"""
comparator.py — Compares two Account Profiles CSVs and returns new entries.

Logic
─────
• Unique key : "Account Domain" (the website domain, e.g. dell.com)
• New entry  : a domain present in the NEW csv that is absent in the OLD csv.
• If OLD csv is missing (first ever run) → every row counts as "new".
"""
import logging
from pathlib import Path

import pandas as pd

logger = logging.getLogger(__name__)

# Column used to identify a unique account across exports
_UNIQUE_KEY = "Account Domain"


def _load(path: str) -> pd.DataFrame:
    return pd.read_csv(path, dtype=str).fillna("-").rename(
        columns=lambda c: c.strip()
    )


def find_new_entries(
    new_csv: str,
    old_csv: str | None,
) -> tuple[pd.DataFrame, bool]:
    """
    Parameters
    ----------
    new_csv : path to the freshly downloaded CSV.
    old_csv : path to the previously stored CSV, or None.

    Returns
    -------
    (new_entries_df, is_first_run)
        new_entries_df — DataFrame of rows that are brand-new.
        is_first_run   — True when there was no previous CSV to compare against.
    """
    new_df = _load(new_csv)

    if _UNIQUE_KEY not in new_df.columns:
        # The downloaded CSV might have a slightly different column order;
        # treat the first column as the domain key.
        logger.warning(
            f"Column '{_UNIQUE_KEY}' not found. "
            f"Available columns: {list(new_df.columns)}"
        )
        first_col = new_df.columns[0]
        new_df.rename(columns={first_col: _UNIQUE_KEY}, inplace=True)
        logger.warning(f"Using '{first_col}' as the unique key instead.")

    if old_csv is None or not Path(old_csv).exists():
        logger.info("No previous CSV found — treating ALL entries as new (first run).")
        return new_df, True

    old_df   = _load(old_csv)
    old_keys = set(
        old_df[_UNIQUE_KEY].str.strip().str.lower()
        if _UNIQUE_KEY in old_df.columns
        else old_df.iloc[:, 0].str.strip().str.lower()
    )
    mask         = ~new_df[_UNIQUE_KEY].str.strip().str.lower().isin(old_keys)
    new_entries  = new_df[mask].copy().reset_index(drop=True)

    logger.info(
        f"Comparison complete: {len(new_entries)} new / "
        f"{len(new_df)} total (previous had {len(old_df)} rows)"
    )
    return new_entries, False