def __init__(self, **kwargs):
super(LogicAdapter, self).__init__(**kwargs)
from chatterbot.comparisons import levenshtein_distance
from chatterbot.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 __init__(self, **kwargs):
super(LogicAdapter, self).__init__(**kwargs)
from chatterbot.comparisons import levenshtein_distance
from chatterbot.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 __init__(self, chatbot, **kwargs):
super().__init__(chatbot, **kwargs)
from chatterbot.comparisons import levenshtein_distance
from chatterbot.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)
self.maximum_similarity_threshold = kwargs.get(
'maximum_similarity_threshold', 0.95)
self.excluded_words = kwargs.get('excluded_words')
self.search_page_size = kwargs.get('search_page_size', 1000)
# 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 __init__(self, **kwargs):
super(LogicAdapter, self).__init__(**kwargs)
from chatterbot.comparisons import levenshtein_distance
from chatterbot.response_selection import get_first_response
if 'tie_breaking_method' in kwargs:
raise DeprecationWarning(
'The parameter "tie_breaking_method" has been removed. ' +
'Instead, pass a callable to "response_selection_method". ' +
'See documentation for details: ' +
'http://chatterbot.readthedocs.io/en/latest/logic/response_selection.html#setting-the-response-selection-method'
)
# 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 __init__(self, name, **kwargs):
self.name = name
storage_adapter = kwargs.get('storage_adapter', 'chatterbot.storage.SQLStorageAdapter')
logic_adapters = kwargs.get('logic_adapters', [
'chatterbot.logic.BestMatch'
])
# Check that each adapter is a valid subclass of it's respective parent
utils.validate_adapter_class(storage_adapter, StorageAdapter)
# Logic adapters used by the chat bot
self.logic_adapters = []
self.storage = utils.initialize_class(storage_adapter, **kwargs)
primary_search_algorithm = IndexedTextSearch(self, **kwargs)
text_search_algorithm = TextSearch(self, **kwargs)
self.search_algorithms = {
primary_search_algorithm.name: primary_search_algorithm,
text_search_algorithm.name: text_search_algorithm
}
for adapter in logic_adapters:
utils.validate_adapter_class(adapter, LogicAdapter)
logic_adapter = utils.initialize_class(adapter, self, **kwargs)
self.logic_adapters.append(logic_adapter)
preprocessors = kwargs.get(
'preprocessors', [
'chatterbot.preprocessors.clean_whitespace'
]
)
self.preprocessors = []
self.postprocessors = []
for preprocessor in preprocessors:
self.preprocessors.append(utils.import_module(preprocessor))
postprocessors = kwargs.get(
'postprocessors', [
'chatterbot.postprocessors.joint_sentence'
]
)
for postprocessor in postprocessors:
self.postprocessors.append(utils.import_module(postprocessor))
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)
def __init__(self, name, **kwargs):
self.name = name
self.logger = kwargs.get(
'logger',
log.Logger('chatterbot',
costum_format='[%Y-%m-%d %H:%M:%S] chatterbot',
debug=kwargs.get('debug', False)))
storage_adapter = kwargs.get('storage_adapter',
'chatterbot.storage.SQLStorageAdapter')
logic_adapters = kwargs.get('logic_adapters',
['chatterbot.logic.BestMatch'])
# Check that each adapter is a valid subclass of it's respective parent
utils.validate_adapter_class(storage_adapter, StorageAdapter)
# Logic adapters used by the chat bot
self.logic_adapters = []
self.storage = utils.initialize_class(storage_adapter, **kwargs)
primary_search_algorithm = IndexedTextSearch(self, **kwargs)
text_search_algorithm = TextSearch(self, **kwargs)
self.search_algorithms = {
primary_search_algorithm.name: primary_search_algorithm,
text_search_algorithm.name: text_search_algorithm
}
for adapter in logic_adapters:
utils.validate_adapter_class(adapter, LogicAdapter)
logic_adapter = utils.initialize_class(adapter, self, **kwargs)
self.logic_adapters.append(logic_adapter)
preprocessors = kwargs.get(
'preprocessors', ['chatterbot.preprocessors.clean_whitespace'])
self.preprocessors = []
for preprocessor in preprocessors:
self.preprocessors.append(utils.import_module(preprocessor))
self.trainer = ChatterBotCorpusTrainer(self)
self.trainer.train("chatterbot.corpus.custom")
log.add_logger('chatterbot', self.logger)
self.this_file = os.path.dirname(__file__)
self.learn_YAML = os.path.join(
''.join(os.path.split(self.this_file)[0]), 'chatterbot_corpus',
'data', 'custom', 'myown.yml')
# Allow the bot to save input it receives so that it can learn
self.read_only = kwargs.get('read_only', False)
def __init__(self, chatbot, **kwargs):
super().__init__(chatbot, **kwargs)
from chatterbot.comparisons import levenshtein_distance
from chatterbot.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)
self.maximum_similarity_threshold = kwargs.get(
'maximum_similarity_threshold', 0.95
)
self.excluded_words = kwargs.get('excluded_words')
self.search_page_size = kwargs.get(
'search_page_size', 1000
)
# 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 __init__(self, chatbot, **kwargs):
super().__init__(chatbot, **kwargs)
from chatterbot.comparisons import levenshtein_distance
from chatterbot.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)
'''
The maximum amount of similarity between two statement that is required
before the search process is halted. The search for a matching statement
will continue until a statement with a greater than or equal similarity
is found or the search set is exhausted.
