Local call

chatter/chatter.py

@@ -7,8 +7,8 @@ from discord.ext import commands
 from redbot.core import Config
 from redbot.core.bot import Red
 
-from .chatterbot import ChatBot
+from .source import ChatBot
-from .chatterbot.trainers import ListTrainer
+from .source.trainers import ListTrainer
 
 from datetime import datetime,timedelta
 
@@ -31,6 +31,8 @@ class Chatter:
 
         self.config.register_global(**default_global)
         self.config.register_guild(**default_guild)
+        
+        self.loop = asyncio.get_event_loop()
     
     async def _get_conversation(self, ctx, in_channel: discord.TextChannel):
         """
@@ -59,12 +61,13 @@ class Chatter:
         
         return out
         
-    async def _train(self, data):
+    def _train(self, data):
         try:
             self.chatbot.train(data)
         except:
             return False
         return True
+
     @commands.group()
     async def chatter(self, ctx: commands.Context):
         """
@@ -90,12 +93,14 @@ class Chatter:
         
         conversation = await self._get_conversation(ctx, channel)
         
-        await ctx.send("Gather successful! Training begins now")
         if not conversation:
             await ctx.send("Failed to gather training data")
             return
-
+            
-        if await self._train(conversation):
+        await ctx.send("Gather successful! Training begins now\n(**This will take a long time, be patient**)")
+        future = await self.loop.run_in_executor(None, self._train, conversation)
+        
+        if future:
             await ctx.send("Training successful!")
         else:
             await ctx.send("Error occurred :(")

chatter/source/__init__.py

@@ -0,0 +1,13 @@
+"""
+ChatterBot is a machine learning, conversational dialog engine.
+"""
+from .chatterbot import ChatBot
+
+__version__ = '0.8.4'
+__author__ = 'Gunther Cox'
+__email__ = 'gunthercx@gmail.com'
+__url__ = 'https://github.com/gunthercox/ChatterBot'
+
+__all__ = (
+    'ChatBot',
+)

chatter/source/__main__.py

@@ -0,0 +1,23 @@
+import sys
+
+
+if __name__ == '__main__':
+    import importlib
+
+    if '--version' in sys.argv:
+        chatterbot = importlib.import_module('chatterbot')
+        print(chatterbot.__version__)
+
+    if 'list_nltk_data' in sys.argv:
+        import os
+        import nltk.data
+
+        data_directories = []
+
+        # Find each data directory in the NLTK path that has content
+        for path in nltk.data.path:
+            if os.path.exists(path):
+                if os.listdir(path):
+                    data_directories.append(path)
+
+        print(os.linesep.join(data_directories))

chatter/source/adapters.py

@@ -0,0 +1,47 @@
+import logging
+
+
+class Adapter(object):
+    """
+    A superclass for all adapter classes.
+
+    :param logger: A python logger.
+    """
+
+    def __init__(self, **kwargs):
+        self.logger = kwargs.get('logger', logging.getLogger(__name__))
+        self.chatbot = kwargs.get('chatbot')
+
+    def set_chatbot(self, chatbot):
+        """
+        Gives the adapter access to an instance of the ChatBot class.
+
+        :param chatbot: A chat bot instanse.
+        :type chatbot: ChatBot
+        """
+        self.chatbot = chatbot
+
+    class AdapterMethodNotImplementedError(NotImplementedError):
+        """
+        An exception to be raised when an adapter method has not been implemented.
+        Typically this indicates that the developer is expected to implement the
+        method in a subclass.
+        """
+
+        def __init__(self, message=None):
+            """
+            Set the message for the esception.
+            """
+            if not message:
+                message = 'This method must be overridden in a subclass method.'
+            self.message = message
+
+        def __str__(self):
+            return self.message
+
+    class InvalidAdapterTypeException(Exception):
+        """
+        An exception to be raised when an adapter
+        of an unexpected class type is received.
+        """
+        pass

chatter/source/chatterbot.py

@@ -0,0 +1,173 @@
+from __future__ import unicode_literals
+import logging
+from .storage import StorageAdapter
+from .input import InputAdapter
+from .output import OutputAdapter
+from . import utils
+
+
+class ChatBot(object):
+    """
+    A conversational dialog chat bot.
+    """
+
+    def __init__(self, name, **kwargs):
+        from .logic import MultiLogicAdapter
+
+        self.name = name
+        kwargs['name'] = name
+        kwargs['chatbot'] = self
+
+        self.default_session = None
+
+        storage_adapter = kwargs.get('storage_adapter', 'chatter.source.storage.SQLStorageAdapter')
+
+        logic_adapters = kwargs.get('logic_adapters', [
+            'chatter.source.logic.BestMatch'
+        ])
+
+        input_adapter = kwargs.get('input_adapter', 'chatter.source.input.VariableInputTypeAdapter')
+
+        output_adapter = kwargs.get('output_adapter', 'chatter.source.output.OutputAdapter')
+
+        # Check that each adapter is a valid subclass of it's respective parent
+        utils.validate_adapter_class(storage_adapter, StorageAdapter)
+        utils.validate_adapter_class(input_adapter, InputAdapter)
+        utils.validate_adapter_class(output_adapter, OutputAdapter)
+
+        self.logic = MultiLogicAdapter(**kwargs)
+        self.storage = utils.initialize_class(storage_adapter, **kwargs)
+        self.input = utils.initialize_class(input_adapter, **kwargs)
+        self.output = utils.initialize_class(output_adapter, **kwargs)
+
+        filters = kwargs.get('filters', tuple())
+        self.filters = tuple([utils.import_module(F)() for F in filters])
+
+        # Add required system logic adapter
+        self.logic.system_adapters.append(
+            utils.initialize_class('chatter.source.logic.NoKnowledgeAdapter', **kwargs)
+        )
+
+        for adapter in logic_adapters:
+            self.logic.add_adapter(adapter, **kwargs)
+
+        # Add the chatbot instance to each adapter to share information such as
+        # the name, the current conversation, or other adapters
+        self.logic.set_chatbot(self)
+        self.input.set_chatbot(self)
+        self.output.set_chatbot(self)
+
+        preprocessors = kwargs.get(
+            'preprocessors', [
+                'chatter.source.preprocessors.clean_whitespace'
+            ]
+        )
+
+        self.preprocessors = []
+
+        for preprocessor in preprocessors:
+            self.preprocessors.append(utils.import_module(preprocessor))
+
+        # Use specified trainer or fall back to the default
+        trainer = kwargs.get('trainer', 'chatter.source.trainers.Trainer')
+        TrainerClass = utils.import_module(trainer)
+        self.trainer = TrainerClass(self.storage, **kwargs)
+        self.training_data = kwargs.get('training_data')
+
+        self.default_conversation_id = None
+
+        self.logger = kwargs.get('logger', logging.getLogger(__name__))
+
+        # Allow the bot to save input it receives so that it can learn
+        self.read_only = kwargs.get('read_only', False)
+
+        if kwargs.get('initialize', True):
+            self.initialize()
+
+    def initialize(self):
+        """
+        Do any work that needs to be done before the responses can be returned.
+        """
+        self.logic.initialize()
+
+    def get_response(self, input_item, conversation_id=None):
+        """
+        Return the bot's response based on the input.
+
+        :param input_item: An input value.
+        :param conversation_id: The id of a conversation.
+        :returns: A response to the input.
+        :rtype: Statement
+        """
+        if not conversation_id:
+            if not self.default_conversation_id:
+                self.default_conversation_id = self.storage.create_conversation()
+            conversation_id = self.default_conversation_id
+
+        input_statement = self.input.process_input_statement(input_item)
+
+        # Preprocess the input statement
+        for preprocessor in self.preprocessors:
+            input_statement = preprocessor(self, input_statement)
+
+        statement, response = self.generate_response(input_statement, conversation_id)
+
+        # Learn that the user's input was a valid response to the chat bot's previous output
+        previous_statement = self.storage.get_latest_response(conversation_id)
+
+        if not self.read_only:
+            self.learn_response(statement, previous_statement)
+            self.storage.add_to_conversation(conversation_id, statement, response)
+
+        # Process the response output with the output adapter
+        return self.output.process_response(response, conversation_id)
+
+    def generate_response(self, input_statement, conversation_id):
+        """
+        Return a response based on a given input statement.
+        """
+        self.storage.generate_base_query(self, conversation_id)
+
+        # Select a response to the input statement
+        response = self.logic.process(input_statement)
+
+        return input_statement, response
+
+    def learn_response(self, statement, previous_statement):
+        """
+        Learn that the statement provided is a valid response.
+        """
+        from .conversation import Response
+
+        if previous_statement:
+            statement.add_response(
+                Response(previous_statement.text)
+            )
+            self.logger.info('Adding "{}" as a response to "{}"'.format(
+                statement.text,
+                previous_statement.text
+            ))
+
+        # Save the statement after selecting a response
+        self.storage.update(statement)
+
+    def set_trainer(self, training_class, **kwargs):
+        """
+        Set the module used to train the chatbot.
+
+        :param training_class: The training class to use for the chat bot.
+        :type training_class: `Trainer`
+
+        :param \**kwargs: Any parameters that should be passed to the training class.
+        """
+        if 'chatbot' not in kwargs:
+            kwargs['chatbot'] = self
+
+        self.trainer = training_class(self.storage, **kwargs)
+
+    @property
+    def train(self):
+        """
+        Proxy method to the chat bot's trainer class.
+        """
+        return self.trainer.train

