"""Main funda scraper module"""
import argparse
import datetime
import json
import multiprocessing as mp
import os
from typing import List, Literal, Optional

import pandas as pd
import requests
from bs4 import BeautifulSoup
from tqdm import tqdm
from tqdm.contrib.concurrent import process_map

from funda_scraper.config.core import config
from funda_scraper.preprocess import clean_list_date, preprocess_data
from funda_scraper.utils import logger


class FundaScraper(object):
    """
    Handles the main scraping function.
    """

    def __init__(
        self,
        want_to: str = "koop",
        area: str = "",
        page_start: int = 1,
        n_pages: int = 1,
        find_past: bool = False,
        min_price: Optional[int] = None,
        max_price: Optional[int] = None,
        url : str = "",
    ):
        # Init attributes
        self.area = area.lower().replace(" ", "-")
        self.want_to = want_to
        self.find_past = find_past
        self.page_start = max(page_start, 1)
        self.n_pages = max(n_pages, 1)
        self.page_end = self.page_start + self.n_pages - 1
        self.min_price = min_price
        self.max_price = max_price

        # Instantiate along the way
        self.links: List[str] = []
        self.raw_df = pd.DataFrame()
        self.clean_df = pd.DataFrame()
        self.base_url = config.base_url
        self.url = url
        self.selectors = config.css_selector

    def __repr__(self):
        return (
            f"FundaScraper(area={self.area}, "
            f"want_to={self.want_to}, "
            f"n_pages={self.n_pages}, "
            f"page_start={self.page_start}, "
            f"find_past={self.find_past})"
            f"min_price={self.min_price})"
            f"max_price={self.max_price})"
        )

    @property
    def to_buy(self) -> bool:
        """Whether to buy or not"""
        if self.want_to.lower() in ["buy", "koop", "b", "k"]:
            return True
        elif self.want_to.lower() in ["rent", "huur", "r", "h"]:
            return False
        else:
            raise ValueError("'want_to' must be either 'buy' or 'rent'.")

    @staticmethod
    def _check_dir() -> None:
        """Check whether a temporary directory for data"""
        if not os.path.exists("data"):
            os.makedirs("data")

    @staticmethod
    def _get_links_from_one_parent(url: str) -> List[str]:
        """Scrape all the available housing items from one Funda search page."""
        response = requests.get(url, headers=config.header)
        soup = BeautifulSoup(response.text, "lxml")

        script_tag = soup.find_all("script", {"type": "application/ld+json"})[0]
        json_data = json.loads(script_tag.contents[0])
        urls = [item["url"] for item in json_data["itemListElement"]]
        return list(set(urls))

    def reset(
        self,
        area: Optional[str] = None,
        want_to: Optional[str] = None,
        page_start: Optional[int] = None,
        n_pages: Optional[int] = None,
        find_past: Optional[bool] = None,
        min_price: Optional[int] = None,
        max_price: Optional[int] = None,
    ) -> None:
        """Overwrite or initialise the searching scope."""
        if area is not None:
            self.area = area
        if want_to is not None:
            self.want_to = want_to
        if page_start is not None:
            self.page_start = max(page_start, 1)
        if n_pages is not None:
            self.n_pages = max(n_pages, 1)
        if find_past is not None:
            self.find_past = find_past
        if min_price is not None:
            self.min_price = min_price
        if max_price is not None:
            self.max_price = max_price

    def fetch_all_links(self, page_start: int = None, n_pages: int = None) -> None:
        """Find all the available links across multiple pages."""

