumbers : Optional[np.ndarray]
            Wavenumber grid for plotting or alignment (unused in scaffold).

        Returns
        -------
        pd.DataFrame
            Ranked matches with placeholder scores/confidence.
        """
        # TODO: Replace with actual scoring using foodspec.library_search utilities
        labels = [str(i) for i in range(len(library_df))]
        scores = np.zeros(len(labels), dtype=float)
        conf = np.zeros(len(labels), dtype=float)
        out = pd.DataFrame(
            {
                "index": list(range(len(labels))),
                "label": labels,
                "score": scores,
                "confidence": conf,
                "metric": [self.metric] * len(labels),
            }
        )
        return out.head(self.top_k)

    def validate(self) -> Dict[str, Any]:
        issues: List[str] = []
        if self.metric not in {"cosine", "sid", "sam"}:
            issues.append(f"unknown metric: {self.metric}")
        if self.top_k <= 0:
            issues.append("top_k must be > 0")
        return {"ok": len(issues) == 0, "issues": issues}

    def to_dict(self) -> Dict[str, Any]:
        return {"metric": self.metric, "top_k": int(self.top_k)}

    def __hash__(self) -> int:
        return hash((self.metric, self.top_k))


__all__ = ["LibrarySearchWorkflow"]