chatter/source/comparisons.py

@@ -0,0 +1,331 @@
+# -*- coding: utf-8 -*-
+import sys
+
+
+"""
+This module contains various text-comparison algorithms
+designed to compare one statement to another.
+"""
+
+# Use python-Levenshtein if available
+try:
+    from Levenshtein.StringMatcher import StringMatcher as SequenceMatcher
+except ImportError:
+    from difflib import SequenceMatcher
+
+
+class Comparator:
+
+    def __call__(self, statement_a, statement_b):
+        return self.compare(statement_a, statement_b)
+
+    def compare(self, statement_a, statement_b):
+        return 0
+
+    def get_initialization_functions(self):
+        """
+        Return all initialization methods for the comparison algorithm.
+        Initialization methods must start with 'initialize_' and
+        take no parameters.
+        """
+        initialization_methods = [
+            (
+                method,
+                getattr(self, method),
+            ) for method in dir(self) if method.startswith('initialize_')
+        ]
+
+        return {
+            key: value for (key, value) in initialization_methods
+        }
+
+
+class LevenshteinDistance(Comparator):
+    """
+    Compare two statements based on the Levenshtein distance
+    of each statement's text.
+
+    For example, there is a 65% similarity between the statements
+    "where is the post office?" and "looking for the post office"
+    based on the Levenshtein distance algorithm.
+    """
+
+    def compare(self, statement, other_statement):
+        """
+        Compare the two input statements.
+
+        :return: The percent of similarity between the text of the statements.
+        :rtype: float
+        """
+
+        PYTHON = sys.version_info[0]
+
+        # Return 0 if either statement has a falsy text value
+        if not statement.text or not other_statement.text:
+            return 0
+
+        # Get the lowercase version of both strings
+        if PYTHON < 3:
+            statement_text = unicode(statement.text.lower()) # NOQA
+            other_statement_text = unicode(other_statement.text.lower()) # NOQA
+        else:
+            statement_text = str(statement.text.lower())
+            other_statement_text = str(other_statement.text.lower())
+
+        similarity = SequenceMatcher(
+            None,
+            statement_text,
+            other_statement_text
+        )
+
+        # Calculate a decimal percent of the similarity
+        percent = round(similarity.ratio(), 2)
+
+        return percent
+
+
+class SynsetDistance(Comparator):
+    """
+    Calculate the similarity of two statements.
+    This is based on the total maximum synset similarity between each word in each sentence.
+
+    This algorithm uses the `wordnet`_ functionality of `NLTK`_ to determine the similarity
+    of two statements based on the path similarity between each token of each statement.
+    This is essentially an evaluation of the closeness of synonyms.
+    """
+
+    def initialize_nltk_wordnet(self):
+        """
+        Download required NLTK corpora if they have not already been downloaded.
+        """
+        from .utils import nltk_download_corpus
+
+        nltk_download_corpus('corpora/wordnet')
+
+    def initialize_nltk_punkt(self):
+        """
+        Download required NLTK corpora if they have not already been downloaded.
+        """
+        from .utils import nltk_download_corpus
+
+        nltk_download_corpus('tokenizers/punkt')
+
+    def initialize_nltk_stopwords(self):
+        """
+        Download required NLTK corpora if they have not already been downloaded.
+        """
+        from .utils import nltk_download_corpus
+
+        nltk_download_corpus('corpora/stopwords')
+
+    def compare(self, statement, other_statement):
+        """
+        Compare the two input statements.
+
+        :return: The percent of similarity between the closest synset distance.
+        :rtype: float
+
+        .. _wordnet: http://www.nltk.org/howto/wordnet.html
+        .. _NLTK: http://www.nltk.org/
+        """
+        from nltk.corpus import wordnet
+        from nltk import word_tokenize
+        from . import utils
+        import itertools
+
+        tokens1 = word_tokenize(statement.text.lower())
+        tokens2 = word_tokenize(other_statement.text.lower())
+
+        # Remove all stop words from the list of word tokens
+        tokens1 = utils.remove_stopwords(tokens1, language='english')
+        tokens2 = utils.remove_stopwords(tokens2, language='english')
+
+        # The maximum possible similarity is an exact match
+        # Because path_similarity returns a value between 0 and 1,
+        # max_possible_similarity is the number of words in the longer
+        # of the two input statements.
+        max_possible_similarity = max(
+            len(statement.text.split()),
+            len(other_statement.text.split())
+        )
+
+        max_similarity = 0.0
+
+        # Get the highest matching value for each possible combination of words
+        for combination in itertools.product(*[tokens1, tokens2]):
+
+            synset1 = wordnet.synsets(combination[0])
+            synset2 = wordnet.synsets(combination[1])
+
+            if synset1 and synset2:
+
+                # Get the highest similarity for each combination of synsets
+                for synset in itertools.product(*[synset1, synset2]):
+                    similarity = synset[0].path_similarity(synset[1])
+
+                    if similarity and (similarity > max_similarity):
+                        max_similarity = similarity
+
+        if max_possible_similarity == 0:
+            return 0
+
+        return max_similarity / max_possible_similarity
+
+
+class SentimentComparison(Comparator):
+    """
+    Calculate the similarity of two statements based on the closeness of
+    the sentiment value calculated for each statement.
+    """
+
+    def initialize_nltk_vader_lexicon(self):
+        """
+        Download the NLTK vader lexicon for sentiment analysis
+        that is required for this algorithm to run.
+        """
+        from .utils import nltk_download_corpus
+
+        nltk_download_corpus('sentiment/vader_lexicon')
+
+    def compare(self, statement, other_statement):
+        """
+        Return the similarity of two statements based on
+        their calculated sentiment values.
+
+        :return: The percent of similarity between the sentiment value.
+        :rtype: float
+        """
+        from nltk.sentiment.vader import SentimentIntensityAnalyzer
+
+        sentiment_analyzer = SentimentIntensityAnalyzer()
+        statement_polarity = sentiment_analyzer.polarity_scores(statement.text.lower())
+        statement2_polarity = sentiment_analyzer.polarity_scores(other_statement.text.lower())
+
+        statement_greatest_polarity = 'neu'
+        statement_greatest_score = -1
+        for polarity in sorted(statement_polarity):
+            if statement_polarity[polarity] > statement_greatest_score:
+                statement_greatest_polarity = polarity
+                statement_greatest_score = statement_polarity[polarity]
+
+        statement2_greatest_polarity = 'neu'
+        statement2_greatest_score = -1
+        for polarity in sorted(statement2_polarity):
+            if statement2_polarity[polarity] > statement2_greatest_score:
+                statement2_greatest_polarity = polarity
+                statement2_greatest_score = statement2_polarity[polarity]
+
+        # Check if the polarity if of a different type
+        if statement_greatest_polarity != statement2_greatest_polarity:
+            return 0
+
+        values = [statement_greatest_score, statement2_greatest_score]
+        difference = max(values) - min(values)
+
+        return 1.0 - difference
+
+
+class JaccardSimilarity(Comparator):
+    """
+    Calculates the similarity of two statements based on the Jaccard index.
+
+    The Jaccard index is composed of a numerator and denominator.
+    In the numerator, we count the number of items that are shared between the sets.
+    In the denominator, we count the total number of items across both sets.
+    Let's say we define sentences to be equivalent if 50% or more of their tokens are equivalent.
+    Here are two sample sentences:
+
+        The young cat is hungry.
+        The cat is very hungry.
+
+    When we parse these sentences to remove stopwords, we end up with the following two sets:
+
+        {young, cat, hungry}
+        {cat, very, hungry}
+
+    In our example above, our intersection is {cat, hungry}, which has count of two.
+    The union of the sets is {young, cat, very, hungry}, which has a count of four.
+    Therefore, our `Jaccard similarity index`_ is two divided by four, or 50%.
+    Given our similarity threshold above, we would consider this to be a match.
+
+    .. _`Jaccard similarity index`: https://en.wikipedia.org/wiki/Jaccard_index
+    """
+
+    SIMILARITY_THRESHOLD = 0.5
+
+    def initialize_nltk_wordnet(self):
+        """
+        Download the NLTK wordnet corpora that is required for this algorithm
+        to run only if the corpora has not already been downloaded.
+        """
+        from .utils import nltk_download_corpus
+
+        nltk_download_corpus('corpora/wordnet')
+
+    def compare(self, statement, other_statement):
+        """
+        Return the calculated similarity of two
+        statements based on the Jaccard index.
+        """
+        from nltk.corpus import wordnet
+        import nltk
+        import string
+
+        a = statement.text.lower()
+        b = other_statement.text.lower()
+
+        # Get default English stopwords and extend with punctuation
+        stopwords = nltk.corpus.stopwords.words('english')
+        stopwords.extend(string.punctuation)
+        stopwords.append('')
+        lemmatizer = nltk.stem.wordnet.WordNetLemmatizer()
+
+        def get_wordnet_pos(pos_tag):
+            if pos_tag[1].startswith('J'):
+                return (pos_tag[0], wordnet.ADJ)
+            elif pos_tag[1].startswith('V'):
+                return (pos_tag[0], wordnet.VERB)
+            elif pos_tag[1].startswith('N'):
+                return (pos_tag[0], wordnet.NOUN)
+            elif pos_tag[1].startswith('R'):
+                return (pos_tag[0], wordnet.ADV)
+            else:
+                return (pos_tag[0], wordnet.NOUN)
+
+        ratio = 0
+        pos_a = map(get_wordnet_pos, nltk.pos_tag(nltk.tokenize.word_tokenize(a)))
+        pos_b = map(get_wordnet_pos, nltk.pos_tag(nltk.tokenize.word_tokenize(b)))
+        lemma_a = [
+            lemmatizer.lemmatize(
+                token.strip(string.punctuation),
+                pos
+            ) for token, pos in pos_a if pos == wordnet.NOUN and token.strip(
+                string.punctuation
+            ) not in stopwords
+        ]
+        lemma_b = [
+            lemmatizer.lemmatize(
+                token.strip(string.punctuation),
+                pos
+            ) for token, pos in pos_b if pos == wordnet.NOUN and token.strip(
+                string.punctuation
+            ) not in stopwords
+        ]
+
+        # Calculate Jaccard similarity
+        try:
+            numerator = len(set(lemma_a).intersection(lemma_b))
+            denominator = float(len(set(lemma_a).union(lemma_b)))
+            ratio = numerator / denominator
+        except Exception as e:
+            print('Error', e)
+        return ratio >= self.SIMILARITY_THRESHOLD
+
+
+# ---------------------------------------- #
+
+
+levenshtein_distance = LevenshteinDistance()
+synset_distance = SynsetDistance()
+sentiment_comparison = SentimentComparison()
+jaccard_similarity = JaccardSimilarity()

chatter/source/constants.py

@@ -0,0 +1,15 @@
+"""
+ChatterBot constants
+"""
+
+'''
+The maximum length of characters that the text of a statement can contain.
+This should be enforced on a per-model basis by the data model for each
+storage adapter.
+'''
+STATEMENT_TEXT_MAX_LENGTH = 400
+
+# The maximum length of characters that the name of a tag can contain
+TAG_NAME_MAX_LENGTH = 50
+
+DEFAULT_DJANGO_APP_NAME = 'django_chatterbot'

chatter/source/conversation.py

@@ -0,0 +1,229 @@
+class StatementMixin(object):
+    """
+    This class has shared methods used to
+    normalize different statement models.
+    """
+
+    def get_tags(self):
+        """
+        Return the list of tags for this statement.
+        """
+        return self.tags
+
+    def add_tags(self, tags):
+        """
+        Add a list of strings to the statement as tags.
+        """
+        for tag in tags:
+            self.tags.append(tag)
+
+
+class Statement(StatementMixin):
+    """
+    A statement represents a single spoken entity, sentence or
+    phrase that someone can say.
+    """
+
+    def __init__(self, text, **kwargs):
+        import sys
+
+        # Try not to allow non-string types to be passed to statements
+        try:
+            text = str(text)
+        except UnicodeEncodeError:
+            pass
+
+        # Prefer decoded utf8-strings in Python 2.7
+        if sys.version_info[0] < 3:
+            try:
+                text = text.decode('utf-8')
+            except UnicodeEncodeError:
+                pass
+
+        self.text = text
+        self.tags = kwargs.pop('tags', [])
+        self.in_response_to = kwargs.pop('in_response_to', [])
+
+        self.extra_data = kwargs.pop('extra_data', {})
+
+        # This is the confidence with which the chat bot believes
+        # this is an accurate response. This value is set when the
+        # statement is returned by the chat bot.
+        self.confidence = 0
+
+        self.storage = None
+
+    def __str__(self):
+        return self.text
+
+    def __repr__(self):
+        return '<Statement text:%s>' % (self.text)
+
+    def __hash__(self):
+        return hash(self.text)
+
+    def __eq__(self, other):
+        if not other:
+            return False
+
+        if isinstance(other, Statement):
+            return self.text == other.text
+
+        return self.text == other
+
+    def save(self):
+        """
+        Save the statement in the database.
+        """
+        self.storage.update(self)
+
+    def add_extra_data(self, key, value):
+        """
+        This method allows additional data to be stored on the statement object.
+
+        Typically this data is something that pertains just to this statement.
+        For example, a value stored here might be the tagged parts of speech for
+        each word in the statement text.
+
+            - key = 'pos_tags'
+            - value = [('Now', 'RB'), ('for', 'IN'), ('something', 'NN'), ('different', 'JJ')]
+
+        :param key: The key to use in the dictionary of extra data.
+        :type key: str
+
+        :param value: The value to set for the specified key.
+        """
+        self.extra_data[key] = value
+
+    def add_response(self, response):
+        """
+        Add the response to the list of statements that this statement is in response to.
+        If the response is already in the list, increment the occurrence count of that response.
+
+        :param response: The response to add.
+        :type response: `Response`
+        """
+        if not isinstance(response, Response):
+            raise Statement.InvalidTypeException(
+                'A {} was received when a {} instance was expected'.format(
+                    type(response),
+                    type(Response(''))
+                )
+            )
+
+        updated = False
+        for index in range(0, len(self.in_response_to)):
+            if response.text == self.in_response_to[index].text:
+                self.in_response_to[index].occurrence += 1
+                updated = True
+
+        if not updated:
+            self.in_response_to.append(response)
+
+    def remove_response(self, response_text):
+        """
+        Removes a response from the statement's response list based
+        on the value of the response text.
+
+        :param response_text: The text of the response to be removed.
+        :type response_text: str
+        """
+        for response in self.in_response_to:
+            if response_text == response.text:
+                self.in_response_to.remove(response)
+                return True
+        return False
+
+    def get_response_count(self, statement):
+        """
+        Find the number of times that the statement has been used
+        as a response to the current statement.
+
+        :param statement: The statement object to get the count for.
+        :type statement: `Statement`
+
+        :returns: Return the number of times the statement has been used as a response.
+        :rtype: int
+        """
+        for response in self.in_response_to:
+            if statement.text == response.text:
+                return response.occurrence
+
+        return 0
+
+    def serialize(self):
+        """
+        :returns: A dictionary representation of the statement object.
+        :rtype: dict
+        """
+        data = {}
+
+        data['text'] = self.text
+        data['in_response_to'] = []
+        data['extra_data'] = self.extra_data
+
+        for response in self.in_response_to:
+            data['in_response_to'].append(response.serialize())
+
+        return data
+
+    @property
+    def response_statement_cache(self):
+        """
+        This property is to allow ChatterBot Statement objects to
+        be swappable with Django Statement models.
+        """
+        return self.in_response_to
+
+    class InvalidTypeException(Exception):
+
+        def __init__(self, value='Received an unexpected value type.'):
+            self.value = value
+
+        def __str__(self):
+            return repr(self.value)
+
+
+class Response(object):
+    """
+    A response represents an entity which response to a statement.
+    """
+
+    def __init__(self, text, **kwargs):
+        from datetime import datetime
+        from dateutil import parser as date_parser
+
+        self.text = text
+        self.created_at = kwargs.get('created_at', datetime.now())
+        self.occurrence = kwargs.get('occurrence', 1)
+
+        if not isinstance(self.created_at, datetime):
+            self.created_at = date_parser.parse(self.created_at)
+
+    def __str__(self):
+        return self.text
+
+    def __repr__(self):
+        return '<Response text:%s>' % (self.text)
+
+    def __hash__(self):
+        return hash(self.text)
+
+    def __eq__(self, other):
+        if not other:
+            return False
+
+        if isinstance(other, Response):
+            return self.text == other.text
+
+        return self.text == other
+
+    def serialize(self):
+        data = {}
+
+        data['text'] = self.text
+        data['created_at'] = self.created_at.isoformat()
+
+        data['occurrence'] = self.occurrence
+
+        return data

