"""
Matches extracted item names against a product catalogue (CSV with sku_code,
product_name, category, unit) using fuzzy string matching.
"""

import pandas as pd
from rapidfuzz import process, fuzz

# Below this score (0-100), a match is flagged as low-confidence for human review.
DEFAULT_CONFIDENCE_THRESHOLD = 70


def load_catalogue(csv_path_or_buffer) -> pd.DataFrame:
    """Load a catalogue CSV. Expects columns: sku_code, product_name, category, unit."""
    df = pd.read_csv(csv_path_or_buffer)
    required_cols = {"sku_code", "product_name", "category", "unit"}
    missing = required_cols - set(c.strip() for c in df.columns)
    if missing:
        raise ValueError(
            f"Catalogue is missing required columns: {missing}. "
            f"Found columns: {list(df.columns)}"
        )
    df["sku_code"] = df["sku_code"].astype(str)
    return df


def match_item(item_name: str, catalogue: pd.DataFrame, threshold: int = DEFAULT_CONFIDENCE_THRESHOLD) -> dict:
    """
    Find the best matching catalogue entry for a given item name.

    Returns a dict with sku_code, matched_name, category, unit, confidence,
    and needs_review (bool).
    """
    if catalogue is None or catalogue.empty or not item_name:
        return {
            "sku_code": None,
            "matched_name": None,
            "category": None,
            "unit": None,
            "confidence": 0,
            "needs_review": True,
        }

    choices = catalogue["product_name"].tolist()
    result = process.extractOne(item_name, choices, scorer=fuzz.WRatio)

    if result is None:
        return {
            "sku_code": None,
            "matched_name": None,
            "category": None,
            "unit": None,
            "confidence": 0,
            "needs_review": True,
        }

    matched_name, score, idx = result
    row = catalogue.iloc[idx]

    return {
        "sku_code": row["sku_code"],
        "matched_name": row["product_name"],
        "category": row["category"],
        "unit": row["unit"],
        "confidence": round(score, 1),
        "needs_review": score < threshold,
    }


def enrich_items(items: list[dict], catalogue: pd.DataFrame, threshold: int = DEFAULT_CONFIDENCE_THRESHOLD) -> list[dict]:
    """
    Take extracted items (from extraction.py) and attach catalogue matches.

    Returns a new list of dicts combining original extraction with match info.
    """
    enriched = []
    for item in items:
        match = match_item(item.get("item_name", ""), catalogue, threshold)
        enriched.append({
            "raw_text": item.get("raw_text"),
            "extracted_name": item.get("item_name"),
            "quantity": item.get("quantity"),
            "unit": item.get("unit"),
            "sku_code": match["sku_code"],
            "matched_product": match["matched_name"],
            "category": match["category"],
            "catalogue_unit": match["unit"],
            "match_confidence": match["confidence"],
            "needs_review": match["needs_review"],
        })
    return enriched