import argparse
import sys
from io import StringIO
from typing import Any, Dict, List, Optional, Tuple

import pandas as pd
import requests
from geopy.distance import geodesic
from geopy.geocoders import Nominatim
from geopy.location import Location
from tabulate import tabulate

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

SORT_KV = {
    "e10": "e10_price",
    "e5": "e5_price",
    "b7s": "diesel_price",
    "distance": "distance",
}

HEADERS = {
    "User-Agent": "Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10_6_4; en-US) AppleWebKit/533.45 (KHTML, like Gecko) Chrome/48.0.2094.221 Safari/602"
}


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", choices=SORT_KV.keys())
    return parser.parse_args()


def get_location(address: str) -> tuple[float, float]:
    geolocator = Nominatim(user_agent="FuelNearMe")
    result = geolocator.geocode(address)
    if not isinstance(result, Location):
        raise ValueError(f"Failed to get location from address: '{address}'")
    return (result.latitude, result.longitude)


def get_latest_data() -> tuple[pd.DataFrame, Optional[str]]:
    response = requests.get(ENDPOINT, headers=HEADERS, timeout=10)
    response.raise_for_status()
    return pd.read_csv(StringIO(response.text)), response.headers.get("Last-Modified")


def filter_df(
    dframe: pd.DataFrame, arguments: argparse.Namespace, loc: Tuple[float, float]
) -> List[Dict[str, Any]]:
    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)
                if not pd.isna(e5_price)
                else "N/A",
                "e10_price": round(e10_price / 100, 2)
                if not pd.isna(e10_price)
                else "N/A",
                "diesel_price": round(diesel_price / 100, 2)
                if not pd.isna(diesel_price)
                else "N/A",
            }
            near_stations.append(station_dict)
    return near_stations


def sort_stations(stations: list[dict], sort: str) -> list[dict]:
    sort_key = SORT_KV[sort]
    return sorted(stations, key=lambda d: d[sort_key] if d[sort_key] != "N/A" else 999)


def output_stations(stations: List[Dict[str, Any]]) -> None:
    if not stations:
        print("[*] No stations found.")
        return
    print(
        tabulate(
            stations,
            headers={
                "station_name": "Station Name",
                "distance": "Distance (miles)",
                "e5_price": "E5 (£/L)",
                "e10_price": "E10 (£/L)",
                "diesel_price": "B7S (£/L)",
            },
            floatfmt=".2f",
        )
    )


def main():
    args = parse_args()

    try:
        location = get_location(args.address)
    except ValueError as e:
        print(f"[*] {e}")
        sys.exit(1)
    df, last_modified = get_latest_data()

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

    df_filtered = filter_df(df, args, location)

    sorted_stations_list = sort_stations(df_filtered, args.sort)

    output_stations(sorted_stations_list)


if __name__ == "__main__":
    main()