        page_start = self.page_start if page_start is None else page_start
        n_pages = self.n_pages if n_pages is None else n_pages

        logger.info("*** Phase 1: Fetch all the available links from all pages *** ")
        urls = []
        main_url = self._build_main_query_url()

        for i in tqdm(range(page_start, page_start + n_pages)):
            try:
                item_list = self._get_links_from_one_parent(
                    f"{main_url}&search_result={i}"
                )
                urls += item_list
            except IndexError:
                self.page_end = i
                logger.info(f"*** The last available page is {self.page_end} ***")
                break

        urls = list(set(urls))
        logger.info(
            f"*** Got all the urls. {len(urls)} houses found from {self.page_start} to {self.page_end} ***"
        )
        self.links = urls

    def _build_main_query_url(self) -> str:
        if self.url: # Override URL if provided.
            main_url = self.url
        else:
            query = "koop" if self.to_buy else "huur"
            main_url = f"{self.base_url}/zoeken/{query}?selected_area=%22{self.area}%22"

        if self.find_past:
            main_url = f"{main_url}&availability=%22unavailable%22"

        if self.min_price is not None or self.max_price is not None:
            min_price = "" if self.min_price is None else self.min_price
            max_price = "" if self.max_price is None else self.max_price
            main_url = f"{main_url}&price=%22{min_price}-{max_price}%22"

        return main_url

    @staticmethod
    def get_value_from_css(soup: BeautifulSoup, selector: str) -> str:
        """Use CSS selector to find certain features."""
        result = soup.select(selector)
        if len(result) > 0:
            result = result[0].text
        else:
            result = "na"
        return result

    def scrape_one_link(self, link: str) -> List[str]:
        """Scrape all the features from one house item given a link."""

        # Initialize for each page
        response = requests.get(link, headers=config.header)
        soup = BeautifulSoup(response.text, "lxml")

        # Get the value according to respective CSS selectors
        if self.to_buy:
            if self.find_past:
                list_since_selector = self.selectors.date_list
            else:
                list_since_selector = self.selectors.listed_since
        else:
            if self.find_past:
                list_since_selector = ".fd-align-items-center:nth-child(9) span"
            else:
                list_since_selector = ".fd-align-items-center:nth-child(7) span"

        result = [
            link,
            self.get_value_from_css(soup, self.selectors.price),
            self.get_value_from_css(soup, self.selectors.address),
            self.get_value_from_css(soup, self.selectors.descrip),
            self.get_value_from_css(soup, list_since_selector),
            self.get_value_from_css(soup, self.selectors.zip_code),
            self.get_value_from_css(soup, self.selectors.size),
            self.get_value_from_css(soup, self.selectors.year),
            self.get_value_from_css(soup, self.selectors.living_area),
            self.get_value_from_css(soup, self.selectors.kind_of_house),
            self.get_value_from_css(soup, self.selectors.building_type),
            self.get_value_from_css(soup, self.selectors.num_of_rooms),
            self.get_value_from_css(soup, self.selectors.num_of_bathrooms),
            self.get_value_from_css(soup, self.selectors.layout),
            self.get_value_from_css(soup, self.selectors.energy_label),
            self.get_value_from_css(soup, self.selectors.insulation),
            self.get_value_from_css(soup, self.selectors.heating),
            self.get_value_from_css(soup, self.selectors.ownership),
            self.get_value_from_css(soup, self.selectors.exteriors),
            self.get_value_from_css(soup, self.selectors.parking),
            self.get_value_from_css(soup, self.selectors.neighborhood_name),
            self.get_value_from_css(soup, self.selectors.date_list),
            self.get_value_from_css(soup, self.selectors.date_sold),
            self.get_value_from_css(soup, self.selectors.term),
            self.get_value_from_css(soup, self.selectors.price_sold),
            self.get_value_from_css(soup, self.selectors.last_ask_price),
            self.get_value_from_css(soup, self.selectors.last_ask_price_m2).split("\r")[
                0
            ],
        ]

        # Deal with list_since_selector especially, since its CSS varies sometimes
        # if clean_list_date(result[4]) == "na":
        #     for i in range(6, 16):
        #         selector = f".fd-align-items-center:nth-child({i}) span"
        #         update_list_since = self.get_value_from_css(soup, selector)
        #         if clean_list_date(update_list_since) == "na":
        #             pass
        #         else:
        #             result[4] = update_list_since

        photos_list = [p.get("data-lazy-srcset") for p in soup.select(self.selectors.photo)]
        photos_string = ", ".join(photos_list)

