from funda_scraper import FundaScraper
import googlemaps
import datetime, os, pickle, json
import tqdm

gmaps = googlemaps.Client(key='AIzaSyC59N9i6Akvx6T2EIU2NfzqYacNDKVm1JE')

# Geocoding an address
# geocode_result = gmaps.geocode('1600 Amphitheatre Parkway, Mountain View, CA')

# Look up an address with reverse geocoding
# reverse_geocode_result = gmaps.reverse_geocode((40.714224, -73.961452))

# # Request directions via public transit
# # now = datetime.now()
# # directions_result = gmaps.directions("Sydney Town Hall",
# #                                      "Parramatta, NSW",
# #                                      mode="transit",
# #                                      departure_time=now)

# # Validate an address with address validation
# addressvalidation_result =  gmaps.addressvalidation(['1600 Amphitheatre Pk'], 
#                                                     regionCode='US',
#                                                     locality='Mountain View', 
#                                                     enableUspsCass=True)

def get_funda_data():
    scraper = FundaScraper(url="nijkerk/beschikbaar/100000-400000/woonhuis/tuin/eengezinswoning/landhuis/+30km/", find_past=False, n_pages=81)
    df = scraper.run()
    return df

out = []

'''
    {
        "name": "Landmarc",
        "address": "10 Columbus Cir #3, New York, NY 10019, USA",
        "url": "http://landmarc-restaurant.com/",
        "position": {"lat": 40.7685566,"lng": -73.985375317}
    }
'''

origin_locations = {
    "nfi_location" : (52.044867266861466, 4.3585175985355225),
    "hoogstraat_location" : (52.08725689123654, 5.147180442716177),
    "bakkersdijk_location" : (51.85802695253161, 4.482033956202426),
    "korhoen_location" : (52.5219455005375, 5.732514040876346),
    "harde_location" : (52.41650138296019, 5.870995170999243)
}

def get_distances(out_dict, destination_location):
    for key in origin_locations:
        distance = gmaps.distance_matrix(origin_locations[key], destination_location, mode = 'driving')
        out_dict[key] = distance['rows'][0]['elements'][0]

def generate_json(houses):
    print(f"Doing {len(houses) * 6} requests")
    for i in tqdm.tqdm(range(len(houses))):
        out_dict = {}
        address = f"{houses.address.get(i)}, {houses.zip_code.get(i)}"
        res = gmaps.geocode(f"{address}")
        if len(res) == 0:
            print(f"{i}:Failed to get any loction for: {address}")
            continue
        if len(res) > 1:
            print(f"{i}:Failed to get location for: {address}, assuming 0")
            # continue
        destination_location = res[0]['geometry']['location']
        # distance_matrix  = gmaps.distance_matrix(origin_locations['nfi_location'], destination_location, mode = 'driving')
        out_dict['name'] = address
        out_dict['position'] = destination_location
        for key in houses.keys():
            out_dict[key] = houses.__getattr__(key).get(i)

        get_distances(out_dict, destination_location)
            
        out.append(out_dict)
        
    
    final = json.dumps(out, indent=2)
    out_f = open('out.json', 'w')
    out_f.writelines(final)


if __name__ == "__main__":
    if os.path.exists('panda_dump.bin'):
        data = pickle.load(open('panda_dump.bin', 'rb'))
    else:
        data = get_funda_data()
        pickle.dump(data, open('panda_dump.bin', 'wb'))
    generate_json(data)