from pathlib import Path
import pandas as pd
from rec.query import QueryParameters
from repositories.listing_repository import ListingRepository


async def export_to_csv(
    repository: ListingRepository,
    output_file: Path,
    query_parameters: QueryParameters | None = None,
) -> None:
    listings = await repository.get_listings(query_parameters=query_parameters)
    ds = [*[listing.__dict__ for listing in listings]]
    df = pd.DataFrame(ds)

    # read decisions on file
    decisions_path = "data/decisions.json"
    decisions = pd.read_json(decisions_path)
    df.loc[:, "decision"] = df.id.apply(lambda x: decisions.get(x))

    # remove _sa_instance_state column
    drop_columns = ["_sa_instance_state", "additional_info"]
    df = df.drop(columns=drop_columns)

    # remove all entries where we didnt calculate transit time (probably due to a too far distance)
    # df2 = df[df.travel_time_fastest.notna()]
    df2 = df

    # drop columns
    # dropcolumns = ['distance_per_transit', 'duration_static', 'distance']
    # s1 = df2['travel_time_fastest'].apply(pd.Series).drop(dropcolumns, axis=1)
    # s1 = df2

    # fill in gap values for service charge and lease left. This is for excel so we can use filters better there
    if "service_charge" not in df2.columns:
        df2.loc[:, "service_charge"] = -1
    df2.loc[:, "service_charge"] = df2.service_charge.fillna(-1)
    if "lease_left" not in df2.columns:
        df2.loc[:, "lease_left"] = -1
    df2.loc[:, "lease_left"] = df2.lease_left.fillna(-1)
    if "square_meters" not in df2.columns:
        df2.loc[:, "square_meters"] = -1
    df2.loc[:, "square_meters"] = df2.square_meters.fillna(-1)

    df3 = df2
    # df3 = pd.concat([df2.drop(['travel_time_fastest', 'travel_time_second'], axis=1), s1], axis=1)
    # df3.loc[:, 'duration'] = (df3.loc[:, ['duration']].min(axis=1) / 60).round()
    df3.shape
    df4 = df3

    # df5 = df4[columns]

    # Add some interesting columns
    df4.loc[:, "price_per_sqm"] = df4.price / df4.square_meters
    df5 = df4

    df6 = df5.sort_values(by=["price_per_sqm"], ascending=True)
    df6.to_csv(str(output_file), index=False)