import pandas as pd
import os

from tqdm import tqdm

from dotenv import load_dotenv
from backend.SearchEpc import SearchEpc

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


def app():
    # Read in rolling master
    master = pd.read_csv(
        "/Users/khalimconn-kowlessar/Downloads/UNITAS ( STOKE) MASTER ROLLING SHEET UPDATED 16.5.24 K - PASSWORD "
        "PROTECTED/ECO 4 - PHASE 1-Table 1.csv"
    )

    master = master[master["INSTALLER"] == "SCIS"]

    master = master[
        [
            'UPRN', 'NO.', 'Street / Block Name', 'Town/Area', 'Post Code', 'Surveyor', "SUBMISSION DATE"
        ]
    ]

    master = master[~pd.isnull(master["UPRN"])]
    master = master[master["UPRN"] != "NOT ON ASSET LIST"]

    heights = []
    eco_assessment_epcs = []
    for _, row in tqdm(master.iterrows(), total=len(master)):
        searcher = SearchEpc(
            address1="",
            postcode="",
            uprn=str(int(row["UPRN"])),
            auth_token=EPC_AUTH_TOKEN,
            os_api_key="",
            property_type=None,
            fast=False,
        )
        # 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

        # Look for eco assessment epcs
        eco_epc = [x for x in [searcher.newest_epc] + searcher.older_epcs if x['transaction-type'] == 'ECO assessment']
        # Take the newest
        eco_epc = sorted(eco_epc, key=lambda x: x['inspection-date'], reverse=True)
        if eco_epc:
            eco_assessment_epcs.append(eco_epc[0])

        height = {
            "uprn": row["UPRN"],
            "floor_height": searcher.newest_epc["floor-height"]
        }
        heights.append(height)

    heights_df = pd.DataFrame(heights)

    eco_assessment_epcs_df = pd.DataFrame(eco_assessment_epcs)

    merged_heights_df = master.merge(heights_df, left_on="UPRN", right_on="uprn", how="inner")
    merged_heights_df = merged_heights_df[merged_heights_df["floor_height"] != ""]
    merged_eco_assessment_epcs_df = master.merge(eco_assessment_epcs_df[["uprn", "floor-height"]], left_on="UPRN",
                                                 right_on="uprn", how="inner")
    merged_eco_assessment_epcs_df["floor-height"] = merged_eco_assessment_epcs_df["floor-height"].astype(float)

    merged_eco_assessment_epcs_df.groupby("Surveyor")["floor-height"].mean()

    # Store
    merged_heights_df.to_csv("Unitas 2022 heights - based on newest EPC.csv", index=False)
    merged_eco_assessment_epcs_df.to_csv("Unitas 2022 heights - based on ECO assessment EPC.csv", index=False)

    # Read in a diferent sheet
    master = pd.read_excel(
        "/Users/khalimconn-kowlessar/Downloads/COMMUNITY HOUSING SURVEYS WITH A POST EPC.xlsx"
    )

    master["row_number"] = master.index

    heights = []
    eco_assessment_epcs = []
    expected_pre = []
    expected_post = []
    biggest_floor_height = []
    for _, row in tqdm(master.iterrows(), total=len(master)):

        full_address = ", ".join([
            str(row["NO."]), row["Street / Block Name"], row["Town/Area"], row["Post Code"]
        ])
        searcher = SearchEpc(
            address1=str(row["NO."]),
            postcode=str(row["Post Code"]),
            auth_token=EPC_AUTH_TOKEN,
            os_api_key="",
            property_type=None,
            fast=False,
            full_address=full_address
        )

        # 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

        all_epcs = [searcher.newest_epc] + searcher.older_epcs
        # Search for SAP 54s
        sap_54s = [x for x in all_epcs if x["current-energy-efficiency"] == "54"]
        sap_69s = [x for x in all_epcs if x["current-energy-efficiency"] == "69"]
        heights = [float(x["floor-height"]) for x in all_epcs if x["floor-height"] != ""]

        # Look for eco assessment epcs
        eco_epc = [x for x in [searcher.newest_epc] + searcher.older_epcs if x['transaction-type'] == 'ECO assessment']
        # Take the newest
        eco_epc = sorted(eco_epc, key=lambda x: x['inspection-date'], reverse=True)
        if eco_epc:
            eco_assessment_epcs.append(
                {
                    "row_number": row["row_number"],
                    **eco_epc[0]
                }
            )

        if heights:
            floor_height_max = max(heights)
            biggest_floor_height.append(
                {
                    "row_number": row["row_number"],
                    "floor_height": floor_height_max
                }
            )

        if sap_54s:
            expected_pre.append(
                {
                    "row_number": row["row_number"],
                    **sap_54s[0]
                }
            )

        if sap_69s:
            expected_post.append(
                {
                    "row_number": row["row_number"],
                    **sap_69s[0]
                }
            )

    expected_pre_df = pd.DataFrame(expected_pre)
    expected_post_df = pd.DataFrame(expected_post)

    heights_df = pd.DataFrame(biggest_floor_height)
    eco_assessment_epcs_df = pd.DataFrame(eco_assessment_epcs)

    merged_heights_df = master.merge(heights_df, on="row_number", how="inner")
    merged_heights_df = merged_heights_df[merged_heights_df["floor_height"] != ""]

    merged_eco_assessment_epcs_df = master.merge(
        eco_assessment_epcs_df[["row_number", "floor-height"]], on="row_number", how="inner"
    )
    merged_eco_assessment_epcs_df["floor-height"] = merged_eco_assessment_epcs_df["floor-height"].astype(float)

    merged_eco_assessment_epcs_df.groupby("Surveyor")["floor-height"].mean()

    # Check average floor height for social housing properties with ECO assessment EPCs in Birmingham
    sample = pd.read_csv("local_data/all-domestic-certificates/domestic-E08000025-Birmingham/certificates.csv")
    sample = sample[sample["TRANSACTION_TYPE"] == "ECO assessment"]
    sample = sample[sample["TENURE"].isin(["rental (social)", "Rented (social)"])]
    sample["FLOOR_HEIGHT"] = sample["FLOOR_HEIGHT"].astype(float)
    sample["FLOOR_HEIGHT"].mean()

    sample[pd.to_datetime(sample["LODGEMENT_DATE"]) >= "2022-01-01"]["FLOOR_HEIGHT"].mean()