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import pickle
import numpy as np
__model =None
__location_encoder = None
__location_list = None
def load_assests():
global __model
global __location_encoder
global __location_list
with open('assets/banglore_price_prediction_model.pickle', 'rb') as f:
__model = pickle.load(f)
with open('assets/location_encoder.pickle', 'rb') as ld:
__location_encoder= pickle.load(ld)
__location_list = __location_encoder.categories_[0]
def get_estimated_price(location,bhk,tsqft,bath):
try:
x = __location_encoder.transform([[location]]).toarray()[0]
except:
x = np.zeros(len(__location_list))
x = np.append(x[1:], np.array([bhk, tsqft, bath]))
return __model.predict(x.reshape(1, -1))[0]
# load_assests()
# get_estimated_price('Devarabeesana Halli', 2, 1100.0, 2.0)
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