import pandas as pd
import twint
from datetime import date


class TwitterScraper(object):
    """
    This class is a twitter TwitterScraper called TwitterScraper. It takes the user as input and collects the user's tweets
    from 'from_date' to 'to_date'. If 'from_date' and 'to_date' are not specified, it collects the number of tweets 'num_tweets' from today.
    It outputs a dictionary with the tweet unique id and some other information.
    input: user, from_date, to_date, num_tweets
    output: dict
    """

    def __init__(self, from_date="2022-07-01", to_date=str(date.today()), num_tweets=20):
        # TODO: add a check to make sure that the dates are in the correct format.
        #  TODO: add a check to make sure that the number of tweets is a positive number.
        #  TODO: add a check to make sure that the number of tweets
        #   is not greater than the number of tweets in the date range.
        self.from_date = from_date
        self.to_date = to_date
        self.num_tweets = num_tweets
        self.conf = twint.Config()

    def scrape_by_user(self, _user: str):
        """This method uses twint to extract tweets  based on username"""
        self.conf.Search = "from:@" + _user  # is the search configuration is given in this format it searches after
        # user_names.
        return self.__get_tweets__from_twint__()

    def scrape_by_several_users(self, _users: list):
        """
        This method uses twint to extract tweets based on username. It takes a list of users as input.

        :param _users: list of users
        :return: dataframe
        """
        # TODO: test this method
        self.conf.Search = "from:@" + _users[0]
        for user in _users[1:]:
            self.conf.Search += " OR from:@" + user
        return self.__get_tweets__from_twint__()

    def scrape_by_string(self, _string: str):
        """This method uses twint to extract tweets based on string.
        all extracted tweets have the specified word in _string parameter in it.
        """
        self.conf.Search = _string  # this tells twint configuration to search for string 
        return self.__get_tweets__from_twint__()

    # TODO: Possibly include more than one user
    def scrape_by_user_and_string(self, _user: str, _string: str):
        """This method uses twint to extract tweets based on string and username"""
        self.conf.Username = _user
        self.conf.Search = _string
        return self.__get_tweets__from_twint__()

    # TODO: make method static (Possibly remove this)
    def get_only_tweets(self, tweet_and_replies_info):
        tweet_and_replies = tweet_and_replies_info["tweet"]
        """
        This functions input arg is a data frame (the output from scrape methords ) and removes...
         all tweets starting with \"@\" which is indicator of a reply or retweet.
        """
        indx_replies = []
        for i in range(len(tweet_and_replies)):
            if tweet_and_replies[i].startswith("@"):
                indx_replies.append(i)

        tweets_info = tweet_and_replies_info.drop(labels=indx_replies, axis=0)
        # drop removes the columns which its index specified by
        # indx_replies. axis=0  if we want to delete rows.
        # print(len(tweets['tweet']), " of them are Tweets")
        return tweets_info

    def __get_tweets__from_twint__(self):
        """ __get_tweets_from_twint__
        tweet info is a dataframe with fallowing columns
            Index(['id', 'conversation_id', 'created_at', 'date', 'timezone', 'place',
            'tweet', 'language', 'hashtags', 'cashtags', 'user_id', 'user_id_str',
            'username', 'name', 'day', 'hour', 'link', 'urls', 'photos', 'video',
            'thumbnail', 'retweet', 'nlikes', 'nreplies', 'nretweets', 'quote_url',
            'search', 'near', 'geo', 'source', 'user_rt_id', 'user_rt',
            'retweet_id', 'reply_to', 'retweet_date', 'translate', 'trans_src',
            'trans_dest']
        we just pick the relevant ones.
        c is a twint.Config() object
        we also configure twint output.
        """
        self.conf.Pandas = True  #
        self.conf.Count = True  #
        self.conf.Limit = self.num_tweets  # specifies how many tweet should be scraped
        self.conf.Since = self.from_date
        self.conf.Until = self.to_date
        self.conf.Hide_output = True  # Hides the output. If set to False it will print tweets in the terminal window.
        twint.run.Search(self.conf)
        tweet_and_replies_inf = twint.output.panda.Tweets_df  # here we say that output souldwe dataframe.
        if tweet_and_replies_inf.empty:
            print("No tweet containing the word \"" + self.conf.Search + "\" could be found!")
        else:
            tweet_and_replies_inf = tweet_and_replies_inf[
                     ["id", "tweet", "date", "user_id", "username", "urls", 'nlikes', 'nreplies', 'nretweets']]
        return tweet_and_replies_inf

    # def __check_date_type(d1,d2): if (type(d1) or type(d2)) is not type("str"):  # If the type of ite date input
    # is not string it generates exception print("[!] Please make sure the date is a string in this format
    # \"yyyy-mm-dd\" ") raise EXCEPTION("Incorrect date type Exception!") elif (len(d1.split("-")) or len(d2.split(
    # "-")))<2: print("[!] Please make sure the date is a string in this format \"yyyy-mm-dd\" ") raise EXCEPTION(
    # "Incorrect date type Exception!")

    def __repr__(self):
        return "TwitterScraper(from_date={}, to_date={}, num_tweets={})".format(self.from_date, self.to_date,
                                                                                self.num_tweets)
                                                                                


if __name__ == "__main__":
    sc = TwitterScraper(from_date="2022-05-01", to_date="2022-07-31", num_tweets=40)
    dc = sc.scrape_by_user("jimmieakesson")
    print(dc.head())
    print(dc.shape)
    print(dc.columns)