import io
import logging
import simplejson as json
import pandas as pd
import mappings
import ghostfolio_service
import datetime
from correpy.domain.enums import TransactionType
from correpy.parsers.brokerage_notes.parser_factory import ParserFactory
from fastapi import UploadFile

account_id = '1590f1bb-b3ee-4dc8-ac46-bd1f15a83e87'

logger = logging.getLogger(__name__)


def read_brokerage_pdf(file: UploadFile):
    content = io.BytesIO(file.file.read())
    content.seek(0)

    logger.info(f'Processing {file.filename} ')

    try:
        brokerage_notes = ParserFactory(brokerage_note=content, password='052').parse()
        return convert_to_ghostfolio(brokerage_notes, file.filename)
    except Exception as e:
        print(f'Failed to parse {file.filename}: {e}')
        logger.error(f'Failed to parse {file.filename}: {e}')
        return 500


def convert_to_ghostfolio(brokerage_list: list, filename: str):
    activities = {}

    for brokerage_note in brokerage_list:
        reference_date = brokerage_note.reference_date.isoformat() + 'T00:00:00.000Z'
        fees = (brokerage_note.settlement_fee + brokerage_note.emoluments + brokerage_note.others) / len(
            brokerage_note.transactions)

        # activities['meta'] = {'date': datetime.datetime.now().isoformat(), 'version': 'v0'}
        activities['activities'] = []
        # activities['updateCashBalance'] = 'false'

        for transaction in brokerage_note.transactions:

            # activity['fee'] = (brokerage_note.settlement_fee + brokerage_note.emoluments)

            transaction_type: str = ''

            if transaction.transaction_type == TransactionType.BUY:
                transaction_type = 'BUY'
            elif transaction.transaction_type == TransactionType.SELL:
                transaction_type = 'SELL'

            activity = {'accountId': account_id, 'comment': filename, 'date': reference_date,
                        'type': transaction_type,
                        'fee': fees, 'unitPrice': transaction.unit_price, 'quantity': transaction.amount,
                        'symbol': mappings.name_to_tickers[transaction.security.name.strip()].strip(),
                        'currency': 'BRL',
                        'dataSource': 'YAHOO'}
            activities['activities'].append(activity)

            if len(activity['symbol']) < 1:
                logger.info(f'No symbol for {transaction.security.name}')

    result = ghostfolio_service.import_activities(activities, filename)

    return result


def read_dividends_xlsx(file: UploadFile):
    dividends_xlsx = file.file
    df = pd.read_excel(dividends_xlsx, engine='openpyxl')
    path = f'./{file.filename}'
    df.to_csv(path, encoding='utf-8')
    df = pd.read_csv(path, encoding='utf-8')
    df = df[df['Produto'].notnull()]
    df = df[df['Tipo de Evento'] != 'PAGAMENTO DE JUROS']

    return dividends_df_to_ghostfolio(df, file.filename)


def dividends_df_to_ghostfolio(dividends_df: pd.DataFrame, filename: str):
    activities = {"activities": []}

    for index, row in dividends_df.iterrows():
        payment_date = datetime.datetime.strptime(row['Pagamento'], "%d/%m/%Y").isoformat()
        name = row['Produto'].split('-')[0].strip() + '.SA'

        if name in mappings.ticker_changes:
            symbol = mappings.ticker_changes[name]
        else:
            symbol = name

        activity = {
            "accountId": account_id,
            "date": payment_date,
            "comment": filename,
            "currency": "BRL",
            "dataSource": "YAHOO",
            "quantity": row['Quantidade'],
            "type": "DIVIDEND",
            "symbol": symbol,
            "unitPrice": row['Preço unitário'],
            "fee": 0
        }

        activities["activities"].append(activity)

    print(activities)

    return ghostfolio_service.import_activities(activities, filename)