import pandas as pd
from bs4 import BeautifulSoup
# from src.transform_lambda import read_from_s3_subfolder_to_df, tables
# from src.extract_lambda import extract_bucket
# import json
# import boto3
# import re
# from datetime import datetime as dt
import requests

# Table names:
# fact_sales_order
# fact_purchase_orders
# fact_payment
# dim_transaction
# dim_staff
# dim_payment_type
# dim_location
# dim_design
# dim_date
# dim_currency
# dim_counterparty


def create_dim_transaction(dict_of_df):
    pass


def create_fact_sales_order(dict_of_df):
    df_sales = dict_of_df["sales_order"]
    df_sales.index.name = "sales_record_id"
    df_sales["created_date"] = pd.to_datetime(df_sales["created_at"]).dt.date
    df_sales["created_time"] = pd.to_datetime(df_sales["created_at"]).dt.time
    df_sales["last_updated_date"] = pd.to_datetime(df_sales["last_updated"]).dt.date
    df_sales["last_updated_time"] = pd.to_datetime(df_sales["last_updated"]).dt.time
    pd.merge(dict_of_df["staff"], df_sales["sales_staff_id"], on="staff_id", how="left")
    # df_sales.rename(columns={"staff_id": "sales_staff_id"})
    fact_sales_order = df_sales.loc[
        :,
        [
            "sales_record_id",
            "sales_order_id",
            "created_date",
            "created_time",
            "last_updated_date",
            "last_updated_time",
            "sales_staff_id",
            "counterparty_id",
            "units_sold",
            "unit_price",
            "currency_id",
            "design_id",
            "agreed_payment_date",
            "agreed_delivery_date",
            "agreed_delivery_location_id",
        ],
    ]
    return fact_sales_order


# fact_purchase_order from purchase_order


def create_fact_purchase_orders(dict_of_df):
    df_po = dict_of_df["purchase_order"]
    df_po.index.name = "purchase_record_id"
    df_po["created_date"] = df_po["created_at"].date()
    df_po["created_time"] = df_po["created_at"].dt.time
    df_po["last_updated_date"] = df_po["last_updated_at"].date()
    df_po["last_updated_time"] = df_po["last_updated_at"].dt.time
    df_po["agreed_delivery_date"] = pd.to_datetime(
        df_po["agreed_delivery_date"], format="%Y-%m-%d"
    )
    df_po["agreed_payment_date"] = pd.to_datetime(
        df_po["agreed_payment_date"], format="%Y-%m-%d"
    )
    df_po.drop(labels=["created_at", "last_updated_at"], axis=1, inplace=True)
    return df_po


def create_fact_payment(dict_of_df):
    df_payment = dict_of_df["payment"]
    df_payment.index.name = "payment_record_id"
    df_payment["created_date"] = pd.to_datetime(df_payment["created_at"]).dt.date
    df_payment["created_time"] = pd.to_datetime(df_payment["created_at"]).dt.time
    df_payment["last_updated_date"] = pd.to_datetime(df_payment["last_updated"]).dt.date
    df_payment["last_updated_time"] = pd.to_datetime(df_payment["last_updated"]).dt.time
    fact_payment = df_payment.loc[
        :,
        [
            "payment_record_id",
            "payment_id",
            "created_date",
            "created_time",
            "last_updated_date",
            "last_updated_time",
            "transaction_id",
            "counterparty_id",
            "payment_amount",
            "currency_id",
            "payment_type_id",
            "paid",
            "payment_date",
        ],
    ]
    return fact_payment


# dim_location from address --> drops 2 columns


def create_dim_location(dict_of_df):
    df_loc = (
        dict_of_df["address"]
        .drop(labels=["created_at", "last_updated"], axis=1)
        .rename(columns={"address_id": "location_id"})
        .set_index("location_id")
    )
    return df_loc


# dim_counterparty from address and counterparty


def create_dim_counterparty(dict_of_df):
    df_prefixed_address = dict_of_df["address"].add_prefix(
        "counterparty_legal_", axis=1
    )
    df_cp = pd.merge(
        dict_of_df["counterparty"],
        df_prefixed_address,
        left_on="legal_address_id",
        right_on="address_id",
        how="outer",
    ).set_index("counterparty_id")
    return df_cp


# dim_date from purchase_order
def create_dim_date(dict_of_df):
    sr_date = pd.concat(
        [
            dict_of_df["created_date"],
            dict_of_df["last_updated_date"],
            dict_of_df["agreed_delivery_date"],
            dict_of_df["agreed_payment_date"],
        ]
    ).sort()
    df_date = pd.DataFrame(sr_date, columns="date_id")
    df_date["year"] = df_date["date_id"].dt.year
    df_date["month"] = df_date["date_id"].dt.month
    df_date["day"] = df_date["date_id"].dt.day
    df_date["day_of_week"] = df_date["date_id"].dt.dayofweek
    df_date["day_name"] = df_date["date_id"].dt.day_name
    df_date["month_name"] = df_date["date_id"].dt.month_name
    df_date["quarter"] = df_date["date_id"].dt.quarter
    df_date.set_index("date_id")


