import openmeteo_requests

import pandas as pd
import requests_cache
from retry_requests import retry
import geocoder


class HappyMeteo:
    def __init__(self):
        # Setup the Open-Meteo API client with cache and retry on error
        cache_session = requests_cache.CachedSession('.cache', expire_after = 3600)
        retry_session = retry(cache_session, retries = 5, backoff_factor = 0.2)
        self.openmeteo = openmeteo_requests.Client(session = retry_session)

    def get_current_location(self):
        """Get current location using IP geolocation"""
        try:
            g = geocoder.ip('me')
            if g.ok:
                latitude = g.latlng[0]
                longitude = g.latlng[1]
                print(f"Latitude: {latitude}")
                print(f"Longitude: {longitude}")
                print(f"Address: {g.address}")
                return latitude, longitude
            else:
                print("Could not determine location")
                return None, None
        except Exception as e:
            print(f"Error getting location: {e}")
            return None, None
        
    def openMeteoCall(self, timeLapse):
        lat, lon = self.get_current_location()
        
        # Make sure all required weather variables are listed here
        # The order of variables in hourly or daily is important to assign them correctly below
        url = "https://api.open-meteo.com/v1/forecast"
        params = {
            "latitude": lat,
            "longitude": lon,
            "daily": ["weather_code", "temperature_2m_mean", "rain_sum", "showers_sum", "precipitation_sum", "daylight_duration", "relative_humidity_2m_mean"],
            "timezone": "auto",
            "forecast_days": timeLapse
        }
        responses = self.openmeteo.weather_api(url, params=params)

        # Process first location. Add a for-loop for multiple locations or weather models
        response = responses[0]
        print(f"Coordinates: {response.Latitude()}°N {response.Longitude()}°E")
        print(f"Elevation: {response.Elevation()} m asl")
        print(f"Timezone: {response.Timezone()}{response.TimezoneAbbreviation()}")
        print(f"Timezone difference to GMT+0: {response.UtcOffsetSeconds()}s")

        # Process daily data. The order of variables needs to be the same as requested.
        daily = response.Daily()
        daily_weather_code = daily.Variables(0).ValuesAsNumpy()
        daily_temperature_2m_mean = daily.Variables(1).ValuesAsNumpy()
        daily_rain_sum = daily.Variables(2).ValuesAsNumpy()
        daily_showers_sum = daily.Variables(3).ValuesAsNumpy()
        daily_precipitation_sum = daily.Variables(4).ValuesAsNumpy()
        daily_daylight_duration = daily.Variables(5).ValuesAsNumpy()
        daily_relative_humidity_2m_mean = daily.Variables(6).ValuesAsNumpy()

        daily_data = {"date": pd.date_range(
            start = pd.to_datetime(daily.Time(), unit = "s", utc = True),
            end = pd.to_datetime(daily.TimeEnd(), unit = "s", utc = True),
            freq = pd.Timedelta(seconds = daily.Interval()),
            inclusive = "left"
        )}

        daily_data["weather_code"] = daily_weather_code
        daily_data["temperature_2m_mean"] = daily_temperature_2m_mean
        daily_data["rain_sum"] = daily_rain_sum
        daily_data["showers_sum"] = daily_showers_sum
        daily_data["precipitation_sum"] = daily_precipitation_sum
        daily_data["daylight_duration"] = daily_daylight_duration
        daily_data["relative_humidity_2m_mean"] = daily_relative_humidity_2m_mean

        daily_dataframe = pd.DataFrame(data = daily_data)
        print("\nDaily data\n", daily_dataframe)