import os
from pathlib import Path
import pandas as pd


def find_duplicates(base_file: Path, ps_files: list[Path], id_col_candidates=None, grade_col_candidates=None):
    """Read the base registration file and several ps files, then find IDs that appear in both.

    Returns a DataFrame with columns: ID, Cijfer, SourceFile
    """
    if id_col_candidates is None:
        id_col_candidates = ["ID", "Id", "id", "inschrijving_id"]
    if grade_col_candidates is None:
        grade_col_candidates = ["Cijfer", "cijfer", "Grade", "grade"]

    # Read base IDs
    print(f"Reading base file: {base_file}")
    base_df = pd.read_excel(base_file)

    # find ID column in base
    base_id_col = next((c for c in base_df.columns if c in id_col_candidates), None)
    if base_id_col is None:
        raise ValueError(f"Could not find an ID column in {base_file}. Tried: {id_col_candidates}")

    base_ids = set(base_df[base_id_col].dropna().astype(str).str.strip())
    print(f"Found {len(base_ids)} IDs in base file (column '{base_id_col}').")

    duplicates = []

    for pf in ps_files:
        print(f"Processing ps file: {pf}")
        try:
            df = pd.read_excel(pf)
        except Exception as e:
            print(f"  Skipping {pf} - failed to read: {e}")
            continue

        # guess ID column
        id_col = next((c for c in df.columns if c in id_col_candidates), None)
        if id_col is None:
            # try fuzzy: column name contains 'id'
            id_col = next((c for c in df.columns if 'id' in str(c).lower()), None)
        if id_col is None:
            print(f"  No ID column found in {pf}; skipping.")
            continue

        grade_col = next((c for c in df.columns if c in grade_col_candidates), None)
        if grade_col is None:
            # try fuzzy: column name contains 'cij' or 'cijfer' or 'grade'
            grade_col = next((c for c in df.columns if any(k in str(c).lower() for k in ['cij', 'grade'])), None)

        # normalize IDs to string
        df_ids = df[[id_col]].dropna()
        df_ids[id_col] = df_ids[id_col].astype(str).str.strip()

        # merge to find intersection
        mask = df_ids[id_col].isin(base_ids)
        matched = df.loc[mask]
        if matched.empty:
            print(f"  No duplicates found in {pf}.")
            continue

        # collect results
        for _, row in matched.iterrows():
            id_val = str(row[id_col]).strip()
            grade_val = row[grade_col] if (grade_col in row and pd.notna(row[grade_col])) else None
            duplicates.append({"ID": id_val, "Cijfer": grade_val, "SourceFile": pf.name})

        print(f"  Found {len(matched)} duplicates in {pf}.")

    dup_df = pd.DataFrame(duplicates)
    return dup_df


def main():
    base = Path(__file__).parent / "inschrijvingslijst sociologie.xlsx"
    # match files like: ps (82).xls.xlsx
    ps_files = sorted(Path(__file__).parent.glob('ps *.xls.xlsx'))

    if not base.exists():
        print(f"Base file not found: {base}")
        return

    if not ps_files:
        print("No ps files found matching pattern 'ps (*.xls).xlsx'")
        return

    dup_df = find_duplicates(base, ps_files)

    if dup_df.empty:
        print("No duplicates found across provided files.")
    else:
        # print duplicates
        print("Duplicates found (ID - Cijfer - SourceFile):")
        for _, r in dup_df.iterrows():
            print(f"{r['ID']} - {r['Cijfer']} - {r['SourceFile']}")

        out_csv = Path(__file__).parent / 'duplicates_summary.csv'
        dup_df.to_csv(out_csv, index=False)
        print(f"Wrote summary to {out_csv}")


if __name__ == '__main__':
    main()