import re
from pathlib import Path
from typing import Optional

import pandas as pd


def excel_food_sales_total_usd(path: str) -> str:
    """
    Sum sales for food items excluding drinks from the GAIA-style fast-food workbook.
    Heuristic: classify rows using a Category/Type/Menu column; exclude drink/beverage/soda.
    """
    p = Path(path)
    if not p.exists():
        return f"Error: file not found: {path}"
    try:
        xl = pd.ExcelFile(path)
    except Exception as e:
        return f"Error opening Excel: {e}"

    total = 0.0
    notes: list[str] = []

    for sheet in xl.sheet_names:
        try:
            df = pd.read_excel(xl, sheet_name=sheet)
        except Exception as e:
            notes.append(f"{sheet}: read error {e}")
            continue
        if df.empty:
            continue
        cols_lower = {str(c).lower(): c for c in df.columns}

        cat_col = None
        for key in (
            "category",
            "type",
            "menu category",
            "item type",
            "group",
        ):
            if key in cols_lower:
                cat_col = cols_lower[key]
                break

        money_col = None
        for c in df.columns:
            cl = str(c).lower()
            if any(
                w in cl
                for w in ("sales", "revenue", "total", "amount", "usd", "price")
            ):
                if df[c].dtype == object or pd.api.types.is_numeric_dtype(df[c]):
                    money_col = c
                    break
        if money_col is None:
            for c in df.columns:
                if pd.api.types.is_numeric_dtype(df[c]):
                    money_col = c
                    break

        if money_col is None:
            notes.append(f"{sheet}: no numeric sales column found")
            continue

        for _, row in df.iterrows():
            val = row[money_col]
            if pd.isna(val):
                continue
            try:
                amount = float(val)
            except (TypeError, ValueError):
                s = str(val).replace("$", "").replace(",", "").strip()
                try:
                    amount = float(s)
                except ValueError:
                    continue

            if cat_col is not None:
                raw = row[cat_col]
                label = str(raw).lower() if pd.notna(raw) else ""
                if any(
                    d in label
                    for d in (
                        "drink",
                        "beverage",
                        "soda",
                        "coffee",
                        "tea",
                        "juice",
                        "water",
                        "shake",
                        "smoothie",
                    )
                ):
                    continue
            total += amount

    if total == 0 and not notes:
        return "Error: could not aggregate (no matching rows)."
    return f"{total:.2f}"