import polars as pl
import plotly.express as px
import pandas as pd
import logging
# import datetime


class data_components:
    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")

    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")
    )

    def __init__(self):
        logging.info("Initializing data components")
        pass

    def update_dates(start_date, end_date):
        logging.info("Updating dates")
        logging.debug(f"start_date: {start_date}, end_date: {end_date}")
        logging.debug(data_components.master_transactions.columns)
        try:
            master_data = data_components.master_transactions.filter(
                pl.col("date").is_between(start_date, end_date)
            )
        except Exception as e:
            logging.error(f"Error updating dates: {e}")
            raise e
        return master_data

    def update_data(master_data, callback=0):
        # 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"
        )

        total_spend_line = f"### £{total_spend:,.2f}"

        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_line,
        }

        if callback == 0:
            return data
        else:
            return (
                spend_per_day_line,
                spend_per_category_bar,
                spend_per_payee_bar,
                total_spend,
            )