main.py

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


def findlast10(postal):
    df = pd.read_json("data/data_features.json", lines=True)
    df_filtered = df[df["Postal"] == str(postal)]

    df_output = df_filtered.sort_values(by="transaction_yr", ascending=False).head(10).reset_index(drop=True)

    storey_dict = {
        "01 TO 03": 1,
        "04 TO 06": 2,
        "07 TO 09": 3,
        "10 TO 12": 4,
        "13 TO 15": 5,
        "16 TO 18": 6,
        "19 TO 21": 7,
        "22 TO 24": 8,
        "25 TO 27": 9,
        "28 TO 30": 10,
        "31 TO 33": 11,
        "34 TO 36": 12,
        "37 TO 39": 13,
        "40 TO 42": 14,
        "43 TO 45": 15,
        "46 TO 48": 16,
        "49 TO 51": 17,
    }

    # Swap keys and values using dictionary comprehension
    swapped_dict = {value: key for key, value in storey_dict.items()}

    df_output["storey_height"] = df_output["storey_height"].apply(lambda x: swapped_dict[x])
    df_out = df_output[["transaction", "area", "storey_height", "resale_price"]]

    return df_out


def main_fn(Postal_, age_, town_, storey_, room_):
    # Load model
    filename = "model/model.pkl"
    if os.path.exists(filename):
        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_input
    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)

    # Obtain the last 10 transactions with the same postal address
    df = findlast10(Postal_input)

    return (int(price), df)


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