'number_last_suffix', # 15
'street_name', # 16
'street_suffix', # 17
'street_type', # 18
'locality_name', # 19
'state', # 20
'postcode' # 21
]
return labels_list
if __name__ == "__main__":
print(tf.__version__)
print(
predict_one(
"casa del gelato, 10A 24-26 high street road mount waverley vic 3183"
))
# load CSV
df_in = pd.read_csv(os.path.join(os.getcwd(), 'data/full_address.csv'),
header=0)
print(df_in.shape)
# get a list of addresses to parse
addresses_to_parse = df_in['FullAddress'].tolist()
# make predictions
print('Parsing addresses...')
parsed_addresses = predict(addresses_to_parse)
# save predictions into a dataframe
df_out = pd.DataFrame()
idx = 0
for parsed_address_dict in parsed_addresses:
def map_address_components(address):
#print('predicting for {}'.format(address))
# model_dir='/home/intotecho/yarraplanning/heritage_register/pretrained/'
return predict_one(address)
import argparse
from addressnet.predict import predict_one
from addressnet.library.log import get_logger
logger = get_logger(__name__)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("model_dir", help="Pretrained model directory")
parser.add_argument("address", help="Address string")
args = parser.parse_args()
predict_result = predict_one(args.address, args.model_dir)
logger.info(f'Model file : {args.model_dir}')
logger.info(f'Input address : {args.address}')
logger.info(f'Predict result: {predict_result}')
from addressnet.predict import predict_one
if __name__ == "__main__":
print(predict_one("33 chandos st st leonard"))
def extractaddress():
return (str(predict_one(request.args.get('fulladdress'))))
def predict_address_POST():
content = request.json
if not content['address'] is None:
return jsonify(predict_one(content['address']))
return jsonify({"message": "not address found"})
def predict_address(address):
return jsonify(predict_one(address))
Created on Fri Apr 3 14:08:26 2020 @author: Aman.Sivaprasad """ from addressnet.predict import predict_one text = "casa del gelato, 10A 24-26 high street road mount waverley vic 3183" text2 = "Van Siclen Avenue and Flatlands Avenue Brooklyn, New York 11207" text3 = "Tower 535 - 11007(4), 535 Jaffe Road, Causeway Bay HK, China" text4 = "Suite 803, 55 Wall Street New York, USA" text5 = 'The Book Club 100-106 Leonard St, Shoreditch, London, Greater London, EC2A 4RH, United Kingdom' text6 = '123 West Mifflin Street, Madison, WI, 53703' out = predict_one(text) out2 = predict_one(text2) out3 = predict_one(text3) out4 = predict_one(text4) out5 = predict_one(text5) print(out) print(out2) print(out3) print(out4) #using docker # #$ git clone https://github.com/anandaroop/try-postal.git
from addressnet.predict import predict_one
if __name__ == "__main__":
print(predict_one(", 10A 24-26,casa del gelato high street road mount waverley vic 3183"))
