from collections import Counter
import pandas as pd
import numpy as np
# from scipy.spatial import cKDTree


# df_amenities = pd.read_csv("df_indonesia.csv").rename(
#     columns={"latitude":"lat", "longitude":"lon"}
# )
# df_banks = pd.read_csv("df_bank_indonesia.csv").rename(
#     columns={"latitude":"lat", "longitude":"lon"}
# )

# df_amenities["fsq_category_labels"] = df_amenities["fsq_category_labels"].apply(
#     lambda x: eval(x)
# )

# bank_coords = df_banks[['lat','lon']].values
# tree_banks = cKDTree(bank_coords)

# amenity_coords = df_amenities[['lat','lon']].values
# tree_amenities = cKDTree(amenity_coords)

DATASET_COLUMNS = [
    'Dining and Drinking', 'Community and Government', 'Retail',
       'Business and Professional Services', 'Landmarks and Outdoors',
       'Arts and Entertainment', 'Health and Medicine',
       'Travel and Transportation', 'Sports and Recreation',
       'Event'
]


GOOGLE_PLACE_TYPE_MAPPING = [
    # Dining and Drinking
    [
        'restaurant', 'bar', 'cafe', 'bakery', 'night_club'
    ],
    # Community and Government
    [
        'government_office', 'local_government_office', 'city_hall',
        'courthouse', 'police', 'fire_station', 'post_office', 'library'
    ],
    # Retail
    [
        'store', 'shopping_mall', 'grocery_store', 'pharmacy',
        'supermarket', 'drugstore'
    ],
    # Business and Professional Services
    [
        'bank', 'atm', 'corporate_office', 'accounting', 'lawyer',
        # 'establishment'
    ],
    # Landmarks and Outdoors
    [
        'park', 'tourist_attraction', 'national_park',
        'historical_landmark', 'cultural_landmark'
    ],
    # Arts and Entertainment
    [
        'movie_theater', 'museum', 'art_gallery',
        'performing_arts_theater', 'amusement_park', 'aquarium', 'zoo'
    ],
    # Health and Medicine
    [
        'hospital', 'doctor', 'dentist', 'pharmacy',
        'physiotherapist', 'spa'
    ],
    # Travel and Transportation
    [
        'airport', 'bus_station', 'train_station', 'transit_station',
        'subway_station', 'parking', 'lodging'
    ],
    # Sports and Recreation
    [
        'gym', 'stadium', 'bowling_alley', 'fitness_center',
        'park', 'amusement_center'
    ],
    # Event (Mapped to common event venues)
    [
        'event_venue', 'convention_center', 'banquet_hall', 'stadium'
    ]
]

import os
from google.maps import areainsights_v1
from google.maps.areainsights_v1.types import ComputeInsightsRequest, Filter, LocationFilter, Insight
from google.type import latlng_pb2
import asyncio


async def compute_places_count_with_api_key(api_key, lat, lng, radius, places):
    try:
        client = areainsights_v1.AreaInsightsAsyncClient(
            client_options={"api_key": api_key}
        )

        # 1. Define the geographic filter (a circle)
        location_filter = LocationFilter(
            circle=LocationFilter.Circle(
                lat_lng=latlng_pb2.LatLng(latitude=lat, longitude=lng),
                radius=radius
            )
        )

        # 2. Define the place type filter
        type_filter = areainsights_v1.TypeFilter(
            included_types=places
        )

        # 3. Assemble the main request body
        request = ComputeInsightsRequest(
            # We want the total count of matching places
            insights=[Insight.INSIGHT_COUNT],
            filter=Filter(
                location_filter=location_filter,
                type_filter=type_filter
            )
        )

        response = await client.compute_insights(request=request)

        count = int(response.count)

        return count
    except Exception as e:
        print(f"An error occurred: {e}")
        return None
        

def compute_features(candidate_point, api_key, radius=5000):
    lat, lon = candidate_point

    features = {
        'num_banks_in_radius':0,
        # 'total_amenities':0,
        # 'category_diversity':0
    }

    for i,places in enumerate(GOOGLE_PLACE_TYPE_MAPPING):
      total_count = asyncio.run(compute_places_count_with_api_key(
          api_key,
          lat,
          lon,
          radius,
          places
      ))

      features[f'num_{DATASET_COLUMNS[i]}'] = total_count


    n_banks = asyncio.run(compute_places_count_with_api_key(
          api_key,
          lat,
          lon,
          radius,
          ['atm']
    ))
    

    features.update({
        'num_banks_in_radius': n_banks,
        # 'total_amenities': sum(v for v in features.values()),
        # 'category_diversity': sum(bool(v) for v in features.values())
    })

    print(features)

    return features