import pandas as pd
import json
import os


from s3Ops import read_file_from_s3

from backendAPIs import (
    update_onboarding_status,
    add_bank_transaction_records
)

def xlsx_to_df(xlsx_file, sheet_name):
    df = pd.read_excel(xlsx_file, sheet_name = sheet_name)
    df.columns = df.iloc[2]
    df = df.drop(df.index[:3])
    df = df.reset_index(drop=True)

    return df


# TODO: add the remaining asset type mapping
def get_security_type(asset_type):
    asset_to_security_type = {
        "Common stock":"COMMON",
        "Preferred stock":"PREFERRED",
        "Warrants":"WARRANTS",
        "Convertible promissory note/SAFEs": "POST MONEY SAFE"
    }

    for asset, security_type in asset_to_security_type.items():
        if asset_type.lower() in asset.lower():  # Case-insensitive match
            return security_type
        
    return ""


def process_bank_transaction_records(df):
    """
    Process and insert users from an Excel file into MongoDB.

    Args:
        file_path (str): Path to the Excel file.
    """

    entity_id = os.getenv('ENTITY_ID')
    fund_id = os.getenv('FUND_ID')

    # Filter out rows with invalid Journal IDs
    # df = df[~df['Journal ID'].isin(['Journal ID'])]
    print(df.head())

    transactions = []

    # Iterate through the DataFrame and prepare partner records
    for index, row in df.iterrows():

        tdate = row['Date']
        transaction_record = { 
                        "fundId": fund_id,
                        "date": tdate.strftime('%m/%d/%Y'),
                        "memo": row['Name'] if pd.notna(row['Name']) else row['Memo'],
                        "credit": row['Credit'] if pd.notna(row['Credit']) else 0.0,
                        "debit": row['Debit'] if pd.notna(row['Debit']) else 0.0,
                        "balance": row['Balance'] if pd.notna(row['Balance']) else 0.0
                    }
                
        response = add_bank_transaction_records(transaction_record)
        print(f"Adding transaction {index}:\n {transaction_record}\n\n")
        if 'error' in response:
            print(f"Failed to fetch onboarding status: Error: {response['error']}")
            print(f"Status Code: {response['status_code']}")
            break # Process next company investment record...
        transactions.append(transaction_record)

        # Test with smaller set of records
        # if index == 1:
        #     break

    return transactions


def process_bank_transactions(file_path):
    # Process bank transactions
    step_number = 5
    step_number -= 1
    success_message = ""
    error_message = ""
    status = ""

    bucket_name = os.getenv('S3_UPLOAD_BUCKET_NAME')
    print(f"Processing file: {file_path}")
    response = update_onboarding_status(step_number, "IN-PROGRESS", "", "")
    if 'error' in response:
        print(f"Failed to fetch onboarding status: Error: {response['error']}")
        print(f"Status Code: {response['status_code']}")
        return False
    journal_excel_file = read_file_from_s3(bucket_name, file_path)
    sheet_name = 'Bank Transactions Report'
    transaction_df = xlsx_to_df(journal_excel_file, sheet_name)
    print(transaction_df.head())
    transactions = process_bank_transaction_records(transaction_df)
    if transactions:
        item_count = len(transactions)
        success_message = f"Onboarded {item_count} bank transactions."
        status =  "COMPLETE"
    else:
        error_message = "No transactions were onboaerded."
        status = "FAILED"

    response = update_onboarding_status(step_number, status, error_message, success_message)
    if 'error' in response:
        print(f"Failed to fetch onboarding status: Error: {response['error']}")
        print(f"Status Code: {response['status_code']}")
        return False

    # print(f"Journals list:\n {transactions}")

    return True


if __name__ == "__main__":

    from dotenv import load_dotenv
    load_dotenv()

    onboardingId = "66ea163564e2f97a059160ef"
    os.environ["ONBOARDING_ID"] = onboardingId
    os.environ["FUND_ID"] = "66c5e6d89ecbf552a05b84fc"

    from initOnboarding import initialize_onboarding
    initialize_onboarding()

    found_files = {'lpa': '66c5e5c99ecbf552a05b84f9/20240917T235220Z/0-Please_DocuSign_CerraCap_II_LP_Limited_Partn.pdf', 'partner': '66c5e5c99ecbf552a05b84f9/20240917T235220Z/0-1-cerracap-ii-lp_2024-07_09_short_partner.xlsx', 'financials': '66c5e5c99ecbf552a05b84f9/20240917T235220Z/2-3-cerracap-ii-lp_2024-08-26_financials.xlsx', 'bankTransactions': '66c5e5c99ecbf552a05b84f9/20240917T235220Z/4-CerraCap_II__LP_bank_transactions_2016-01-01-2024-07-03.xlsx', 'journals': '66c5e5c99ecbf552a05b84f9/20240917T235220Z/5-cerracap-ii-lp_2024-07-09_journals-export.xlsx', 'fund_performance': '66c5e5c99ecbf552a05b84f9/20240917T235220Z/6-cerracap-ii-lp_2024-07-09_fund-performance-report.xlsx'}
    financials_excel_file_path = found_files["bankTransactions"]
    process_bank_transactions(financials_excel_file_path)