chatter/source/corpus.py

@@ -0,0 +1,11 @@
+"""
+Seamlessly import the external chatterbot corpus module.
+View the corpus on GitHub at https://github.com/gunthercox/chatterbot-corpus
+"""
+
+from chatterbot_corpus import Corpus
+
+
+__all__ = (
+    'Corpus',
+)

chatter/source/ext/django_chatterbot/__init__.py

@@ -0,0 +1,3 @@
+default_app_config = (
+    'chatter.source.ext.django_chatterbot.apps.DjangoChatterBotConfig'
+)

chatter/source/ext/django_chatterbot/abstract_models.py

@@ -0,0 +1,261 @@
+from ...conversation import StatementMixin
+from ... import constants
+from django.db import models
+from django.apps import apps
+from django.utils import timezone
+from django.conf import settings
+
+
+DJANGO_APP_NAME = constants.DEFAULT_DJANGO_APP_NAME
+STATEMENT_MODEL = 'Statement'
+RESPONSE_MODEL = 'Response'
+
+if hasattr(settings, 'CHATTERBOT'):
+    """
+    Allow related models to be overridden in the project settings.
+    Default to the original settings if one is not defined.
+    """
+    DJANGO_APP_NAME = settings.CHATTERBOT.get(
+        'django_app_name',
+        DJANGO_APP_NAME
+    )
+    STATEMENT_MODEL = settings.CHATTERBOT.get(
+        'statement_model',
+        STATEMENT_MODEL
+    )
+    RESPONSE_MODEL = settings.CHATTERBOT.get(
+        'response_model',
+        RESPONSE_MODEL
+    )
+
+
+class AbstractBaseStatement(models.Model, StatementMixin):
+    """
+    The abstract base statement allows other models to
+    be created using the attributes that exist on the
+    default models.
+    """
+
+    text = models.CharField(
+        unique=True,
+        blank=False,
+        null=False,
+        max_length=constants.STATEMENT_TEXT_MAX_LENGTH
+    )
+
+    extra_data = models.CharField(
+        max_length=500,
+        blank=True
+    )
+
+    # This is the confidence with which the chat bot believes
+    # this is an accurate response. This value is set when the
+    # statement is returned by the chat bot.
+    confidence = 0
+
+    class Meta:
+        abstract = True
+
+    def __str__(self):
+        if len(self.text.strip()) > 60:
+            return '{}...'.format(self.text[:57])
+        elif len(self.text.strip()) > 0:
+            return self.text
+        return '<empty>'
+
+    def __init__(self, *args, **kwargs):
+        super(AbstractBaseStatement, self).__init__(*args, **kwargs)
+
+        # Responses to be saved if the statement is updated with the storage adapter
+        self.response_statement_cache = []
+
+    @property
+    def in_response_to(self):
+        """
+        Return the response objects that are for this statement.
+        """
+        ResponseModel = apps.get_model(DJANGO_APP_NAME, RESPONSE_MODEL)
+        return ResponseModel.objects.filter(statement=self)
+
+    def add_extra_data(self, key, value):
+        """
+        Add extra data to the extra_data field.
+        """
+        import json
+
+        if not self.extra_data:
+            self.extra_data = '{}'
+
+        extra_data = json.loads(self.extra_data)
+        extra_data[key] = value
+
+        self.extra_data = json.dumps(extra_data)
+
+    def add_tags(self, tags):
+        """
+        Add a list of strings to the statement as tags.
+        (Overrides the method from StatementMixin)
+        """
+        for tag in tags:
+            self.tags.create(
+                name=tag
+            )
+
+    def add_response(self, statement):
+        """
+        Add a response to this statement.
+        """
+        self.response_statement_cache.append(statement)
+
+    def remove_response(self, response_text):
+        """
+        Removes a response from the statement's response list based
+        on the value of the response text.
+
+        :param response_text: The text of the response to be removed.
+        :type response_text: str
+        """
+        is_deleted = False
+        response = self.in_response.filter(response__text=response_text)
+
+        if response.exists():
+            is_deleted = True
+
+        return is_deleted
+
+    def get_response_count(self, statement):
+        """
+        Find the number of times that the statement has been used
+        as a response to the current statement.
+
+        :param statement: The statement object to get the count for.
+        :type statement: chatterbot.conversation.Statement
+
+        :returns: Return the number of times the statement has been used as a response.
+        :rtype: int
+        """
+        return self.in_response.filter(response__text=statement.text).count()
+
+    def serialize(self):
+        """
+        :returns: A dictionary representation of the statement object.
+        :rtype: dict
+        """
+        import json
+        data = {}
+
+        if not self.extra_data:
+            self.extra_data = '{}'
+
+        data['text'] = self.text
+        data['in_response_to'] = []
+        data['extra_data'] = json.loads(self.extra_data)
+
+        for response in self.in_response.all():
+            data['in_response_to'].append(response.serialize())
+
+        return data
+
+
+class AbstractBaseResponse(models.Model):
+    """
+    The abstract base response allows other models to
+    be created using the attributes that exist on the
+    default models.
+    """
+
+    statement = models.ForeignKey(
+        STATEMENT_MODEL,
+        related_name='in_response',
+        on_delete=models.CASCADE
+    )
+
+    response = models.ForeignKey(
+        STATEMENT_MODEL,
+        related_name='responses',
+        on_delete=models.CASCADE
+    )
+
+    created_at = models.DateTimeField(
+        default=timezone.now,
+        help_text='The date and time that this response was created at.'
+    )
+
+    class Meta:
+        abstract = True
+
+    @property
+    def occurrence(self):
+        """
+        Return a count of the number of times this response has occurred.
+        """
+        ResponseModel = apps.get_model(DJANGO_APP_NAME, RESPONSE_MODEL)
+
+        return ResponseModel.objects.filter(
+            statement__text=self.statement.text,
+            response__text=self.response.text
+        ).count()
+
+    def __str__(self):
+        statement = self.statement.text
+        response = self.response.text
+        return '{} => {}'.format(
+            statement if len(statement) <= 20 else statement[:17] + '...',
+            response if len(response) <= 40 else response[:37] + '...'
+        )
+
+    def serialize(self):
+        """
+        :returns: A dictionary representation of the statement object.
+        :rtype: dict
+        """
+        data = {}
+
+        data['text'] = self.response.text
+        data['created_at'] = self.created_at.isoformat()
+        data['occurrence'] = self.occurrence
+
+        return data
+
+
+class AbstractBaseConversation(models.Model):
+    """
+    The abstract base conversation allows other models to
+    be created using the attributes that exist on the
+    default models.
+    """
+
+    responses = models.ManyToManyField(
+        RESPONSE_MODEL,
+        related_name='conversations',
+        help_text='The responses in this conversation.'
+    )
+
+    class Meta:
+        abstract = True
+
+    def __str__(self):
+        return str(self.id)
+
+
+class AbstractBaseTag(models.Model):
+    """
+    The abstract base tag allows other models to
+    be created using the attributes that exist on the
+    default models.
+    """
+
+    name = models.SlugField(
+        max_length=constants.TAG_NAME_MAX_LENGTH
+    )
+
+    statements = models.ManyToManyField(
+        STATEMENT_MODEL,
+        related_name='tags'
+    )
+
+    class Meta:
+        abstract = True
+
+    def __str__(self):
+        return self.name

chatter/source/ext/django_chatterbot/admin.py

@@ -0,0 +1,31 @@
+from django.contrib import admin
+from .models import (
+    Statement, Response, Conversation, Tag
+)
+
+
+class StatementAdmin(admin.ModelAdmin):
+    list_display = ('text', )
+    list_filter = ('text', )
+    search_fields = ('text', )
+
+
+class ResponseAdmin(admin.ModelAdmin):
+    list_display = ('statement', 'response', 'occurrence', )
+    search_fields = ['statement__text', 'response__text']
+
+
+class ConversationAdmin(admin.ModelAdmin):
+    list_display = ('id', )
+
+
+class TagAdmin(admin.ModelAdmin):
+    list_display = ('name', )
+    list_filter = ('name', )
+    search_fields = ('name', )
+
+
+admin.site.register(Statement, StatementAdmin)
+admin.site.register(Response, ResponseAdmin)
+admin.site.register(Conversation, ConversationAdmin)
+admin.site.register(Tag, TagAdmin)

chatter/source/ext/django_chatterbot/apps.py

@@ -0,0 +1,8 @@
+from django.apps import AppConfig
+
+
+class DjangoChatterBotConfig(AppConfig):
+
+    name = 'chatter.source.ext.django_chatterbot'
+    label = 'django_chatterbot'
+    verbose_name = 'Django ChatterBot'

chatter/source/ext/django_chatterbot/factories.py

@@ -0,0 +1,42 @@
+"""
+These factories are used to generate fake data for testing.
+"""
+import factory
+from . import models
+from ... import constants
+from factory.django import DjangoModelFactory
+
+
+class StatementFactory(DjangoModelFactory):
+
+    text = factory.Faker(
+        'text',
+        max_nb_chars=constants.STATEMENT_TEXT_MAX_LENGTH
+    )
+
+    class Meta:
+        model = models.Statement
+
+
+class ResponseFactory(DjangoModelFactory):
+
+    statement = factory.SubFactory(StatementFactory)
+
+    response = factory.SubFactory(StatementFactory)
+
+    class Meta:
+        model = models.Response
+
+
+class ConversationFactory(DjangoModelFactory):
+
+    class Meta:
+        model = models.Conversation
+
+
+class TagFactory(DjangoModelFactory):
+
+    name = factory.Faker('word')
+
+    class Meta:
+        model = models.Tag

chatter/source/ext/django_chatterbot/management/commands/train.py

@@ -0,0 +1,29 @@
+from django.core.management.base import BaseCommand
+
+
+class Command(BaseCommand):
+    """
+    A Django management command for calling a
+    chat bot's training method.
+    """
+
+    help = 'Trains the database used by the chat bot'
+    can_import_settings = True
+
+    def handle(self, *args, **options):
+        from ..... import ChatBot
+        from ... import settings
+
+        chatterbot = ChatBot(**settings.CHATTERBOT)
+
+        chatterbot.train(chatterbot.training_data)
+
+        # Django 1.8 does not define SUCCESS
+        if hasattr(self.style, 'SUCCESS'):
+            style = self.style.SUCCESS
+        else:
+            style = self.style.NOTICE
+
+        self.stdout.write(style('Starting training...'))
+        training_class = chatterbot.trainer.__class__.__name__
+        self.stdout.write(style('ChatterBot trained using "%s"' % training_class))

