#!/usr/bin/env python3

from pathlib import Path
import argparse
import pandas as pd

BASE_DIR = Path(__file__).resolve().parents[1]
DATA_DIR = BASE_DIR / "data"
OUTPUT_DIR = BASE_DIR / "output"

def clean_value(v):
    if pd.isna(v):
        return ""
    return str(v).strip()

def bci_escape(v):
    v = clean_value(v)
    v = v.replace("\\", "\\\\")
    v = v.replace("'", "\\'")
    v = v.replace("\n", "\\n")
    return v

def build_column_dict(row, exclude_cols):
    data = {}
    for col in row.index:
        if col in exclude_cols:
            continue
        val = clean_value(row[col])
        if val != "":
            data[col] = val
    return data

def dict_to_bci_payload(d):
    parts = []
    for k, v in d.items():
        parts.append(f"'{bci_escape(k)}':'{bci_escape(v)}'")
    return "{" + ",".join(parts) + "}"

def main():
    parser = argparse.ArgumentParser(description="Generate Bluebeam BCI script from Markups CSV + update CSV.")
    parser.add_argument("--bb-csv", default=str(DATA_DIR / "bluebeam_markups.csv"), help="Bluebeam Markups List CSV export")
    parser.add_argument("--updates-csv", default=str(DATA_DIR / "bluebeam_updates.csv"), help="Your update CSV")
    parser.add_argument("--out", default=str(OUTPUT_DIR / "update_columns.bci"), help="Output BCI file")
    parser.add_argument("--pdf-path", default=r"C:\PATH\TO\TARGET.pdf", help="Windows path to target PDF for the BCI Open() command")
    parser.add_argument("--match-column", default="Comment", help="Column in Bluebeam CSV to match against match_value")
    parser.add_argument("--page-column", default="Page Index", help="Bluebeam page index column")
    parser.add_argument("--id-column", default="ID", help="Bluebeam markup ID column")
    parser.add_argument("--contains", action="store_true", help="Use contains match instead of exact match")
    args = parser.parse_args()

    bb_csv = Path(args.bb_csv)
    updates_csv = Path(args.updates_csv)
    out_path = Path(args.out)

    OUTPUT_DIR.mkdir(exist_ok=True)

    bb = pd.read_csv(bb_csv, dtype=str).fillna("")
    upd = pd.read_csv(updates_csv, dtype=str).fillna("")

    required_bb = {args.page_column, args.id_column, args.match_column}
    missing_bb = required_bb - set(bb.columns)
    if missing_bb:
        raise ValueError(f"Bluebeam CSV missing required columns: {sorted(missing_bb)}")

    required_upd = {"match_value"}
    missing_upd = required_upd - set(upd.columns)
    if missing_upd:
        raise ValueError(f"Updates CSV missing required columns: {sorted(missing_upd)}")

    lines = []
    lines.append(f"Open('{args.pdf_path}', '')")

    total_matches = 0

    for _, urow in upd.iterrows():
        match_value = clean_value(urow["match_value"])
        if not match_value:
            continue

        if args.contains:
            mask = bb[args.match_column].astype(str).str.contains(match_value, na=False, regex=False)
        else:
            mask = bb[args.match_column].astype(str).str.strip() == match_value

        matches = bb[mask]

        col_data = build_column_dict(urow, exclude_cols={"match_value"})
        if not col_data:
            continue

        payload = dict_to_bci_payload(col_data)

        for _, brow in matches.iterrows():
            page_index = clean_value(brow[args.page_column])
            markup_id = clean_value(brow[args.id_column])

            if page_index == "" or markup_id == "":
                continue

            lines.append(f'ColumnDataSet({page_index},"{markup_id}","{payload}")')
            total_matches += 1

    lines.append("Save()")
    lines.append("Close()")

    out_path.write_text("\n".join(lines) + "\n", encoding="utf-8")

    print(f"WROTE: {out_path}")
    print(f"MATCHED MARKUPS: {total_matches}")

if __name__ == "__main__":
    main()