from __future__ import unicode_literals

from datetime import datetime

from chatter.chatterbot.logic import LogicAdapter


class TimeLogicAdapter(LogicAdapter):
    """
    The TimeLogicAdapter returns the current time.

    :kwargs:
        * *positive* (``list``) --
          The time-related questions used to identify time questions.
          Defaults to a list of English sentences.
        * *negative* (``list``) --
          The non-time-related questions used to identify time questions.
          Defaults to a list of English sentences.
    """

    def __init__(self, **kwargs):
        super(TimeLogicAdapter, self).__init__(**kwargs)
        from nltk import NaiveBayesClassifier

        self.positive = kwargs.get('positive', [
            'what time is it',
            'hey what time is it',
            'do you have the time',
            'do you know the time',
            'do you know what time it is',
            'what is the time'
        ])

        self.negative = kwargs.get('negative', [
            'it is time to go to sleep',
            'what is your favorite color',
            'i had a great time',
            'thyme is my favorite herb',
            'do you have time to look at my essay',
            'how do you have the time to do all this'
            'what is it'
        ])

        labeled_data = (
                [(name, 0) for name in self.negative] +
                [(name, 1) for name in self.positive]
        )

        train_set = [
            (self.time_question_features(text), n) for (text, n) in labeled_data
        ]

        self.classifier = NaiveBayesClassifier.train(train_set)

    def time_question_features(self, text):
        """
        Provide an analysis of significant features in the string.
        """
        features = {}

        # A list of all words from the known sentences
        all_words = " ".join(self.positive + self.negative).split()

        # A list of the first word in each of the known sentence
        all_first_words = []
        for sentence in self.positive + self.negative:
            all_first_words.append(
                sentence.split(' ', 1)[0]
            )

        for word in text.split():
            features['first_word({})'.format(word)] = (word in all_first_words)

        for word in text.split():
            features['contains({})'.format(word)] = (word in all_words)

        for letter in 'abcdefghijklmnopqrstuvwxyz':
            features['count({})'.format(letter)] = text.lower().count(letter)
            features['has({})'.format(letter)] = (letter in text.lower())

        return features

    def process(self, statement):
        from chatter.chatterbot.conversation import Statement

        now = datetime.now()

        time_features = self.time_question_features(statement.text.lower())
        confidence = self.classifier.classify(time_features)
        response = Statement('The current time is ' + now.strftime('%I:%M %p'))

        response.confidence = confidence
        return response