import os

import pandas as pd
from tqdm import tqdm

from dotenv import load_dotenv
from backend.SearchEpc import SearchEpc
from etl.epc_clean.epc_attributes.RoofAttributes import RoofAttributes

from recommendations.recommendation_utils import (
    estimate_perimeter,
    estimate_external_wall_area,
    estimate_number_of_floors
)

load_dotenv(dotenv_path="backend/.env")
EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")


def app():
    """
    This app is EPC pulling data for some properties owned by Unitas
    :return:
    """
    # asset_list = read_excel_from_s3(
    #     bucket_name="retrofit-datalake-dev",
    #     file_key="customers/guiness/TGP CW Properties PV.xlsx",
    #     header_row=0
    # )
    asset_list = pd.read_excel(
        "/Users/khalimconn-kowlessar/Downloads/UNITAS BUNGALOWS - EPC DATA PULL.xlsx", header=0
    )

    epc_data = []
    for _, home in tqdm(asset_list.iterrows(), total=len(asset_list)):

        searcher = SearchEpc(
            address1=str(home["Address Line 1"]),
            postcode=home["Post Code"],
            uprn=home["Property Reference"],
            auth_token=EPC_AUTH_TOKEN,
            os_api_key="",
            property_type=None,
            fast=True
        )
        # Force the skipping of estimating the EPC
        searcher.ordnance_survey_client.property_type = None
        searcher.ordnance_survey_client.built_form = None

        searcher.find_property(skip_os=True)
        if searcher.newest_epc is None:
            continue

        epc = {
            "asset_list_address": home["Address Line 1"],
            "asset_list_postcode": home["Post Code"],
            **searcher.newest_epc.copy()
        }

        epc_data.append(epc)

    epc_df = pd.DataFrame(epc_data)

    # Retrieve just the data we need
    epc_df = epc_df[
        [
            "asset_list_address",
            "uprn",
            "property-type",
            "built-form",
            "inspection-date",
            "current-energy-rating",
            "current-energy-efficiency",
            "roof-description",
            "walls-description",
            "transaction-type",
            # New fields needed
            "secondheat-description",
            "total-floor-area",
            "construction-age-band",
            "floor-height",
            "number-habitable-rooms",
            "mainheat-description"
        ]
    ]

    asset_list = asset_list.merge(
        epc_df,
        how="left",
        left_on=["Address Line 1"],
        right_on=["asset_list_address"]
    )

    asset_list = asset_list.drop(columns=["asset_list_address"])

    # Rename the columns
    asset_list = asset_list.rename(columns={
        "inspection-date": "Date of last EPC",
        "current-energy-efficiency": "SAP score on register",
        "current-energy-rating": "EPC rating on register",
        "property-type": "EPC Property Type",
        "built-form": "Archetype",
        "total-floor-area": "Property Floor Area",
        "construction-age-band": "Property Age Band",
        "floor-height": "Property Floor Height",
        "number-habitable-rooms": "Number of Habitable Rooms",
        "walls-description": "Wall Construction",
        "roof-description": "Roof Construction",
        "mainheat-description": "Heating Type",
        "secondheat-description": "Secondary Heating",
        "transaction-type": "Reason for last EPC"
    })

    asset_list["Estimated Number of Floors"] = asset_list.apply(
        lambda x: estimate_number_of_floors(property_type=x["EPC Property Type"]) if not pd.isnull(
            x["EPC Property Type"]) else None,
        axis=1
    )

    asset_list["Property Floor Area"] = asset_list["Property Floor Area"].astype(float)
    asset_list["Number of Habitable Rooms"] = asset_list["Number of Habitable Rooms"].astype(float)

    asset_list["Estimated Perimeter (m)"] = asset_list.apply(
        lambda x: estimate_perimeter(
            floor_area=x["Property Floor Area"] / x["Estimated Number of Floors"],
            num_rooms=x["Number of Habitable Rooms"] / x["Estimated Number of Floors"],
        ) if not pd.isnull(x["uprn"]) else None, axis=1
    )

    asset_list["Estimated Heat Loss Perimeter (m)"] = asset_list.apply(
        lambda x: estimate_external_wall_area(
            num_floors=x["Estimated Number of Floors"],
            floor_height=float(x["Property Floor Height"]) if x["Property Floor Height"] else 2.5,
            perimeter=x["Estimated Perimeter (m)"],
            built_form=x["Archetype"]
        ) if not pd.isnull(x["uprn"]) else None,
        axis=1
    )

    asset_list["Roof Insulation Thickness"] = asset_list.apply(
        lambda x: RoofAttributes(description=x["Roof Construction"]).process()["insulation_thickness"] if not pd.isnull(
            x["uprn"]) else None,
        axis=1
    )

    # Store as an excel
    filename = "UNITAS BUNGALOWS - EPC DATA PULL - May 30tg 2024.xlsx"
    asset_list.to_excel(filename, index=False)