chatter/source/ext/django_chatterbot/migrations/0001_initial.py

@@ -0,0 +1,39 @@
+# -*- coding: utf-8 -*-
+from __future__ import unicode_literals
+
+from django.db import migrations, models
+import django.db.models.deletion
+
+
+class Migration(migrations.Migration):
+
+    initial = True
+
+    dependencies = []
+
+    operations = [
+        migrations.CreateModel(
+            name='Response',
+            fields=[
+                ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
+                ('occurrence', models.PositiveIntegerField(default=0)),
+            ],
+        ),
+        migrations.CreateModel(
+            name='Statement',
+            fields=[
+                ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
+                ('text', models.CharField(max_length=255, unique=True)),
+            ],
+        ),
+        migrations.AddField(
+            model_name='response',
+            name='response',
+            field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to='django_chatterbot.Statement'),
+        ),
+        migrations.AddField(
+            model_name='response',
+            name='statement',
+            field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='in_response_to', to='django_chatterbot.Statement'),
+        ),
+    ]

chatter/source/ext/django_chatterbot/migrations/0002_statement_extra_data.py

@@ -0,0 +1,21 @@
+# -*- coding: utf-8 -*-
+# Generated by Django 1.10.2 on 2016-10-30 12:13
+from __future__ import unicode_literals
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('django_chatterbot', '0001_initial'),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name='statement',
+            name='extra_data',
+            field=models.CharField(default='{}', max_length=500),
+            preserve_default=False,
+        ),
+    ]

chatter/source/ext/django_chatterbot/migrations/0003_change_occurrence_default.py

@@ -0,0 +1,20 @@
+# -*- coding: utf-8 -*-
+# Generated by Django 1.9 on 2016-12-12 00:06
+from __future__ import unicode_literals
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('django_chatterbot', '0002_statement_extra_data'),
+    ]
+
+    operations = [
+        migrations.AlterField(
+            model_name='response',
+            name='occurrence',
+            field=models.PositiveIntegerField(default=1),
+        ),
+    ]

chatter/source/ext/django_chatterbot/migrations/0004_rename_in_response_to.py

@@ -0,0 +1,26 @@
+# -*- coding: utf-8 -*-
+# Generated by Django 1.10.3 on 2016-12-04 23:52
+from __future__ import unicode_literals
+
+from django.db import migrations, models
+import django.db.models.deletion
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('django_chatterbot', '0003_change_occurrence_default'),
+    ]
+
+    operations = [
+        migrations.AlterField(
+            model_name='response',
+            name='statement',
+            field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='in_response', to='django_chatterbot.Statement'),
+        ),
+        migrations.AlterField(
+            model_name='response',
+            name='response',
+            field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='responses', to='django_chatterbot.Statement'),
+        ),
+    ]

chatter/source/ext/django_chatterbot/migrations/0005_statement_created_at.py

@@ -0,0 +1,24 @@
+# -*- coding: utf-8 -*-
+# Generated by Django 1.10.1 on 2016-12-29 19:20
+from __future__ import unicode_literals
+
+from django.db import migrations, models
+import django.utils.timezone
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('django_chatterbot', '0004_rename_in_response_to'),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name='statement',
+            name='created_at',
+            field=models.DateTimeField(
+                default=django.utils.timezone.now,
+                help_text='The date and time that this statement was created at.'
+            ),
+        ),
+    ]

chatter/source/ext/django_chatterbot/migrations/0006_create_conversation.py

@@ -0,0 +1,33 @@
+# -*- coding: utf-8 -*-
+# Generated by Django 1.9 on 2017-01-17 07:02
+from __future__ import unicode_literals
+
+from django.db import migrations, models
+import django.db.models.deletion
+import django.utils.timezone
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('django_chatterbot', '0005_statement_created_at'),
+    ]
+
+    operations = [
+        migrations.CreateModel(
+            name='Conversation',
+            fields=[
+                ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
+            ],
+        ),
+        migrations.AlterField(
+            model_name='statement',
+            name='created_at',
+            field=models.DateTimeField(default=django.utils.timezone.now, help_text='The date and time that this statement was created at.'),
+        ),
+        migrations.AddField(
+            model_name='conversation',
+            name='statements',
+            field=models.ManyToManyField(help_text='The statements in this conversation.', related_name='conversation', to='django_chatterbot.Statement'),
+        ),
+    ]

chatter/source/ext/django_chatterbot/migrations/0007_response_created_at.py

@@ -0,0 +1,24 @@
+# -*- coding: utf-8 -*-
+# Generated by Django 1.11 on 2017-07-18 00:16
+from __future__ import unicode_literals
+
+from django.db import migrations, models
+import django.utils.timezone
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('django_chatterbot', '0006_create_conversation'),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name='response',
+            name='created_at',
+            field=models.DateTimeField(
+                default=django.utils.timezone.now,
+                help_text='The date and time that this response was created at.'
+            ),
+        ),
+    ]

chatter/source/ext/django_chatterbot/migrations/0008_update_conversations.py

@@ -0,0 +1,32 @@
+# -*- coding: utf-8 -*-
+# Generated by Django 1.11 on 2017-07-18 11:25
+from __future__ import unicode_literals
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('django_chatterbot', '0007_response_created_at'),
+    ]
+
+    operations = [
+        migrations.RemoveField(
+            model_name='conversation',
+            name='statements',
+        ),
+        migrations.RemoveField(
+            model_name='response',
+            name='occurrence',
+        ),
+        migrations.RemoveField(
+            model_name='statement',
+            name='created_at',
+        ),
+        migrations.AddField(
+            model_name='conversation',
+            name='responses',
+            field=models.ManyToManyField(help_text='The responses in this conversation.', related_name='conversations', to='django_chatterbot.Response'),
+        ),
+    ]

chatter/source/ext/django_chatterbot/migrations/0009_tags.py

@@ -0,0 +1,35 @@
+# -*- coding: utf-8 -*-
+# Generated by Django 1.11a1 on 2017-07-07 00:12
+from __future__ import unicode_literals
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('django_chatterbot', '0008_update_conversations'),
+    ]
+
+    operations = [
+        migrations.CreateModel(
+            name='Tag',
+            fields=[
+                ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
+                ('name', models.SlugField()),
+            ],
+            options={
+                'abstract': False,
+            },
+        ),
+        migrations.AlterField(
+            model_name='statement',
+            name='text',
+            field=models.CharField(max_length=255, unique=True),
+        ),
+        migrations.AddField(
+            model_name='tag',
+            name='statements',
+            field=models.ManyToManyField(related_name='tags', to='django_chatterbot.Statement'),
+        ),
+    ]

chatter/source/ext/django_chatterbot/migrations/0010_statement_text.py

@@ -0,0 +1,20 @@
+# -*- coding: utf-8 -*-
+# Generated by Django 1.11.4 on 2017-08-16 00:56
+from __future__ import unicode_literals
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('django_chatterbot', '0009_tags'),
+    ]
+
+    operations = [
+        migrations.AlterField(
+            model_name='statement',
+            name='text',
+            field=models.CharField(max_length=400, unique=True),
+        ),
+    ]

chatter/source/ext/django_chatterbot/migrations/0011_blank_extra_data.py

@@ -0,0 +1,20 @@
+# -*- coding: utf-8 -*-
+# Generated by Django 1.11.4 on 2017-08-20 13:55
+from __future__ import unicode_literals
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('django_chatterbot', '0010_statement_text'),
+    ]
+
+    operations = [
+        migrations.AlterField(
+            model_name='statement',
+            name='extra_data',
+            field=models.CharField(blank=True, max_length=500),
+        ),
+    ]

chatter/source/ext/django_chatterbot/models.py

@@ -0,0 +1,34 @@
+from .abstract_models import (
+    AbstractBaseConversation, AbstractBaseResponse,
+    AbstractBaseStatement, AbstractBaseTag
+)
+
+
+class Statement(AbstractBaseStatement):
+    """
+    A statement represents a single spoken entity, sentence or
+    phrase that someone can say.
+    """
+    pass
+
+
+class Response(AbstractBaseResponse):
+    """
+    A connection between a statement and anther statement
+    that response to it.
+    """
+    pass
+
+
+class Conversation(AbstractBaseConversation):
+    """
+    A sequence of statements representing a conversation.
+    """
+    pass
+
+
+class Tag(AbstractBaseTag):
+    """
+    A label that categorizes a statement.
+    """
+    pass

chatter/source/ext/django_chatterbot/settings.py

@@ -0,0 +1,19 @@
+"""
+Default ChatterBot settings for Django.
+"""
+from django.conf import settings
+from ... import constants
+
+
+CHATTERBOT_SETTINGS = getattr(settings, 'CHATTERBOT', {})
+
+CHATTERBOT_DEFAULTS = {
+    'name': 'ChatterBot',
+    'storage_adapter': 'chatter.source.storage.DjangoStorageAdapter',
+    'input_adapter': 'chatter.source.input.VariableInputTypeAdapter',
+    'output_adapter': 'chatter.source.output.OutputAdapter',
+    'django_app_name': constants.DEFAULT_DJANGO_APP_NAME
+}
+
+CHATTERBOT = CHATTERBOT_DEFAULTS.copy()
+CHATTERBOT.update(CHATTERBOT_SETTINGS)

chatter/source/ext/django_chatterbot/urls.py

@@ -0,0 +1,11 @@
+from django.conf.urls import url
+from .views import ChatterBotView
+
+
+urlpatterns = [
+    url(
+        r'^$',
+        ChatterBotView.as_view(),
+        name='chatterbot',
+    ),
+]

chatter/source/ext/django_chatterbot/views.py

@@ -0,0 +1,118 @@
+import json
+from django.views.generic import View
+from django.http import JsonResponse
+from ... import ChatBot
+from . import settings
+
+
+class ChatterBotViewMixin(object):
+    """
+    Subclass this mixin for access to the 'chatterbot' attribute.
+    """
+
+    chatterbot = ChatBot(**settings.CHATTERBOT)
+
+    def validate(self, data):
+        """
+        Validate the data recieved from the client.
+
+        * The data should contain a text attribute.
+        """
+        from django.core.exceptions import ValidationError
+
+        if 'text' not in data:
+            raise ValidationError('The attribute "text" is required.')
+
+    def get_conversation(self, request):
+        """
+        Return the conversation for the session if one exists.
+        Create a new conversation if one does not exist.
+        """
+        from .models import Conversation, Response
+
+        class Obj(object):
+            def __init__(self):
+                self.id = None
+                self.statements = []
+
+        conversation = Obj()
+
+        conversation.id = request.session.get('conversation_id', 0)
+        existing_conversation = False
+        try:
+            Conversation.objects.get(id=conversation.id)
+            existing_conversation = True
+
+        except Conversation.DoesNotExist:
+            conversation_id = self.chatterbot.storage.create_conversation()
+            request.session['conversation_id'] = conversation_id
+            conversation.id = conversation_id
+
+        if existing_conversation:
+            responses = Response.objects.filter(
+                conversations__id=conversation.id
+            )
+
+            for response in responses:
+                conversation.statements.append(response.statement.serialize())
+                conversation.statements.append(response.response.serialize())
+
+        return conversation
+
+
+class ChatterBotView(ChatterBotViewMixin, View):
+    """
+    Provide an API endpoint to interact with ChatterBot.
+    """
+
+    def post(self, request, *args, **kwargs):
+        """
+        Return a response to the statement in the posted data.
+        """
+        input_data = json.loads(request.read().decode('utf-8'))
+
+        self.validate(input_data)
+
+        conversation = self.get_conversation(request)
+
+        response = self.chatterbot.get_response(input_data, conversation.id)
+        response_data = response.serialize()
+
+        return JsonResponse(response_data, status=200)
+
+    def get(self, request, *args, **kwargs):
+        """
+        Return data corresponding to the current conversation.
+        """
+        conversation = self.get_conversation(request)
+
+        data = {
+            'detail': 'You should make a POST request to this endpoint.',
+            'name': self.chatterbot.name,
+            'conversation': conversation.statements
+        }
+
+        # Return a method not allowed response
+        return JsonResponse(data, status=405)
+
+    def patch(self, request, *args, **kwargs):
+        """
+        The patch method is not allowed for this endpoint.
+        """
+        data = {
+            'detail': 'You should make a POST request to this endpoint.'
+        }
+
+        # Return a method not allowed response
+        return JsonResponse(data, status=405)
+
+    def delete(self, request, *args, **kwargs):
+        """
+        The delete method is not allowed for this endpoint.
+        """
+        data = {
+            'detail': 'You should make a POST request to this endpoint.'
+        }
+
+        # Return a method not allowed response
+        return JsonResponse(data, status=405)

