import pandas as pd
import re

import postprocess_funcs
import config


def postprocess_results(combined_df):

    # Define valid values dictionary
    valid_values_dict = {
        "CONTRACT_LOB": config.VALID_LOBS,
        "CONTRACT_PROGRAM": config.VALID_PROGRAMS,
        "CONTRACT_NETWORK": config.VALID_NETWORKS,
    }

    # Sanitize the combined data
    try:
        df = postprocess_funcs.sanitize_combined(combined_df)
    except Exception as e:
        print(f"Postprocessing Error - santize_combined : {e}")

    # Clean specific columns for exact matches
    for field_list in [
        ["CONTRACT_LOB", "Corrected_LOB", config.VALID_LOBS],
        ["CONTRACT_PROGRAM", "Corrected_PROGRAM", config.VALID_PROGRAMS],
        ["CONTRACT_NETWORK", "Corrected_NETWORK", config.VALID_NETWORKS],
    ]:
        try:
            postprocess_funcs.clean_columns_combined(df, field_list[0], field_list[2], field_list[1])
        except Exception as e:
            print(f"Postprocessing Error - clean_columns_combined : {e}")

        try:
            postprocess_funcs.clean_columns_combined_fuzzy(df, field_list[0], field_list[2], config.FUZZY_MATCH_THRESHOLD)
        except Exception as e:
            print(f"Postprocessing Error - clean_columns_combined_fuzzy : {e}")

    # Correct misplaced values across columns
    try:
        df = postprocess_funcs.correct_misplaced_values(df, ['CONTRACT_LOB', 'CONTRACT_PROGRAM', 'CONTRACT_NETWORK'], valid_values_dict)
    except Exception as e:
        print(f"Postprocessing Error - correct_misplaced_values : {e}")

    # Move percentages and large numbers to correct columns
    try:
        df = postprocess_funcs.move_percentage_to_rate(df)
    except Exception as e:
        print(f"Postprocessing Error - move_percentage_to_rate : {e}")

    # Replace N/As in "Not Covered" with 0%
    try:
        df = postprocess_funcs.set_rate_to_zero_if_not_covered(df)
    except Exception as e:
        print(f"Postprocessing Error - set_rate_to_zero_if_not_covered : {e}")

    # Adjust for adding or subtracting
    try:
        df = postprocess_funcs.adjust_reimbursement_rate(df)
    except Exception as e:
        print(f"Postprocessing Error - adjust_reimbursement_rate : {e}")

    # -- Deprecated --
    # Clean dates so that Termination and Effective aren't the same
    # try:
    #     df = postprocessing_funcs.clean_dates(df)
    # except Exception as e:
    #     print(f"Postprocessing Error - clean_dates : {e}")

    # Re order columns
    column_order = [col for col in config.VALID_COLUMNS if col in df.columns] + [
        col for col in df.columns if col not in config.VALID_COLUMNS
    ]
    df = df[column_order]

    return df
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    # Order columns
    cols = [col for col in config.VALID_COLUMNS if col in combined_df.columns] + [col for col in combined_df.columns if col not in config.VALID_COLUMNS]
    #combined_df = combined_df.loc[:, cols]
    combined_df = combined_df[cols]
    return combined_df