def scrape_currency_names():
    response = requests.get("https://www.xe.com/currency/").content
    soup = BeautifulSoup(response, "html.parser")
    currency = [
        item.text for item in soup.findAll("a", attrs={"class": "sc-299dec64-6 fZPTSw"})
    ]
    sr = pd.Series(currency)
    df_cur = sr.str.split(pat=" - ", expand=True).rename(
        {0: "currency_code", 1: "currency_name"}, axis=1
    )
    return df_cur


def create_dim_currency(dict_of_df, names=scrape_currency_names()):
    df_cur = dict_of_df["currency"].drop(labels=["created_at", "last_updated"], axis=1)
    dim_cur = pd.merge(
        df_cur, names, left_on="currency_code", right_on="currency_code", how="inner"
    ).set_index("currency_id")
    return dim_cur


def create_dim_payment_type(dict_of_df):
    df_payment_type = dict_of_df["payment_type"]
    dim_payment_type = df_payment_type.loc[:, ["payment_type_id", "payment_type_name"]]
    return dim_payment_type


def create_fact_payment(dict_of_df):
    df_payment = dict_of_df["payment"]
    df_payment.index.name = "payment_record_id"
    df_payment["created_date"] = pd.to_datetime(df_payment["created_at"]).dt.date
    df_payment["created_time"] = pd.to_datetime(df_payment["created_at"]).dt.time
    df_payment["last_updated_date"] = pd.to_datetime(df_payment["last_updated"]).dt.date
    df_payment["last_updated_time"] = pd.to_datetime(df_payment["last_updated"]).dt.time
    fact_payment = df_payment.loc[
        :,
        [
            "payment_record_id",
            "payment_id",
            "created_date",
            "created_time",
            "last_updated_date",
            "last_updated_time",
            "transaction_id",
            "counterparty_id",
            "payment_amount",
            "currency_id",
            "payment_type_id",
            "paid",
            "payment_date",
        ],
    ]
    return fact_payment


def create_dim_design(dict_of_df):
    df_design = dict_of_df["design"]
    dim_design = df_design.loc[
        :, ["design_id", "design_name", "file_name", "file_location"]
    ]
    return dim_design


def create_dim_staff(dict_of_df):
    staff_department = pd.merge(
        dict_of_df["staff"], dict_of_df["department"], on="department_id", how="left"
    )
    dim_staff = staff_department.loc[
        :,
        [
            "staff_id",
            "first_name",
            "last_name",
            "department_name",
            "location",
            "email_address",
        ],
    ]
    return dim_staff


def create_dim_currency(dict_of_df):
    df_currency = dict_of_df["currency"]
    dim_currency = df_currency.loc[:, ["currency_id", "currency_code"]]
    mappings = {"GBP": "Pound", "USD": "US Dollar", "EUR": "Euro"}
    dim_currency["currency_name"] = dim_currency["currency_code"].map(mappings)
    return dim_currency


def create_dim_date(dict_of_df):
    df_sales = dict_of_df["sales"]
    df_sales = df_sales.loc[:, ["agreed_delivery_date"]]
    df_sales["agreed_delivery_date"] = pd.to_datetime["agreed_delivery_date"]
    df_sales["year"] = df_sales["agreed_delivery_date"].dt.year
    df_sales["month"] = df_sales["agreed_delivery_date"].dt.month
    df_sales["day"] = df_sales["agreed_delivery_date"].dt.day
    df_sales["day_of_week"] = df_sales["agreed_delivery_date"].dt.dayofweek
    df_sales["day_name"] = df_sales["agreed_delivery_date"].dt.day_name()
    df_sales["month_name"] = df_sales["agreed_delivery_date"].dt.month_name()
    df_sales["quarter"] = df_sales["agreed_delivery_date"].dt.quarter()
    dim_date = [
        "date_id",
        "year",
        "month",
        "day",
        "day_of_week",
        "day_name",
        "month_name",
        "quarter",
    ]  # series.dt.quarter()
    return dim_date


# TO DO:
# complete dim_date from merged fact table
# merge dataframes into one dataframe
# remove duplicates
# test dim_date and fact_sales_order


def create_sales_star_schema(dict_of_df):
    dim_design = create_dim_design(dict_of_df)
    dim_staff = create_dim_staff(dict_of_df)
    dim_currency = create_dim_currency(dict_of_df)
    dim_date = create_dim_date(dict_of_df)

    fact_sales_order = create_fact_sales_order(dict_of_df)

    fact_sales_order = fact_sales_order.merge(dim_design, on="design_id", how="left")
    fact_sales_order = fact_sales_order.merge(
        dim_staff, left_on="sales_staff_id", right_on="staff_id", how="left"
    )
    fact_sales_order = fact_sales_order.merge(
        dim_currency, on="currency_id", how="left"
    )
    fact_sales_order = fact_sales_order.merge(
        dim_date, left_on="agreed_delivery_date", right_on="date_id", how="left"
    )

    return fact_sales_order


def create_dim_payment_type(dict_of_df):
    df_payment_type = dict_of_df["payment_type"]
    dim_payment_type = df_payment_type.loc[:, ["payment_type_id", "payment_type_name"]]
    return dim_payment_type