from .core.scrapers.redfin import RedfinScraper
from .core.scrapers.realtor import RealtorScraper
from .core.scrapers.zillow import ZillowScraper
from .core.scrapers.models import ListingType, Property, SiteName
from .core.scrapers import ScraperInput
from .exceptions import InvalidSite, InvalidListingType
from typing import Union
import pandas as pd


_scrapers = {
    "redfin": RedfinScraper,
    "realtor.com": RealtorScraper,
    "zillow": ZillowScraper,
}


def validate_input(site_name: str, listing_type: str) -> None:
    if site_name.lower() not in _scrapers:
        raise InvalidSite(f"Provided site, '{site_name}', does not exist.")

    if listing_type.upper() not in ListingType.__members__:
        raise InvalidListingType(
            f"Provided listing type, '{listing_type}', does not exist."
        )


def get_ordered_properties(result: Property) -> list[str]:
    return [
        "property_url",
        "site_name",
        "listing_type",
        "property_type",
        "status_text",
        "currency",
        "price",
        "apt_min_price",
        "tax_assessed_value",
        "square_feet",
        "price_per_sqft",
        "beds",
        "baths",
        "lot_area_value",
        "lot_area_unit",
        "street_address",
        "unit",
        "city",
        "state",
        "zip_code",
        "country",
        "posted_time",
        "bldg_min_beds",
        "bldg_min_baths",
        "bldg_min_area",
        "bldg_unit_count",
        "bldg_name",
        "stories",
        "year_built",
        "agent_name",
        "mls_id",
        "description",
        "img_src",
        "latitude",
        "longitude",
    ]


def process_result(result: Property) -> pd.DataFrame:
    prop_data = result.__dict__

    prop_data["site_name"] = prop_data["site_name"].value
    prop_data["listing_type"] = prop_data["listing_type"].value.lower()
    if "property_type" in prop_data and prop_data["property_type"] is not None:
        prop_data["property_type"] = prop_data["property_type"].value.lower()
    else:
        prop_data["property_type"] = None
    if "address" in prop_data:
        address_data = prop_data["address"]
        prop_data["street_address"] = address_data.street_address
        prop_data["unit"] = address_data.unit
        prop_data["city"] = address_data.city
        prop_data["state"] = address_data.state
        prop_data["zip_code"] = address_data.zip_code
        prop_data["country"] = address_data.country

        del prop_data["address"]

    properties_df = pd.DataFrame([prop_data])
    properties_df = properties_df[get_ordered_properties(result)]

    return properties_df


def scrape_property(
    location: str,
    site_name: str,
    listing_type: str = "for_sale",  #: for_sale, for_rent, sold
) -> pd.DataFrame:
    """
    Scrape property from various sites from a given location and listing type.

    :returns: pd.DataFrame
    :param location: US Location (e.g. 'San Francisco, CA', 'Cook County, IL', '85281', '2530 Al Lipscomb Way')
    :param site_name: Site name (e.g. 'realtor.com', 'zillow', 'redfin')
    :param listing_type: Listing type (e.g. 'for_sale', 'for_rent', 'sold')
    :return: pd.DataFrame containing properties
    """

    validate_input(site_name, listing_type)

    scraper_input = ScraperInput(
        location=location,
        listing_type=ListingType[listing_type.upper()],
        site_name=SiteName.get_by_value(site_name.lower()),
    )

    site = _scrapers[site_name.lower()](scraper_input)
    results = site.search()

    properties_dfs = [process_result(result) for result in results]
    if not properties_dfs:
        return pd.DataFrame()

    return pd.concat(properties_dfs, ignore_index=True)