RvtExporter.exe <input_path> [export_mode] [options]

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import subprocess
import pandas as pd
from pathlib import Path
from typing import List, Optional

class RevitExporter:
    def __init__(self, exporter_path: str = "RvtExporter.exe"):
        self.exporter = Path(exporter_path)
        if not self.exporter.exists():
            raise FileNotFoundError(f"RvtExporter not found: {exporter_path}")

    def convert(self, rvt_file: str, mode: str = "complete",
                options: List[str] = None) -> Path:
        """Convert Revit file to Excel."""
        rvt_path = Path(rvt_file)
        if not rvt_path.exists():
            raise FileNotFoundError(f"Revit file not found: {rvt_file}")

        cmd = [str(self.exporter), str(rvt_path), mode]
        if options:
            cmd.extend(options)

        result = subprocess.run(cmd, capture_output=True, text=True)

        if result.returncode != 0:
            raise RuntimeError(f"Export failed: {result.stderr}")

        # Output file is same name with .xlsx extension
        output_file = rvt_path.with_suffix('.xlsx')
        return output_file

    def batch_convert(self, folder: str, mode: str = "standard",
                      pattern: str = "*.rvt") -> List[Path]:
        """Convert all Revit files in folder."""
        folder_path = Path(folder)
        converted = []

        for rvt_file in folder_path.glob(pattern):
            try:
                output = self.convert(str(rvt_file), mode)
                converted.append(output)
                print(f"Converted: {rvt_file.name}")
            except Exception as e:
                print(f"Failed: {rvt_file.name} - {e}")

        return converted

    def read_elements(self, xlsx_file: str) -> pd.DataFrame:
        """Read converted Excel as DataFrame."""
        return pd.read_excel(xlsx_file, sheet_name="Elements")

    def get_quantities(self, xlsx_file: str,
                       group_by: str = "Category") -> pd.DataFrame:
        """Get quantity summary grouped by category."""
        df = self.read_elements(xlsx_file)

        # Group and count
        summary = df.groupby(group_by).agg({
            'ElementId': 'count',
            'Area': 'sum',
            'Volume': 'sum'
        }).reset_index()

        summary.columns = [group_by, 'Count', 'Total_Area', 'Total_Volume']
        return summary

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