chatter/source/ext/sqlalchemy_app/models.py

@@ -0,0 +1,132 @@
+from sqlalchemy import Table, Column, Integer, DateTime, ForeignKey, PickleType
+from sqlalchemy.orm import relationship
+from sqlalchemy.sql import func
+from sqlalchemy.ext.declarative import declared_attr, declarative_base
+
+from ...constants import TAG_NAME_MAX_LENGTH, STATEMENT_TEXT_MAX_LENGTH
+from .types import UnicodeString
+from ...conversation import StatementMixin
+
+
+class ModelBase(object):
+    """
+    An augmented base class for SqlAlchemy models.
+    """
+
+    @declared_attr
+    def __tablename__(cls):
+        """
+        Return the lowercase class name as the name of the table.
+        """
+        return cls.__name__.lower()
+
+    id = Column(
+        Integer,
+        primary_key=True,
+        autoincrement=True
+    )
+
+
+Base = declarative_base(cls=ModelBase)
+
+
+tag_association_table = Table(
+    'tag_association',
+    Base.metadata,
+    Column('tag_id', Integer, ForeignKey('tag.id')),
+    Column('statement_id', Integer, ForeignKey('statement.id'))
+)
+
+
+class Tag(Base):
+    """
+    A tag that describes a statement.
+    """
+
+    name = Column(UnicodeString(TAG_NAME_MAX_LENGTH))
+
+
+class Statement(Base, StatementMixin):
+    """
+    A Statement represents a sentence or phrase.
+    """
+
+    text = Column(UnicodeString(STATEMENT_TEXT_MAX_LENGTH), unique=True)
+
+    tags = relationship(
+        'Tag',
+        secondary=lambda: tag_association_table,
+        backref='statements'
+    )
+
+    extra_data = Column(PickleType)
+
+    in_response_to = relationship(
+        'Response',
+        back_populates='statement_table'
+    )
+
+    def get_tags(self):
+        """
+        Return a list of tags for this statement.
+        """
+        return [tag.name for tag in self.tags]
+
+    def get_statement(self):
+        from ...conversation import Statement as StatementObject
+        from ...conversation import Response as ResponseObject
+
+        statement = StatementObject(
+            self.text,
+            tags=[tag.name for tag in self.tags],
+            extra_data=self.extra_data
+        )
+        for response in self.in_response_to:
+            statement.add_response(
+                ResponseObject(text=response.text, occurrence=response.occurrence)
+            )
+        return statement
+
+
+class Response(Base):
+    """
+    Response, contains responses related to a given statement.
+    """
+
+    text = Column(UnicodeString(STATEMENT_TEXT_MAX_LENGTH))
+
+    created_at = Column(
+        DateTime(timezone=True),
+        server_default=func.now()
+    )
+
+    occurrence = Column(Integer, default=1)
+
+    statement_text = Column(UnicodeString(STATEMENT_TEXT_MAX_LENGTH), ForeignKey('statement.text'))
+
+    statement_table = relationship(
+        'Statement',
+        back_populates='in_response_to',
+        cascade='all',
+        uselist=False
+    )
+
+
+conversation_association_table = Table(
+    'conversation_association',
+    Base.metadata,
+    Column('conversation_id', Integer, ForeignKey('conversation.id')),
+    Column('statement_id', Integer, ForeignKey('statement.id'))
+)
+
+
+class Conversation(Base):
+    """
+    A conversation.
+    """
+
+    statements = relationship(
+        'Statement',
+        secondary=lambda: conversation_association_table,
+        backref='conversations'
+    )

chatter/source/ext/sqlalchemy_app/types.py

@@ -0,0 +1,21 @@
+from sqlalchemy.types import TypeDecorator, Unicode
+
+
+class UnicodeString(TypeDecorator):
+    """
+    Type for unicode strings.
+    """
+
+    impl = Unicode
+
+    def process_bind_param(self, value, dialect):
+        """
+        Coerce Python bytestrings to unicode before
+        saving them to the database.
+        """
+        import sys
+
+        if sys.version_info[0] < 3:
+            if isinstance(value, str):
+                value = value.decode('utf-8')
+        return value

chatter/source/filters.py

@@ -0,0 +1,47 @@
+"""
+Filters set the base query that gets passed to the storage adapter.
+"""
+
+
+class Filter(object):
+    """
+    A base filter object from which all other
+    filters should be subclassed.
+    """
+
+    def filter_selection(self, chatterbot, conversation_id):
+        """
+        Because this is the base filter class, this method just
+        returns the storage adapter's base query. Other filters
+        are expected to override this method.
+        """
+        return chatterbot.storage.base_query
+
+
+class RepetitiveResponseFilter(Filter):
+    """
+    A filter that eliminates possibly repetitive responses to prevent
+    a chat bot from repeating statements that it has recently said.
+    """
+
+    def filter_selection(self, chatterbot, conversation_id):
+
+        text_of_recent_responses = []
+
+        # TODO: Add a larger quantity of response history
+        latest_response = chatterbot.storage.get_latest_response(conversation_id)
+        if latest_response:
+            text_of_recent_responses.append(latest_response.text)
+
+        # Return the query with no changes if there are no statements to exclude
+        if not text_of_recent_responses:
+            return super(RepetitiveResponseFilter, self).filter_selection(
+                chatterbot,
+                conversation_id
+            )
+
+        query = chatterbot.storage.base_query.statement_text_not_in(
+            text_of_recent_responses
+        )
+
+        return query

chatter/source/input/__init__.py

@@ -0,0 +1,18 @@
+from .input_adapter import InputAdapter
+from .microsoft import Microsoft
+from .gitter import Gitter
+from .hipchat import HipChat
+from .mailgun import Mailgun
+from .terminal import TerminalAdapter
+from .variable_input_type_adapter import VariableInputTypeAdapter
+
+
+__all__ = (
+    'InputAdapter',
+    'Microsoft',
+    'Gitter',
+    'HipChat',
+    'Mailgun',
+    'TerminalAdapter',
+    'VariableInputTypeAdapter',
+)

chatter/source/input/gitter.py

@@ -0,0 +1,176 @@
+from __future__ import unicode_literals
+from time import sleep
+from . import InputAdapter
+from ..conversation import Statement
+
+
+class Gitter(InputAdapter):
+    """
+    An input adapter that allows a ChatterBot instance to get
+    input statements from a Gitter room.
+    """
+
+    def __init__(self, **kwargs):
+        super(Gitter, self).__init__(**kwargs)
+
+        self.gitter_host = kwargs.get('gitter_host', 'https://api.gitter.im/v1/')
+        self.gitter_room = kwargs.get('gitter_room')
+        self.gitter_api_token = kwargs.get('gitter_api_token')
+        self.only_respond_to_mentions = kwargs.get('gitter_only_respond_to_mentions', True)
+        self.sleep_time = kwargs.get('gitter_sleep_time', 4)
+
+        authorization_header = 'Bearer {}'.format(self.gitter_api_token)
+
+        self.headers = {
+            'Authorization': authorization_header,
+            'Content-Type': 'application/json',
+            'Accept': 'application/json'
+        }
+
+        # Join the Gitter room
+        room_data = self.join_room(self.gitter_room)
+        self.room_id = room_data.get('id')
+
+        user_data = self.get_user_data()
+        self.user_id = user_data[0].get('id')
+        self.username = user_data[0].get('username')
+
+    def _validate_status_code(self, response):
+        code = response.status_code
+        if code not in [200, 201]:
+            raise self.HTTPStatusException('{} status code recieved'.format(code))
+
+    def join_room(self, room_name):
+        """
+        Join the specified Gitter room.
+        """
+        import requests
+
+        endpoint = '{}rooms'.format(self.gitter_host)
+        response = requests.post(
+            endpoint,
+            headers=self.headers,
+            json={'uri': room_name}
+        )
+        self.logger.info('{} joining room {}'.format(
+            response.status_code, endpoint
+        ))
+        self._validate_status_code(response)
+        return response.json()
+
+    def get_user_data(self):
+        import requests
+
+        endpoint = '{}user'.format(self.gitter_host)
+        response = requests.get(
+            endpoint,
+            headers=self.headers
+        )
+        self.logger.info('{} retrieving user data {}'.format(
+            response.status_code, endpoint
+        ))
+        self._validate_status_code(response)
+        return response.json()
+
+    def mark_messages_as_read(self, message_ids):
+        """
+        Mark the specified message ids as read.
+        """
+        import requests
+
+        endpoint = '{}user/{}/rooms/{}/unreadItems'.format(
+            self.gitter_host, self.user_id, self.room_id
+        )
+        response = requests.post(
+            endpoint,
+            headers=self.headers,
+            json={'chat': message_ids}
+        )
+        self.logger.info('{} marking messages as read {}'.format(
+            response.status_code, endpoint
+        ))
+        self._validate_status_code(response)
+        return response.json()
+
+    def get_most_recent_message(self):
+        """
+        Get the most recent message from the Gitter room.
+        """
+        import requests
+
+        endpoint = '{}rooms/{}/chatMessages?limit=1'.format(self.gitter_host, self.room_id)
+        response = requests.get(
+            endpoint,
+            headers=self.headers
+        )
+        self.logger.info('{} getting most recent message'.format(
+            response.status_code
+        ))
+        self._validate_status_code(response)
+        data = response.json()
+        if data:
+            return data[0]
+        return None
+
+    def _contains_mention(self, mentions):
+        for mention in mentions:
+            if self.username == mention.get('screenName'):
+                return True
+        return False
+
+    def should_respond(self, data):
+        """
+        Takes the API response data from a single message.
+        Returns true if the chat bot should respond.
+        """
+        if data:
+            unread = data.get('unread', False)
+
+            if self.only_respond_to_mentions:
+                if unread and self._contains_mention(data['mentions']):
+                    return True
+                else:
+                    return False
+            elif unread:
+                return True
+
+        return False
+
+    def remove_mentions(self, text):
+        """
+        Return a string that has no leading mentions.
+        """
+        import re
+        text_without_mentions = re.sub(r'@\S+', '', text)
+
+        # Remove consecutive spaces
+        text_without_mentions = re.sub(' +', ' ', text_without_mentions.strip())
+
+        return text_without_mentions
+
+    def process_input(self, statement):
+        new_message = False
+
+        while not new_message:
+            data = self.get_most_recent_message()
+            if self.should_respond(data):
+                self.mark_messages_as_read([data['id']])
+                new_message = True
+            sleep(self.sleep_time)
+
+        text = self.remove_mentions(data['text'])
+        statement = Statement(text)
+
+        return statement
+
+    class HTTPStatusException(Exception):
+        """
+        Exception raised when unexpected non-success HTTP
+        status codes are returned in a response.
+        """
+
+        def __init__(self, value):
+            self.value = value
+
+        def __str__(self):
+            return repr(self.value)