        # Clean up the retried result from one page
        result = [r.replace("\n", "").replace("\r", "").strip() for r in result]
        result.append(photos_string)
        return result

    def scrape_pages(self) -> None:
        """Scrape all the content acoss multiple pages."""

        logger.info("*** Phase 2: Start scraping from individual links ***")
        df = pd.DataFrame({key: [] for key in self.selectors.keys()})

        # Scrape pages with multiprocessing to improve efficiency
        # TODO: use asynctio instead
        pools = mp.cpu_count()
        content = process_map(self.scrape_one_link, self.links, max_workers=pools)

        for i, c in enumerate(content):
            df.loc[len(df)] = c

        df["city"] = df["url"].map(lambda x: x.split("/")[4])
        df["log_id"] = datetime.datetime.now().strftime("%Y%m-%d%H-%M%S")
        if not self.find_past:
            df = df.drop(["term", "price_sold", "date_sold"], axis=1)
        logger.info(f"*** All scraping done: {df.shape[0]} results ***")
        self.raw_df = df

    def save_csv(self, df: pd.DataFrame, filepath: str = None) -> None:
        """Save the result to a .csv file."""
        if filepath is None:
            self._check_dir()
            date = str(datetime.datetime.now().date()).replace("-", "")
            status = "unavailable" if self.find_past else "unavailable"
            want_to = "buy" if self.to_buy else "rent"
            filepath = f"./data/houseprice_{date}_{self.area}_{want_to}_{status}_{len(self.links)}.csv"
        df.to_csv(filepath, index=False)
        logger.info(f"*** File saved: {filepath}. ***")

    def run(
        self, raw_data: bool = False, save: bool = False, filepath: str = None
    ) -> pd.DataFrame:
        """
        Scrape all links and all content.

        :param raw_data: if true, the data won't be pre-processed
        :param save: if true, the data will be saved as a csv file
        :param filepath: the name for the file
        :return: the (pre-processed) dataframe from scraping
        """
        self.fetch_all_links()
        self.scrape_pages()

        if raw_data:
            df = self.raw_df
        else:
            logger.info("*** Cleaning data ***")
            df = preprocess_data(df=self.raw_df, is_past=self.find_past)
            self.clean_df = df

        if save:
            self.save_csv(df, filepath)

        logger.info("*** Done! ***")
        return df


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--area",
        type=str,
        help="Specify which area you are looking for",
        default="amsterdam",
    )
    parser.add_argument(
        "--want_to",
        type=str,
        help="Specify you want to 'rent' or 'buy'",
        default="rent",
    )
    parser.add_argument(
        "--find_past",
        type=bool,
        help="Indicate whether you want to use hisotrical data or not",
        default=False,
    )
    parser.add_argument(
        "--page_start", type=int, help="Specify which page to start scraping", default=1
    )
    parser.add_argument(
        "--n_pages", type=int, help="Specify how many pages to scrape", default=1
    )
    parser.add_argument(
        "--min_price", type=int, help="Specify the min price", default=None
    )
    parser.add_argument(
        "--max_price", type=int, help="Specify the max price", default=None
    )
    parser.add_argument(
        "--raw_data",
        type=bool,
        help="Indicate whether you want the raw scraping result or preprocessed one",
        default=False,
    )
    parser.add_argument(
        "--save",
        type=bool,
        help="Indicate whether you want to save the data or not",
        default=True,
    )

    args = parser.parse_args()
    scraper = FundaScraper(
        area=args.area,
        want_to=args.want_to,
        find_past=args.find_past,
        page_start=args.page_start,
        n_pages=args.n_pages,
        min_price=args.min_price,
        max_price=args.max_price,
    )
    df = scraper.run(raw_data=args.raw_data, save=args.save)
    print(df.head())