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

# 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'
# ]

DATASET_COLUMNS = [
    'restaurant',
    'bookstore',
    'fast_food',
    'clinic',
    'school',
    'bakery',
    'convenience_store',
    'shopping_mall',
    'atm',
    'bank',
    'dentist',
    'pharmacy',
    'cafe',
    'supermarket',
    'hospital',
    'gym',
    'jewelry_store',
    'charging_station',
    'electronics_store',
    'clothing_store',
    'department_store',
    'hotel',
    'yoga',
    'attraction',
    'college',
    'co_working',
    'university',
    'hostel',
    'viewpoint'
]


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

GOOGLE_PLACE_TYPE_MAPPING = [
    # Original Column: 'restaurant'
    ['restaurant'],
    # Original Column: 'bookstore'
    ['book_store'],
    # Original Column: 'fast_food'
    ['fast_food_restaurant'],
    # Original Column: 'clinic'
    ['dental_clinic', 'doctor', 'medical_lab', 'skin_care_clinic'],
    # Original Column: 'school'
    ['school', 'primary_school', 'secondary_school', 'preschool'],
    # Original Column: 'bakery'
    ['bakery'],
    # Original Column: 'convenience_store'
    ['convenience_store'],
    # Original Column: 'shopping_mall'
    ['shopping_mall'],
    # Original Column: 'atm'
    ['atm'],
    # Original Column: 'bank'
    ['bank'],
    # Original Column: 'dentist'
    ['dentist', 'dental_clinic'],
    # Original Column: 'pharmacy'
    ['pharmacy', 'drugstore'],
    # Original Column: 'cafe'
    ['cafe', 'coffee_shop'],
    # Original Column: 'supermarket'
    ['supermarket'],
    # Original Column: 'hospital'
    ['hospital'],
    # Original Column: 'gym'
    ['gym', 'wellness_center'],
    # Original Column: 'jewelry_store'
    ['jewelry_store'],
    # Original Column: 'charging_station'
    ['electric_vehicle_charging_station'],
    # Original Column: 'electronics_store'
    ['electronics_store'],
    # Original Column: 'clothing_store'
    ['clothing_store', 'department_store'],
    # Original Column: 'department_store'
    ['department_store'],
    # Original Column: 'hotel'
    ['hotel', 'motel', 'resort_hotel'],
    # Original Column: 'yoga'
    ['yoga_studio'],
    # Original Column: 'attraction'
    ['tourist_attraction', 'amusement_park', 'museum', 'cultural_landmark'],
    # Original Column: 'college'
    ['university'],
    # Original Column: 'co_working' (No direct Place Type exists, using closest fit)
    ['corporate_office'],
    # Original Column: 'university'
    ['university'],
    # Original Column: 'hostel'
    ['hostel', 'budget_japanese_inn'],
    # Original Column: 'viewpoint' (No direct Place Type exists, using closest fit)
    ['tourist_attraction', 'observation_deck']
]



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