chatter/source/input/hipchat.py

@@ -0,0 +1,113 @@
+from __future__ import unicode_literals
+from time import sleep
+from . import InputAdapter
+from ..conversation import Statement
+
+
+class HipChat(InputAdapter):
+    """
+    An input adapter that allows a ChatterBot instance to get
+    input statements from a HipChat room.
+    """
+
+    def __init__(self, **kwargs):
+        super(HipChat, self).__init__(**kwargs)
+
+        self.hipchat_host = kwargs.get('hipchat_host')
+        self.hipchat_access_token = kwargs.get('hipchat_access_token')
+        self.hipchat_room = kwargs.get('hipchat_room')
+        self.session_id = str(self.chatbot.default_session.uuid)
+
+        import requests
+        self.session = requests.Session()
+        self.session.verify = kwargs.get('ssl_verify', True)
+
+        authorization_header = 'Bearer {}'.format(self.hipchat_access_token)
+
+        self.headers = {
+            'Authorization': authorization_header,
+            'Content-Type': 'application/json'
+        }
+
+        # This is a list of the messages that have been responded to
+        self.recent_message_ids = self.get_initial_ids()
+
+    def get_initial_ids(self):
+        """
+        Returns a list of the most recent message ids.
+        """
+        data = self.view_recent_room_history(
+            self.hipchat_room,
+            max_results=75
+        )
+
+        results = set()
+
+        for item in data['items']:
+            results.add(item['id'])
+
+        return results
+
+    def view_recent_room_history(self, room_id_or_name, max_results=1):
+        """
+        https://www.hipchat.com/docs/apiv2/method/view_recent_room_history
+        """
+
+        recent_histroy_url = '{}/v2/room/{}/history?max-results={}'.format(
+            self.hipchat_host,
+            room_id_or_name,
+            max_results
+        )
+
+        response = self.session.get(
+            recent_histroy_url,
+            headers=self.headers
+        )
+
+        return response.json()
+
+    def get_most_recent_message(self, room_id_or_name):
+        """
+        Return the most recent message from the HipChat room.
+        """
+        data = self.view_recent_room_history(room_id_or_name)
+
+        items = data['items']
+
+        if not items:
+            return None
+        return items[-1]
+
+    def process_input(self, statement):
+        """
+        Process input from the HipChat room.
+        """
+        new_message = False
+
+        response_statement = self.chatbot.storage.get_latest_response(
+            self.session_id
+        )
+
+        if response_statement:
+            last_message_id = response_statement.extra_data.get(
+                'hipchat_message_id', None
+            )
+            if last_message_id:
+                self.recent_message_ids.add(last_message_id)
+
+        while not new_message:
+            data = self.get_most_recent_message(self.hipchat_room)
+
+            if data and data['id'] not in self.recent_message_ids:
+                self.recent_message_ids.add(data['id'])
+                new_message = True
+            else:
+                pass
+            sleep(3.5)
+
+        text = data['message']
+
+        statement = Statement(text)
+        statement.add_extra_data('hipchat_message_id', data['id'])
+
+        return statement

chatter/source/input/input_adapter.py

@@ -0,0 +1,33 @@
+from __future__ import unicode_literals
+from ..adapters import Adapter
+
+
+class InputAdapter(Adapter):
+    """
+    This is an abstract class that represents the
+    interface that all input adapters should implement.
+    """
+
+    def process_input(self, *args, **kwargs):
+        """
+        Returns a statement object based on the input source.
+        """
+        raise self.AdapterMethodNotImplementedError()
+
+    def process_input_statement(self, *args, **kwargs):
+        """
+        Return an existing statement object (if one exists).
+        """
+        input_statement = self.process_input(*args, **kwargs)
+
+        self.logger.info('Received input statement: {}'.format(input_statement.text))
+
+        existing_statement = self.chatbot.storage.find(input_statement.text)
+
+        if existing_statement:
+            self.logger.info('"{}" is a known statement'.format(input_statement.text))
+            input_statement = existing_statement
+        else:
+            self.logger.info('"{}" is not a known statement'.format(input_statement.text))
+
+        return input_statement

chatter/source/input/mailgun.py

@@ -0,0 +1,61 @@
+from __future__ import unicode_literals
+import datetime
+from . import InputAdapter
+from ..conversation import Statement
+
+
+class Mailgun(InputAdapter):
+    """
+    Get input from Mailgun.
+    """
+
+    def __init__(self, **kwargs):
+        super(Mailgun, self).__init__(**kwargs)
+
+        # Use the bot's name for the name of the sender
+        self.name = kwargs.get('name')
+        self.from_address = kwargs.get('mailgun_from_address')
+        self.api_key = kwargs.get('mailgun_api_key')
+        self.endpoint = kwargs.get('mailgun_api_endpoint')
+
+    def get_email_stored_events(self):
+        import requests
+
+        yesterday = datetime.datetime.now() - datetime.timedelta(1)
+        return requests.get(
+            '{}/events'.format(self.endpoint),
+            auth=('api', self.api_key),
+            params={
+                'begin': yesterday.isoformat(),
+                'ascending': 'yes',
+                'limit': 1
+            }
+        )
+
+    def get_stored_email_urls(self):
+        response = self.get_email_stored_events()
+        data = response.json()
+
+        for item in data.get('items', []):
+            if 'storage' in item:
+                if 'url' in item['storage']:
+                    yield item['storage']['url']
+
+    def get_message(self, url):
+        import requests
+
+        return requests.get(
+            url,
+            auth=('api', self.api_key)
+        )
+
+    def process_input(self, statement):
+        urls = self.get_stored_email_urls()
+        url = list(urls)[0]
+
+        response = self.get_message(url)
+        message = response.json()
+
+        text = message.get('stripped-text')
+
+        return Statement(text)

chatter/source/input/microsoft.py

@@ -0,0 +1,115 @@
+from __future__ import unicode_literals
+from time import sleep
+from . import InputAdapter
+from ..conversation import Statement
+
+
+class Microsoft(InputAdapter):
+    """
+    An input adapter that allows a ChatterBot instance to get
+    input statements from a Microsoft Bot using *Directline client protocol*.
+    https://docs.botframework.com/en-us/restapi/directline/#navtitle
+    """
+
+    def __init__(self, **kwargs):
+        super(Microsoft, self).__init__(**kwargs)
+        import requests
+        from requests.packages.urllib3.exceptions import InsecureRequestWarning
+        requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
+
+        self.directline_host = kwargs.get('directline_host', 'https://directline.botframework.com')
+
+        # NOTE: Direct Line client credentials are different from your bot's
+        # credentials
+        self.direct_line_token_or_secret = kwargs.\
+            get('direct_line_token_or_secret')
+
+        authorization_header = 'BotConnector  {}'.\
+            format(self.direct_line_token_or_secret)
+
+        self.headers = {
+            'Authorization': authorization_header,
+            'Content-Type': 'application/json',
+            'Accept': 'application/json',
+            'charset': 'utf-8'
+        }
+
+        conversation_data = self.start_conversation()
+        self.conversation_id = conversation_data.get('conversationId')
+        self.conversation_token = conversation_data.get('token')
+
+    def _validate_status_code(self, response):
+        code = response.status_code
+        if not code == 200:
+            raise self.HTTPStatusException('{} status code recieved'.
+                                           format(code))
+
+    def start_conversation(self):
+        import requests
+
+        endpoint = '{host}/api/conversations'.format(host=self.directline_host)
+        response = requests.post(
+            endpoint,
+            headers=self.headers,
+            verify=False
+        )
+        self.logger.info('{} starting conversation {}'.format(
+            response.status_code, endpoint
+        ))
+        self._validate_status_code(response)
+        return response.json()
+
+    def get_most_recent_message(self):
+        import requests
+
+        endpoint = '{host}/api/conversations/{id}/messages'\
+            .format(host=self.directline_host,
+                    id=self.conversation_id)
+
+        response = requests.get(
+            endpoint,
+            headers=self.headers,
+            verify=False
+        )
+
+        self.logger.info('{} retrieving most recent messages {}'.format(
+            response.status_code, endpoint
+        ))
+
+        self._validate_status_code(response)
+
+        data = response.json()
+
+        if data['messages']:
+            last_msg = int(data['watermark'])
+            return data['messages'][last_msg - 1]
+        return None
+
+    def process_input(self, statement):
+        new_message = False
+        data = None
+        while not new_message:
+            data = self.get_most_recent_message()
+            if data and data['id']:
+                new_message = True
+            else:
+                pass
+            sleep(3.5)
+
+        text = data['text']
+        statement = Statement(text)
+        self.logger.info('processing user statement {}'.format(statement))
+
+        return statement
+
+    class HTTPStatusException(Exception):
+        """
+        Exception raised when unexpected non-success HTTP
+        status codes are returned in a response.
+        """
+
+        def __init__(self, value):
+            self.value = value
+
+        def __str__(self):
+            return repr(self.value)

chatter/source/input/terminal.py

@@ -0,0 +1,18 @@
+from __future__ import unicode_literals
+from . import InputAdapter
+from ..conversation import Statement
+from ..utils import input_function
+
+
+class TerminalAdapter(InputAdapter):
+    """
+    A simple adapter that allows ChatterBot to
+    communicate through the terminal.
+    """
+
+    def process_input(self, *args, **kwargs):
+        """
+        Read the user's input from the terminal.
+        """
+        user_input = input_function()
+        return Statement(user_input)

chatter/source/input/variable_input_type_adapter.py

@@ -0,0 +1,65 @@
+from __future__ import unicode_literals
+from . import InputAdapter
+from ..conversation import Statement
+
+
+class VariableInputTypeAdapter(InputAdapter):
+
+    JSON = 'json'
+    TEXT = 'text'
+    OBJECT = 'object'
+    VALID_FORMATS = (JSON, TEXT, OBJECT, )
+
+    def detect_type(self, statement):
+        import sys
+
+        if sys.version_info[0] < 3:
+            string_types = basestring # NOQA
+        else:
+            string_types = str
+
+        if hasattr(statement, 'text'):
+            return self.OBJECT
+        if isinstance(statement, string_types):
+            return self.TEXT
+        if isinstance(statement, dict):
+            return self.JSON
+
+        input_type = type(statement)
+
+        raise self.UnrecognizedInputFormatException(
+            'The type {} is not recognized as a valid input type.'.format(
+                input_type
+            )
+        )
+
+    def process_input(self, statement):
+        input_type = self.detect_type(statement)
+
+        # Return the statement object without modification
+        if input_type == self.OBJECT:
+            return statement
+
+        # Convert the input string into a statement object
+        if input_type == self.TEXT:
+            return Statement(statement)
+
+        # Convert input dictionary into a statement object
+        if input_type == self.JSON:
+            input_json = dict(statement)
+            text = input_json['text']
+            del input_json['text']
+
+            return Statement(text, **input_json)
+
+    class UnrecognizedInputFormatException(Exception):
+        """
+        Exception raised when an input format is specified that is
+        not in the VariableInputTypeAdapter.VALID_FORMATS variable.
+        """
+
+        def __init__(self, value='The input format was not recognized.'):
+            self.value = value
+
+        def __str__(self):
+            return repr(self.value)

chatter/source/logic/__init__.py

@@ -0,0 +1,20 @@
+from .logic_adapter import LogicAdapter
+from .best_match import BestMatch
+from .low_confidence import LowConfidenceAdapter
+from .mathematical_evaluation import MathematicalEvaluation
+from .multi_adapter import MultiLogicAdapter
+from .no_knowledge_adapter import NoKnowledgeAdapter
+from .specific_response import SpecificResponseAdapter
+from .time_adapter import TimeLogicAdapter
+
+
+__all__ = (
+    'LogicAdapter',
+    'BestMatch',
+    'LowConfidenceAdapter',
+    'MathematicalEvaluation',
+    'MultiLogicAdapter',
+    'NoKnowledgeAdapter',
+    'SpecificResponseAdapter',
+    'TimeLogicAdapter',
+)

