from typing import Union, List, Dict

import pandas as pd

from app.config import logger
from app.models import ConversionResponse
from app.convert.utils.content_from_file import content_from_file
from app.convert.base_converter import BaseConverter


class ExportTxtConverter(BaseConverter):
    def read_file(self) -> None:
        txt_file = content_from_file(self.content)
        # Assuming the txt file is structured in a way that can be read into a DataFrame
        # You might need to adjust this depending on the actual structure of your export txt files
        self.df = pd.read_csv(txt_file,
                              sep='\t')  # or another appropriate method to read the txt file

    def add_metadata(self) -> None:
        self.df = self.df.assign(index="export_txt", type="export",
                                 network="Generic")

    def convert_columns(self) -> None:
        # Implement specific column conversions for export txt files
        # For example, converting timestamps, etc.
        pass

    def rename_columns(self) -> None:
        # Implement column renaming for export txt files
        # Map the original column names to the standardized names
        column_mapping = {
            # Add your column mappings here
            # "original_column_name": "standardized_column_name",
        }
        self.df = self.df.rename(columns=column_mapping)

    def clean_data(self) -> None:
        # Add any export txt-specific data cleaning
        # For example, removing any specific formatting, etc.
        pass


def convert_export_txt(content: Union[str, bytes]) -> List[Dict]:
    """
    Convert export txt content to a standardized format.

    Args:
        content (Union[str, bytes]): The txt content of the export.

    Returns:
        ConversionResponse: An object containing the converted data, status, and metadata.
    """
    logger.info(f"Starting conversion of {len(content)} bytes")
    converter = ExportTxtConverter(content)

    return converter.convert()
