import nltk
from nltk import wordpunct_tokenize
from nltk.corpus import stopwords


#
# See: http://www.nltk.org/
#
# Code from: http://blog.alejandronolla.com/2013/05/15/detecting-text-language-with-python-and-nltk/
#


class LanguageIdentifier(object):

    def __init__(self):
        """Initialize the language detection."""

        # Avoid the following LookupError:
        #   """ Resource 'corpora/stopwords.zip/stopwords/' not found. """

        try:
            languages = stopwords.fileids()
        except:
            nltk.download("stopwords")


    def _calculate_languages_ratios(self, text):
        """
        Calculate probability of given text to be written in several languages and
        return a dictionary that looks like {'french': 2, 'spanish': 4, 'english': 0}
        
        @param text: Text whose language want to be detected
        @type text: str
        
        @return: Dictionary with languages and unique stopwords seen in analyzed text
        @rtype: dict
        """

        languages_ratios = {}

        tokens = wordpunct_tokenize(text)
        words = [ word.lower() for word in tokens ]

        # Compute per language included in nltk number of unique stopwords appearing in analyzed text
        for language in stopwords.fileids():

            stopwords_set = set(stopwords.words(language))
            words_set = set(words)
            common_elements = words_set.intersection(stopwords_set)

            languages_ratios[language] = len(common_elements) # language "score"

        return languages_ratios


    def detect_language(self, text):
        """
        Calculate probability of given text to be written in several languages and
        return the highest scored.
        
        It uses a stopwords based approach, counting how many unique stopwords
        are seen in analyzed text.
        
        @param text: Text whose language want to be detected
        @type text: str
        
        @return: Most scored language guessed
        @rtype: str
        """

        ratios = self._calculate_languages_ratios(text)

        most_rated_language = max(ratios, key=ratios.get)

        return most_rated_language