import json
import requests
from misc import nearest_mrt
import pickle
import os
import pandas as pd

###This is to create MRT names and MRT locations


def main_fn(Postal_,age_,town_,storey_,room_):
##Input structure into model is##
    filename = 'finalized_model.sav'

    if os.path.exists("./finalized_model.sav"):
        model = pickle.load(open(filename, 'rb'))
        print('loaded model')
    else:
        print('failed loading model')

    #extract feature names#
    feature_names = model.feature_names
    input = [0]*len(feature_names)
    # print(feature_names)

    #Set up mrt_list
    mrt_name = []
    mrt_loc = []
    with open('data/mrt_list.json', 'r') as file:
        for line in file:
            item = json.loads(line)
            mrt_name.append(item['MRT'])
            loc = tuple([float(i) for i in item['location']])
            mrt_loc.append(loc)

    #Query for latitude and longitude

    ##POSTAL
    Postal_input = int(Postal_)
    # Postal_input = 680705
    input[feature_names.index('Postal')] = Postal_input

    ##DISTANCE TO MRT
    search_term = Postal_input
    query_string='https://developers.onemap.sg/commonapi/search?searchVal={}&returnGeom=Y&getAddrDetails=Y&pageNum=1'.format(search_term)
    resp = requests.get(query_string)
    data = json.loads(resp.content)
    print(query_string)
    print(data)
    chosen_result = data['results'][0]

    #Calculate the distance to nearest MRT
    distance_km, nearest_mr = nearest_mrt(chosen_result['LATITUDE'], chosen_result['LONGITUDE'], mrt_name, mrt_loc)
    input[feature_names.index('distance_mrt')] = distance_km

    ##STOREY
    #Height is input, but then converted to the scale we used for iterating model
    height_input = int(storey_)
    # height_input = 51
    Height = (height_input+2)//3
    input[feature_names.index('storey_height')] = Height

    ##TOWN
    town_input = town_
    # town_input = 'CHOA CHU KANG'
    input[feature_names.index("town_"+town_input)] = 1

    ##ROOM
    room_input = room_
    # room_input = '4 ROOM'
    input[feature_names.index("flat_num_"+room_input)] = 1

    ##AGE/ TRANSACTION YEAR [Current default to 2022]
    age_input = int(age_)
    # age_input = 30
    input[feature_names.index('age_transation')] = age_input
    input[feature_names.index('transaction_yr')] = 2022  #Default to 2022 first

    #Create final_dataframe as input to model

    Actual = dict(zip(feature_names,input))
    Actual_df = pd.DataFrame(Actual, index=[0])

    resale_adj_price = model.predict(Actual_df)[0]

    #New resale index is set arbitrarily as 170
    resale_index = 170
    price = resale_adj_price*resale_index/133.9
    print(Actual_df)

    return int(price)

if __name__ == "__main__":
    Postal_,age_,town_,storey_,room_ = 680705, 30, 'CHOA CHU KANG', 12, '5 ROOM'
    price = main_fn(Postal_,age_,town_,storey_,room_)
    print(price)