chatter/source/logic/best_match.py

@@ -0,0 +1,84 @@
+from __future__ import unicode_literals
+from .logic_adapter import LogicAdapter
+
+
+class BestMatch(LogicAdapter):
+    """
+    A logic adapter that returns a response based on known responses to
+    the closest matches to the input statement.
+    """
+
+    def get(self, input_statement):
+        """
+        Takes a statement string and a list of statement strings.
+        Returns the closest matching statement from the list.
+        """
+        statement_list = self.chatbot.storage.get_response_statements()
+
+        if not statement_list:
+            if self.chatbot.storage.count():
+                # Use a randomly picked statement
+                self.logger.info(
+                    'No statements have known responses. ' +
+                    'Choosing a random response to return.'
+                )
+                random_response = self.chatbot.storage.get_random()
+                random_response.confidence = 0
+                return random_response
+            else:
+                raise self.EmptyDatasetException()
+
+        closest_match = input_statement
+        closest_match.confidence = 0
+
+        # Find the closest matching known statement
+        for statement in statement_list:
+            confidence = self.compare_statements(input_statement, statement)
+
+            if confidence > closest_match.confidence:
+                statement.confidence = confidence
+                closest_match = statement
+
+        return closest_match
+
+    def can_process(self, statement):
+        """
+        Check that the chatbot's storage adapter is available to the logic
+        adapter and there is at least one statement in the database.
+        """
+        return self.chatbot.storage.count()
+
+    def process(self, input_statement):
+
+        # Select the closest match to the input statement
+        closest_match = self.get(input_statement)
+        self.logger.info('Using "{}" as a close match to "{}"'.format(
+            input_statement.text, closest_match.text
+        ))
+
+        # Get all statements that are in response to the closest match
+        response_list = self.chatbot.storage.filter(
+            in_response_to__contains=closest_match.text
+        )
+
+        if response_list:
+            self.logger.info(
+                'Selecting response from {} optimal responses.'.format(
+                    len(response_list)
+                )
+            )
+            response = self.select_response(input_statement, response_list)
+            response.confidence = closest_match.confidence
+            self.logger.info('Response selected. Using "{}"'.format(response.text))
+        else:
+            response = self.chatbot.storage.get_random()
+            self.logger.info(
+                'No response to "{}" found. Selecting a random response.'.format(
+                    closest_match.text
+                )
+            )
+
+            # Set confidence to zero because a random response is selected
+            response.confidence = 0
+
+        return response

chatter/source/logic/logic_adapter.py

@@ -0,0 +1,100 @@
+from __future__ import unicode_literals
+from ..adapters import Adapter
+from ..utils import import_module
+
+
+class LogicAdapter(Adapter):
+    """
+    This is an abstract class that represents the interface
+    that all logic adapters should implement.
+
+    :param statement_comparison_function: The dot-notated import path to a statement comparison function.
+                                          Defaults to ``levenshtein_distance``.
+
+    :param response_selection_method: The a response selection method.
+                                      Defaults to ``get_first_response``.
+    """
+
+    def __init__(self, **kwargs):
+        super(LogicAdapter, self).__init__(**kwargs)
+        from ..comparisons import levenshtein_distance
+        from ..response_selection import get_first_response
+
+        # Import string module parameters
+        if 'statement_comparison_function' in kwargs:
+            import_path = kwargs.get('statement_comparison_function')
+            if isinstance(import_path, str):
+                kwargs['statement_comparison_function'] = import_module(import_path)
+
+        if 'response_selection_method' in kwargs:
+            import_path = kwargs.get('response_selection_method')
+            if isinstance(import_path, str):
+                kwargs['response_selection_method'] = import_module(import_path)
+
+        # By default, compare statements using Levenshtein distance
+        self.compare_statements = kwargs.get(
+            'statement_comparison_function',
+            levenshtein_distance
+        )
+
+        # By default, select the first available response
+        self.select_response = kwargs.get(
+            'response_selection_method',
+            get_first_response
+        )
+
+    def get_initialization_functions(self):
+        """
+        Return a dictionary of functions to be run once when the chat bot is instantiated.
+        """
+        return self.compare_statements.get_initialization_functions()
+
+    def initialize(self):
+        for function in self.get_initialization_functions().values():
+            function()
+
+    def can_process(self, statement):
+        """
+        A preliminary check that is called to determine if a
+        logic adapter can process a given statement. By default,
+        this method returns true but it can be overridden in
+        child classes as needed.
+
+        :rtype: bool
+        """
+        return True
+
+    def process(self, statement):
+        """
+        Override this method and implement your logic for selecting a response to an input statement.
+
+        A confidence value and the selected response statement should be returned.
+        The confidence value represents a rating of how accurate the logic adapter
+        expects the selected response to be. Confidence scores are used to select
+        the best response from multiple logic adapters.
+
+        The confidence value should be a number between 0 and 1 where 0 is the
+        lowest confidence level and 1 is the highest.
+
+        :param statement: An input statement to be processed by the logic adapter.
+        :type statement: Statement
+
+        :rtype: Statement
+        """
+        raise self.AdapterMethodNotImplementedError()
+
+    @property
+    def class_name(self):
+        """
+        Return the name of the current logic adapter class.
+        This is typically used for logging and debugging.
+        """
+        return str(self.__class__.__name__)
+
+    class EmptyDatasetException(Exception):
+
+        def __init__(self, value='An empty set was received when at least one statement was expected.'):
+            self.value = value
+
+        def __str__(self):
+            return repr(self.value)

chatter/source/logic/low_confidence.py

@@ -0,0 +1,58 @@
+from __future__ import unicode_literals
+from ..conversation import Statement
+from .best_match import BestMatch
+
+
+class LowConfidenceAdapter(BestMatch):
+    """
+    Returns a default response with a high confidence
+    when a high confidence response is not known.
+
+    :kwargs:
+        * *threshold* (``float``) --
+          The low confidence value that triggers this adapter.
+          Defaults to 0.65.
+        * *default_response* (``str``) or (``iterable``)--
+          The response returned by this logic adaper.
+        * *response_selection_method* (``str``) or (``callable``)
+          The a response selection method.
+          Defaults to ``get_first_response``.
+    """
+
+    def __init__(self, **kwargs):
+        super(LowConfidenceAdapter, self).__init__(**kwargs)
+
+        self.confidence_threshold = kwargs.get('threshold', 0.65)
+
+        default_responses = kwargs.get(
+            'default_response', "I'm sorry, I do not understand."
+        )
+
+        # Convert a single string into a list
+        if isinstance(default_responses, str):
+            default_responses = [
+                default_responses
+            ]
+
+        self.default_responses = [
+            Statement(text=default) for default in default_responses
+        ]
+
+    def process(self, input_statement):
+        """
+        Return a default response with a high confidence if
+        a high confidence response is not known.
+        """
+        # Select the closest match to the input statement
+        closest_match = self.get(input_statement)
+
+        # Choose a response from the list of options
+        response = self.select_response(input_statement, self.default_responses)
+
+        # Confidence should be high only if it is less than the threshold
+        if closest_match.confidence < self.confidence_threshold:
+            response.confidence = 1
+        else:
+            response.confidence = 0
+
+        return response

chatter/source/logic/mathematical_evaluation.py

@@ -0,0 +1,67 @@
+from __future__ import unicode_literals
+from . import LogicAdapter
+from ..conversation import Statement
+
+
+class MathematicalEvaluation(LogicAdapter):
+    """
+    The MathematicalEvaluation logic adapter parses input to determine
+    whether the user is asking a question that requires math to be done.
+    If so, the equation is extracted from the input and returned with
+    the evaluated result.
+
+    For example:
+        User: 'What is three plus five?'
+        Bot: 'Three plus five equals eight'
+
+    :kwargs:
+        * *language* (``str``) --
+          The language is set to 'ENG' for English by default.
+    """
+
+    def __init__(self, **kwargs):
+        super(MathematicalEvaluation, self).__init__(**kwargs)
+
+        self.language = kwargs.get('language', 'ENG')
+        self.cache = {}
+
+    def can_process(self, statement):
+        """
+        Determines whether it is appropriate for this
+        adapter to respond to the user input.
+        """
+        response = self.process(statement)
+        self.cache[statement.text] = response
+        return response.confidence == 1
+
+    def process(self, statement):
+        """
+        Takes a statement string.
+        Returns the equation from the statement with the mathematical terms solved.
+        """
+        from mathparse import mathparse
+
+        input_text = statement.text
+
+        # Use the result cached by the process method if it exists
+        if input_text in self.cache:
+            cached_result = self.cache[input_text]
+            self.cache = {}
+            return cached_result
+
+        # Getting the mathematical terms within the input statement
+        expression = mathparse.extract_expression(input_text, language=self.language)
+
+        response = Statement(text=expression)
+
+        try:
+            response.text += ' = ' + str(
+                mathparse.parse(expression, language=self.language)
+            )
+
+            # The confidence is 1 if the expression could be evaluated
+            response.confidence = 1
+        except mathparse.PostfixTokenEvaluationException:
+            response.confidence = 0
+
+        return response

chatter/source/logic/multi_adapter.py

@@ -0,0 +1,153 @@
+from __future__ import unicode_literals
+from collections import Counter
+from .. import utils
+from .logic_adapter import LogicAdapter
+
+
+class MultiLogicAdapter(LogicAdapter):
+    """
+    MultiLogicAdapter allows ChatterBot to use multiple logic
+    adapters. It has methods that allow ChatterBot to add an
+    adapter, set the chat bot, and process an input statement
+    to get a response.
+    """
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+        # Logic adapters added by the chat bot
+        self.adapters = []
+
+        # Required logic adapters that must always be present
+        self.system_adapters = []
+
+    def get_initialization_functions(self):
+        """
+        Get the initialization functions for each logic adapter.
+        """
+        functions_dict = {}
+
+        # Iterate over each adapter and get its initialization functions
+        for logic_adapter in self.get_adapters():
+            functions = logic_adapter.get_initialization_functions()
+            functions_dict.update(functions)
+
+        return functions_dict
+
+    def process(self, statement):
+        """
+        Returns the output of a selection of logic adapters
+        for a given input statement.
+
+        :param statement: The input statement to be processed.
+        """
+        results = []
+        result = None
+        max_confidence = -1
+
+        for adapter in self.get_adapters():
+            if adapter.can_process(statement):
+
+                output = adapter.process(statement)
+                results.append((output.confidence, output, ))
+
+                self.logger.info(
+                    '{} selected "{}" as a response with a confidence of {}'.format(
+                        adapter.class_name, output.text, output.confidence
+                    )
+                )
+
+                if output.confidence > max_confidence:
+                    result = output
+                    max_confidence = output.confidence
+            else:
+                self.logger.info(
+                    'Not processing the statement using {}'.format(adapter.class_name)
+                )
+
+        # If multiple adapters agree on the same statement,
+        # then that statement is more likely to be the correct response
+        if len(results) >= 3:
+            statements = [s[1] for s in results]
+            count = Counter(statements)
+            most_common = count.most_common()
+            if most_common[0][1] > 1:
+                result = most_common[0][0]
+                max_confidence = self.get_greatest_confidence(result, results)
+
+        result.confidence = max_confidence
+        return result
+
+    def get_greatest_confidence(self, statement, options):
+        """
+        Returns the greatest confidence value for a statement that occurs
+        multiple times in the set of options.
+
+        :param statement: A statement object.
+        :param options: A tuple in the format of (confidence, statement).
+        """
+        values = []
+        for option in options:
+            if option[1] == statement:
+                values.append(option[0])
+
+        return max(values)
+
+    def get_adapters(self):
+        """
+        Return a list of all logic adapters being used, including system logic adapters.
+        """
+        adapters = []
+        adapters.extend(self.adapters)
+        adapters.extend(self.system_adapters)
+        return adapters
+
+    def add_adapter(self, adapter, **kwargs):
+        """
+        Appends a logic adapter to the list of logic adapters being used.
+
+        :param adapter: The logic adapter to be added.
+        :type adapter: `LogicAdapter`
+        """
+        utils.validate_adapter_class(adapter, LogicAdapter)
+        adapter = utils.initialize_class(adapter, **kwargs)
+        self.adapters.append(adapter)
+
+    def insert_logic_adapter(self, logic_adapter, insert_index, **kwargs):
+        """
+        Adds a logic adapter at a specified index.
+
+        :param logic_adapter: The string path to the logic adapter to add.
+        :type logic_adapter: str
+
+        :param insert_index: The index to insert the logic adapter into the list at.
+        :type insert_index: int
+        """
+        utils.validate_adapter_class(logic_adapter, LogicAdapter)
+
+        NewAdapter = utils.import_module(logic_adapter)
+        adapter = NewAdapter(**kwargs)
+
+        self.adapters.insert(insert_index, adapter)
+
+    def remove_logic_adapter(self, adapter_name):
+        """
+        Removes a logic adapter from the chat bot.
+
+        :param adapter_name: The class name of the adapter to remove.
+        :type adapter_name: str
+        """
+        for index, adapter in enumerate(self.adapters):
+            if adapter_name == type(adapter).__name__:
+                del self.adapters[index]
+                return True
+        return False
+
+    def set_chatbot(self, chatbot):
+        """
+        Set the chatbot for each of the contained logic adapters.
+        """
+        super(MultiLogicAdapter, self).set_chatbot(chatbot)
+
+        for adapter in self.get_adapters():
+            adapter.set_chatbot(chatbot)

