import argparse
import sys
from io import StringIO
from textwrap import dedent

import pandas as pd
import requests
from colorama import Back, Fore, Style, just_fix_windows_console
from geopy.distance import geodesic
from geopy.geocoders import Nominatim

just_fix_windows_console()


ENDPOINT = "https://www.fuel-finder.service.gov.uk/internal/v1.0.2/csv/get-latest-fuel-prices-csv"


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument("-a", "--address", type=str, required=True)
    parser.add_argument("-r", "--radius", type=int, default=5)
    parser.add_argument("-s", "--sort", type=str, default="e10")
    return parser.parse_args()


def get_location(address):
    loc = Nominatim(user_agent="FuelNearMe")
    getLoc = loc.geocode(address)
    if not getLoc:
        print("[*] Failed to get location. Please check if the address is valid.")
        sys.exit(1)
    latitude = getLoc.latitude
    longitude = getLoc.longitude
    return (latitude, longitude)


def get_latest_data():
    response = requests.get(ENDPOINT)
    return pd.read_csv(StringIO(response.text)), response.headers.get("Last-Modified")


def process_data(dframe):
    price_cols = [c for c in dframe.columns if "fuel_price" in c]
    dframe[price_cols] = dframe[price_cols].fillna(0.0)
    return dframe.fillna("N/A")


def filter_df(dframe, arguments, loc):
    near_stations = []
    for station, latitude, longitude, e5_price, e10_price, diesel_price in zip(
        dframe["forecourts.trading_name"],
        dframe["forecourts.location.latitude"],
        dframe["forecourts.location.longitude"],
        dframe["forecourts.fuel_price.E5"],
        dframe["forecourts.fuel_price.E10"],
        dframe["forecourts.fuel_price.B7S"],
    ):
        distance_from_current_location = geodesic((latitude, longitude), loc).miles
        if distance_from_current_location < arguments.radius:
            station_dict = {
                "station_name": station,
                "distance": round(distance_from_current_location, 1),
                "e5_price": round(e5_price / 100, 2),
                "e10_price": round(e10_price / 100, 2),
                "diesel_price": round(diesel_price / 100, 2),
            }
            near_stations.append(station_dict)
    return near_stations


def sort_list_of_stations(stations_list, arguments):
    match arguments.sort:
        case "e10":
            sort_by = "e10_price"
            return sorted(stations_list, key=lambda d: d[sort_by])
        case "e5":
            sort_by = "e5_price"
            return sorted(stations_list, key=lambda d: d[sort_by])
        case "b7s":
            sort_by = "diesel_price"
            return sorted(stations_list, key=lambda d: d[sort_by])
        case "distance":
            sort_by = "distance"
            return sorted(stations_list, key=lambda d: d[sort_by])


def output_stations(stations):
    for number, row in enumerate(stations):
        output = dedent(f"""
        {number + 1}. {row["station_name"]}
        Distance: {row["distance"]} miles
        E5 Price: £{row["e5_price"]:.2f}/L
        E10 Price: £{row["e10_price"]:.2f}/L
        B7S (Standard Diesel) Price: £{row["diesel_price"]:.2f}/L""")
        print(output)


def main():
    args = parse_args()
    location = get_location(args.address)
    df, last_modified = get_latest_data()

    print(f"Last modified: {last_modified}")

    df_processed = process_data(df)

    print(f"\n{Fore.MAGENTA}Stations: " + Style.RESET_ALL + str(len(df_processed)))

    df_filtered = filter_df(df_processed, args, location)

    sorted_stations_list = sort_list_of_stations(df_filtered, args)

    output_stations(sorted_stations_list)


if __name__ == "__main__":
    main()