import pandas as pd
from tqdm import tqdm

from model_data.temp_inputs import input_data
from model_data.Property import Property
from model_data.config import EPC_AUTH_TOKEN
from epc_api.client import EpcClient
from model_data.downloader import pagenated_epc_download
from model_data.EpcClean import EpcClean


def handler():
    # To begin with, the input data is a list of dictionaries, however we would read this file in

    epc_client = EpcClient(auth_token=EPC_AUTH_TOKEN)

    input_properties = [
        Property(postcode=config['postcode'], address1=config['address1'], epc_client=epc_client)
        for config in input_data
    ]

    for p in input_properties:
        p.search_address_epc()

    local_authorities = {p.data['local-authority'] for p in input_properties}

    data = []
    for la in tqdm(local_authorities):
        data.extend(
            pagenated_epc_download(
                client=epc_client,
                params={"local-authority": la},
                page_size=5000,
                n_pages=10,
            )
        )

    cleaner = EpcClean(data)

    cleaner.clean()

    import pickle
    import os
    with open(os.path.abspath(os.path.dirname(__file__)) + "/data.pkl", "rb") as f:
        data = pickle.load(f)

    postcodes = [x["postcode"].upper() for x in data]
    address_meta = [
        {
            "postcode": x["postcode"].upper(),
            "address1": x["address1"].upper(),
            "address2": x["address2"].upper(),
            "address3": x["address3"].upper(),
            "address": x["address"],
            "uprn": x["uprn"]
        } for x in data
    ]

    # For testing:
    # from model_data.epc_attributes.HotWaterAttributes import HotWaterAttributes
    # from collections import Counter
    # count = Counter([x["main-fuel"] for x in data])
    # descriptions = {x["hotwater-description"] for x in data}
    # out = []
    # for description in descriptions:
    #     res = HotWaterAttributes(description).process()
    #     out.append(
    #         {
    #             "original_description": description,
    #             **res
    #         }
    #     )
    # df = pd.DataFrame(out)
    # df = df.sort_values("original_description")
    # df = df.reset_index(drop=True)
    #
    # import numpy as np
    # idx = 1
    # record = df[df.index == idx].to_dict("records")[0]
    # record = {k: v for k, v in record.items() if v not in [None, np.nan]}
    # from pprint import pprint
    # pprint(record)
    #
    # # Issues:
    # # 1) '2207 Time and temperature zone control' - we don't pick up any reference to the fact this is a time and
    # # temperature zone control
    # #     and we only pick up temperature zone control at the moment. Can we capture this too
    # # 2) 'Charging system linked to use of community heating, programmer and at least two room stats' - what are room
    # # stats and how should
    # #     we capture this?
    #
    # df.to_dict("records")

    from model_data.Need import Need
    import os

    need_client = Need(
        local_authorities=local_authorities,
        path=os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/need_2021_anon_dataset_4million.csv"
    )
    need_data = need_client.read()

    ## Land registry
    from model_data.LandRegistryClient import LandRegistryClient
    import os

    land_registry_client = LandRegistryClient(
        paths=[
            os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-monthly-update-new-version.csv",
            os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2022 (1).csv",
            os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2021.csv",
            os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2020.csv",
            os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2019.csv",
            os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2018.csv",
            os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2017-part1.csv",
            os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-2017-part2.csv",
        ],
        addresses=address_meta
    )