import polars as pl
import plotly.express as px
import pandas as pd
import logging
import sys
import config.exit_codes as ec
import datetime

try:
    accounts = pl.read_parquet('data/warehouse/accounts.parquet')
    categories = pl.read_parquet('data/warehouse/categories.parquet')
    dates = pl.read_parquet('data/warehouse/dates.parquet')
    payees = pl.read_parquet('data/warehouse/payees.parquet')
    scheduled_transactions = pl.read_parquet('data/warehouse/scheduled_transactions.parquet')
    transactions = pl.read_parquet('data/warehouse/transactions.parquet')
except FileNotFoundError:
    logging.error('Data warehouse files not found. Run the data pipeline to create them.')
    sys.exit(ec.MISSING_DATA_FILES)

try:
    # Join transactions with accounts, categories, and payees to create a master DataFrame
    master_transactions = transactions.join(categories, left_on='category_id', right_on='category_id', suffix='_category')\
                        .join(accounts, left_on='account_id', right_on='account_id', suffix='_account')\
                        .join(payees, left_on='payee_id', right_on='payee_id', suffix='_payee')\
                        .join(dates, left_on='transaction_date', right_on='date_id', suffix='_date')

except Exception as e:
    logging.error(f'Error joining DataFrames: {e}')
    sys.exit(ec.BAD_JOIN)

def update_dates(start_date, end_date):
    master_data = master_transactions.filter(
        (pl.col('year') >= start_date.year ) & (pl.col('year') <= end_date.year) & 
        (pl.col('month') >= start_date.month ) & (pl.col('month') <= end_date.month) &
        (pl.col('day') >= start_date.day ) & (pl.col('day') <= end_date.day)
    )
    return master_data

def update_data(master_data):
    # Create aggregations
    spend_per_day = master_data.sql('''
        SELECT 
            date,
            year,
            month,
            day,
            ABS(SUM(transaction_amount)) as total
        FROM self
        WHERE category_name != 'Inflow: Ready to Assign'
        GROUP BY date, year, month, day
        ORDER BY date DESC
        '''
    )

    spend_per_category = master_data.sql('''
        SELECT
            category_name,
            ABS(SUM(transaction_amount)) as total
        FROM self
        WHERE category_name != 'Inflow: Ready to Assign'
        GROUP BY category_name
        ORDER BY total DESC
        '''
    )

    spend_per_payee = master_data.sql('''
        SELECT
            payee_name,
            ABS(SUM(transaction_amount)) as total
        FROM self
        WHERE payee_name != 'Starting Balance'
            AND transaction_amount < 0
        GROUP BY payee_name
        ORDER BY total DESC
        '''
    )
    total_spend = master_data.sql('''
        SELECT ABS(SUM(transaction_amount)) AS total
        FROM self
        WHERE payee_name != 'Starting Balance'
            AND transaction_amount < 0
    ''').item()


    # Convert DataFrame to list of dictionaries
    spend_per_day_data = spend_per_day.to_dicts()
    spend_per_category_data = spend_per_category.to_dicts()
    spend_per_payee_data = spend_per_payee.to_dicts()


    # Convert list of dictionaries to Pandas DataFrame
    spend_per_day_df = pd.DataFrame(spend_per_day_data)
    spend_per_category_df = pd.DataFrame(spend_per_category_data)
    spend_per_payee_df = pd.DataFrame(spend_per_payee_data)


    spend_per_day_line = px.line(spend_per_day_df, x="date", y="total")
    spend_per_day_line.update_layout(
        plot_bgcolor='black',
        paper_bgcolor='black',
        font_color='white'
    )

    spend_per_category_bar = px.bar(spend_per_category_df, x="category_name", y="total")
    spend_per_category_bar.update_layout(
        plot_bgcolor='black',
        paper_bgcolor='black',
        font_color='white'
    )

    spend_per_payee_bar = px.bar(spend_per_payee_df, x="payee_name", y="total")
    spend_per_payee_bar.update_layout(
        plot_bgcolor='black',
        paper_bgcolor='black',
        font_color='white'
    )

    data = {"spend_per_day_line": spend_per_day_line,
            "spend_per_category_bar": spend_per_category_bar,
            "spend_per_payee_bar": spend_per_payee_bar,
            "total_spend": total_spend}
    print(data)
    return data