import pandas as pd

import os


def read_and_compare_csvs(csv_file1, csv_file2):
    # Read CSV files into pandas DataFrames
    df1 = pd.read_csv(csv_file1)
    df2 = pd.read_csv(csv_file2)

    df2 = df2[df1.columns]

    # Check for any differences
    diff = (df1 != df2) & df1.notnull() & df2.notnull()

    # Create a writer for Excel
    writer = pd.ExcelWriter("data/differences.xlsx", engine="xlsxwriter")
    df2.to_excel(writer, sheet_name="Differences", index=False)

    # Get the xlsxwriter workbook and worksheet objects
    workbook = writer.book
    worksheet = writer.sheets["Differences"]

    # Define a format for changed cells
    highlight_fmt = workbook.add_format({"font_color": "red", "bg_color": "yellow"})

    # Apply formatting to the changed cells
    for col in df1.columns:
        col_idx = (
            df1.columns.get_loc(col) + 1
        )  # Excel index starts from 1, and there is an index column
        for row in diff.index[diff[col]]:
            value = df2.at[row, col]
            if pd.isna(value):
                value = (
                    "NaN"  # Change this if you want a different representation for NaN
                )
            worksheet.write(row + 1, col_idx, value, highlight_fmt)

    # Close the Pandas Excel writer and output the Excel file
    writer.close()


# Example usage
base_path = "output/57-2883440-Cordia Anderson-Hopkins LCSW-ICMProviderAgreement_221673"
read_and_compare_csvs(
    os.path.join(base_path, "combined_results_post_processed.csv"),
    os.path.join(base_path, "test_3.5.csv"),
)