chatter/source/logic/no_knowledge_adapter.py

@@ -0,0 +1,26 @@
+from __future__ import unicode_literals
+from .logic_adapter import LogicAdapter
+
+
+class NoKnowledgeAdapter(LogicAdapter):
+    """
+    This is a system adapter that is automatically added
+    to the list of logic adapters during initialization.
+    This adapter is placed at the beginning of the list
+    to be given the highest priority.
+    """
+
+    def process(self, statement):
+        """
+        If there are no known responses in the database,
+        then a confidence of 1 should be returned with
+        the input statement.
+        Otherwise, a confidence of 0 should be returned.
+        """
+
+        if self.chatbot.storage.count():
+            statement.confidence = 0
+        else:
+            statement.confidence = 1
+
+        return statement

chatter/source/logic/specific_response.py

@@ -0,0 +1,38 @@
+from __future__ import unicode_literals
+from .logic_adapter import LogicAdapter
+
+
+class SpecificResponseAdapter(LogicAdapter):
+    """
+    Return a specific response to a specific input.
+
+    :kwargs:
+        * *input_text* (``str``) --
+          The input text that triggers this logic adapter.
+        * *output_text* (``str``) --
+          The output text returned by this logic adapter.
+    """
+
+    def __init__(self, **kwargs):
+        super(SpecificResponseAdapter, self).__init__(**kwargs)
+        from ..conversation import Statement
+
+        self.input_text = kwargs.get('input_text')
+
+        output_text = kwargs.get('output_text')
+        self.response_statement = Statement(output_text)
+
+    def can_process(self, statement):
+        if statement == self.input_text:
+            return True
+
+        return False
+
+    def process(self, statement):
+
+        if statement == self.input_text:
+            self.response_statement.confidence = 1
+        else:
+            self.response_statement.confidence = 0
+
+        return self.response_statement

chatter/source/logic/time_adapter.py

@@ -0,0 +1,91 @@
+from __future__ import unicode_literals
+from datetime import datetime
+from .logic_adapter 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 ..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

chatter/source/output/__init__.py

@@ -0,0 +1,15 @@
+from .output_adapter import OutputAdapter
+from .microsoft import Microsoft
+from .terminal import TerminalAdapter
+from .mailgun import Mailgun
+from .gitter import Gitter
+from .hipchat import HipChat
+
+__all__ = (
+    'OutputAdapter',
+    'Microsoft',
+    'TerminalAdapter',
+    'Mailgun',
+    'Gitter',
+    'HipChat',
+)

chatter/source/output/gitter.py

@@ -0,0 +1,85 @@
+from __future__ import unicode_literals
+from .output_adapter import OutputAdapter
+
+
+class Gitter(OutputAdapter):
+    """
+    An output adapter that allows a ChatterBot instance to send
+    responses to a Gitter room.
+    """
+
+    def __init__(self, **kwargs):
+        super(Gitter, self).__init__(**kwargs)
+
+        self.gitter_host = kwargs.get('gitter_host', 'https://api.gitter.im/v1/')
+        self.gitter_room = kwargs.get('gitter_room')
+        self.gitter_api_token = kwargs.get('gitter_api_token')
+
+        authorization_header = 'Bearer {}'.format(self.gitter_api_token)
+
+        self.headers = {
+            'Authorization': authorization_header,
+            'Content-Type': 'application/json; charset=utf-8',
+            'Accept': 'application/json'
+        }
+
+        # Join the Gitter room
+        room_data = self.join_room(self.gitter_room)
+        self.room_id = room_data.get('id')
+
+    def _validate_status_code(self, response):
+        code = response.status_code
+        if code not in [200, 201]:
+            raise self.HTTPStatusException('{} status code recieved'.format(code))
+
+    def join_room(self, room_name):
+        """
+        Join the specified Gitter room.
+        """
+        import requests
+
+        endpoint = '{}rooms'.format(self.gitter_host)
+        response = requests.post(
+            endpoint,
+            headers=self.headers,
+            json={'uri': room_name}
+        )
+        self.logger.info('{} status joining room {}'.format(
+            response.status_code, endpoint
+        ))
+        self._validate_status_code(response)
+        return response.json()
+
+    def send_message(self, text):
+        """
+        Send a message to a Gitter room.
+        """
+        import requests
+
+        endpoint = '{}rooms/{}/chatMessages'.format(self.gitter_host, self.room_id)
+        response = requests.post(
+            endpoint,
+            headers=self.headers,
+            json={'text': text}
+        )
+        self.logger.info('{} sending message to {}'.format(
+            response.status_code, endpoint
+        ))
+        self._validate_status_code(response)
+        return response.json()
+
+    def process_response(self, statement, session_id=None):
+        self.send_message(statement.text)
+        return statement
+
+    class HTTPStatusException(Exception):
+        """
+        Exception raised when unexpected non-success HTTP
+        status codes are returned in a response.
+        """
+
+        def __init__(self, value):
+            self.value = value
+
+        def __str__(self):
+            return repr(self.value)

chatter/source/output/hipchat.py

@@ -0,0 +1,67 @@
+from __future__ import unicode_literals
+import json
+from .output_adapter import OutputAdapter
+
+
+class HipChat(OutputAdapter):
+    """
+    An output adapter that allows a ChatterBot instance to send
+    responses to a HipChat room.
+    """
+
+    def __init__(self, **kwargs):
+        super(HipChat, self).__init__(**kwargs)
+
+        self.hipchat_host = kwargs.get("hipchat_host")
+        self.hipchat_access_token = kwargs.get("hipchat_access_token")
+        self.hipchat_room = kwargs.get("hipchat_room")
+
+        authorization_header = "Bearer {}".format(self.hipchat_access_token)
+
+        self.headers = {
+            'Authorization': authorization_header,
+            'Content-Type': 'application/json'
+        }
+
+        import requests
+        self.session = requests.Session()
+        self.session.verify = kwargs.get('ssl_verify', True)
+
+    def send_message(self, room_id_or_name, message):
+        """
+        Send a message to a HipChat room.
+        https://www.hipchat.com/docs/apiv2/method/send_message
+        """
+        message_url = "{}/v2/room/{}/message".format(
+            self.hipchat_host,
+            room_id_or_name
+        )
+
+        response = self.session.post(
+            message_url,
+            headers=self.headers,
+            data=json.dumps({
+                'message': message
+            })
+        )
+
+        return response.json()
+
+    def reply_to_message(self):
+        """
+        The HipChat api supports responding to a given message.
+        This may be a good feature to implement in the future to
+        help with multi-user conversations.
+        https://www.hipchat.com/docs/apiv2/method/reply_to_message
+        """
+        raise self.AdapterMethodNotImplementedError()
+
+    def process_response(self, statement, session_id=None):
+        data = self.send_message(self.hipchat_room, statement.text)
+
+        # Update the output statement with the message id
+        self.chatbot.storage.update(
+            statement.add_extra_data('hipchat_message_id', data['id'])
+        )
+
+        return statement

chatter/source/output/mailgun.py

@@ -0,0 +1,49 @@
+from __future__ import unicode_literals
+from .output_adapter import OutputAdapter
+
+
+class Mailgun(OutputAdapter):
+
+    def __init__(self, **kwargs):
+        super(Mailgun, self).__init__(**kwargs)
+
+        # Use the bot's name for the name of the sender
+        self.name = kwargs.get('name')
+        self.from_address = kwargs.get('mailgun_from_address')
+        self.api_key = kwargs.get('mailgun_api_key')
+        self.endpoint = kwargs.get('mailgun_api_endpoint')
+        self.recipients = kwargs.get('mailgun_recipients')
+
+    def send_message(self, subject, text, from_address, recipients):
+        """
+        * subject: Subject of the email.
+        * text: Text body of the email.
+        * from_email: The email address that the message will be sent from.
+        * recipients: A list of recipient email addresses.
+        """
+        import requests
+
+        return requests.post(
+            self.endpoint,
+            auth=('api', self.api_key),
+            data={
+                'from': '%s <%s>' % (self.name, from_address),
+                'to': recipients,
+                'subject': subject,
+                'text': text
+            })
+
+    def process_response(self, statement, session_id=None):
+        """
+        Send the response statement as an email.
+        """
+        subject = 'Message from %s' % (self.name)
+
+        self.send_message(
+            subject,
+            statement.text,
+            self.from_address,
+            self.recipients
+        )
+
+        return statement

chatter/source/output/microsoft.py

@@ -0,0 +1,109 @@
+from __future__ import unicode_literals
+import json
+from .output_adapter import OutputAdapter
+
+
+class Microsoft(OutputAdapter):
+    """
+    An output adapter that allows a ChatterBot instance to send
+    responses to a Microsoft bot using *Direct Line client protocol*.
+    """
+
+    def __init__(self, **kwargs):
+        super(Microsoft, self).__init__(**kwargs)
+
+        self.directline_host = kwargs.get(
+            'directline_host',
+            'https://directline.botframework.com'
+        )
+        self.direct_line_token_or_secret = kwargs.get(
+            'direct_line_token_or_secret'
+        )
+        self.conversation_id = kwargs.get('conversation_id')
+
+        authorization_header = 'BotConnector {}'.format(
+            self.direct_line_token_or_secret
+        )
+
+        self.headers = {
+            'Authorization': authorization_header,
+            'Content-Type': 'application/json'
+        }
+
+    def _validate_status_code(self, response):
+        status_code = response.status_code
+        if status_code not in [200, 204]:
+            raise self.HTTPStatusException('{} status code recieved'.format(status_code))
+
+    def get_most_recent_message(self):
+        """
+        Return the most recently sent message.
+        """
+        import requests
+        endpoint = '{host}/api/conversations/{id}/messages'.format(
+            host=self.directline_host,
+            id=self.conversation_id
+        )
+
+        response = requests.get(
+            endpoint,
+            headers=self.headers,
+            verify=False
+        )
+
+        self.logger.info('{} retrieving most recent messages {}'.format(
+            response.status_code, endpoint
+        ))
+
+        self._validate_status_code(response)
+
+        data = response.json()
+
+        if data['messages']:
+            last_msg = int(data['watermark'])
+            return data['messages'][last_msg - 1]
+        return None
+
+    def send_message(self, conversation_id, message):
+        """
+        Send a message to a HipChat room.
+        https://www.hipchat.com/docs/apiv2/method/send_message
+        """
+        import requests
+
+        message_url = "{host}/api/conversations/{conversationId}/messages".format(
+            host=self.directline_host,
+            conversationId=conversation_id
+        )
+
+        response = requests.post(
+            message_url,
+            headers=self.headers,
+            data=json.dumps({
+                'message': message
+            })
+        )
+
+        self.logger.info('{} sending message {}'.format(
+            response.status_code, message_url
+        ))
+        self._validate_status_code(response)
+        # Microsoft return 204 on operation succeeded and no content was returned.
+        return self.get_most_recent_message()
+
+    def process_response(self, statement, session_id=None):
+        data = self.send_message(self.conversation_id, statement.text)
+        self.logger.info('processing user response {}'.format(data))
+        return statement
+
+    class HTTPStatusException(Exception):
+        """
+        Exception raised when unexpected non-success HTTP
+        status codes are returned in a response.
+        """
+
+        def __init__(self, value):
+            self.value = value
+
+        def __str__(self):
+            return repr(self.value)