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

def main_fn(Postal_,age_,town_,storey_,room_):
    #Load model
    filename = 'finalized_model2.sav'
    if os.path.exists("./finalized_model2.sav"):
        model = pickle.load(open(filename, 'rb'))
        print('loaded model')
    else:
        print('failed loading model')

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

    # #Test input
    # Postal_,age_,town_,storey_,room_ = 680705, 30, 'CHOA CHU KANG', 12, '5 ROOM'
   
    ##POSTAL
    Postal_input = int(Postal_)
    # Postal_input = 680705
    input[feature_names.index('Postal')] = Postal_input

    ##DISTANCE TO MRT
    search_term = Postal_
    query_string= 'https://www.onemap.gov.sg/api/common/elastic/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
    input[feature_names.index("town")]=town_

    ##Room
    input[feature_names.index("flat_num")]=room_

    ##AGE/ TRANSACTION YEAR [Current default to 2024]
    age_input = int(age_)
    # age_input = 30

    # Get the current date
    current_date = datetime.now()

    input[feature_names.index('age_transation')] = age_input
    input[feature_names.index('transaction_yr')] = current_date.year  #Default to 2024 first

    # Create final_dataframe as input to model
    Actual = dict(zip(feature_names,input))
    Actual_df = pd.DataFrame(Actual, index=[0])

    # Use model to predict adjusted price
    resale_adj_price = model.predict(Actual_df)[0]

    # Readjust back to actual price
    # Calculate the quarter
    quarter = (current_date.month - 1) // 3 + 1
    # Format the quarter in the desired format
    formatted_quarter = f"{quarter}Q{current_date.year}"
    RPI_pd = pd.read_csv('data/RPI_dict.csv', header=None)
    RPI_dict = dict(zip(RPI_pd[0], RPI_pd[1]))
    RPI = float(RPI_dict[formatted_quarter])
    price = resale_adj_price*(RPI/133.9) 

    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)