'''
self.maximum_similarity_threshold = kwargs.get(
'maximum_similarity_threshold', 0.95)
# The maximum number of records to load into memory at a time when searching
self.search_page_size = kwargs.get('search_page_size', 1000)
# 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 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: class
: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 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 __init__(self, name, **kwargs):
self.name = name
# storge
storage_adapter = kwargs.get('storage_adapter',
'chatterbot.storage.SQLStorageAdapter')
# Check that each adapter is a valid subclass of it's respective parent
utils.validate_adapter_class(storage_adapter, StorageAdapter)
# processors
preprocessors = kwargs.get(
'preprocessors', ['chatterbot.PreProcessing.clean_whitespace'])
self.preprocessors = []
for preprocessor in preprocessors:
self.preprocessors.append(utils.import_module(preprocessor))
def __init__(self, name, **kwargs):
self.name = name
storage_adapter = kwargs.get('storage_adapter', 'chatterbot.storage.SQLStorageAdapter')
logic_adapters = kwargs.get('logic_adapters', [
'chatterbot.logic.BestMatch'
])
# Check that each adapter is a valid subclass of it's respective parent
utils.validate_adapter_class(storage_adapter, StorageAdapter)
# Logic adapters used by the chat bot
self.logic_adapters = []
self.storage = utils.initialize_class(storage_adapter, **kwargs)
primary_search_algorithm = IndexedTextSearch(self, **kwargs)
self.search_algorithms = {
primary_search_algorithm.name: primary_search_algorithm
}
for adapter in logic_adapters:
utils.validate_adapter_class(adapter, LogicAdapter)
logic_adapter = utils.initialize_class(adapter, self, **kwargs)
self.logic_adapters.append(logic_adapter)
preprocessors = kwargs.get(
'preprocessors', [
'chatterbot.preprocessors.clean_whitespace'
]
)
self.preprocessors = []
for preprocessor in preprocessors:
self.preprocessors.append(utils.import_module(preprocessor))
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)
def test_import_module(self):
datetime = utils.import_module('datetime.datetime')
self.assertTrue(hasattr(datetime, 'now'))
def __init__(self, name, **kwargs):
self.name = name
storage_adapter = kwargs.get('storage_adapter', 'chatterbot.storage.SQLStorageAdapter')
# These are logic adapters that are required for normal operation
system_logic_adapters = kwargs.get('system_logic_adapters', (
'chatterbot.logic.NoKnowledgeAdapter',
))
logic_adapters = kwargs.get('logic_adapters', [
'chatterbot.logic.BestMatch'
])
input_adapter = kwargs.get('input_adapter', 'chatterbot.input.InputAdapter')
output_adapter = kwargs.get('output_adapter', 'chatterbot.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)
# Logic adapters used by the chat bot
self.logic_adapters = []
# Required logic adapters that must always be present
self.system_logic_adapters = []
self.storage = utils.initialize_class(storage_adapter, **kwargs)
self.input = utils.initialize_class(input_adapter, self, **kwargs)
self.output = utils.initialize_class(output_adapter, self, **kwargs)
# Add required system logic adapter
for system_logic_adapter in system_logic_adapters:
utils.validate_adapter_class(system_logic_adapter, LogicAdapter)
logic_adapter = utils.initialize_class(system_logic_adapter, self, **kwargs)
self.system_logic_adapters.append(logic_adapter)
for adapter in logic_adapters:
utils.validate_adapter_class(adapter, LogicAdapter)
logic_adapter = utils.initialize_class(adapter, self, **kwargs)
self.logic_adapters.append(logic_adapter)
preprocessors = kwargs.get(
'preprocessors', [
'chatterbot.preprocessors.clean_whitespace'
]
)
self.preprocessors = []
for preprocessor in preprocessors:
self.preprocessors.append(utils.import_module(preprocessor))
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 test_import_module(self):
datetime = utils.import_module('datetime.datetime')
self.assertTrue(hasattr(datetime, 'now'))
("policjantach", "Policjanci narażają swoje życie i zdrowie aby dbać o bezpieczeństwo obywateli."),
("policji", "Polska policja dba o bezpieczńestwo polaków."),
("strazakach", "Strażacy gaszą pożary, psik, psik."),
(["500+", "programie 500+", "programie 500+"], "Program 500+ wspiera wiele polskich rodzin. Polega na tym, że zabiera się pieniądzie złodziejom i daje dzieciom. Poparcie Polaków wobec programu 500+ wynosi ponad 90% procent."),
(["stacji tvp", "tvp"], "TVP to rzetelna i uczciwa telewizja, której misją jest informowanie o wiadomościach z kraju i krzewieniu kultury wśród polaków."),
(["stacji tvn", "tvn"], "TVN to telewizja sponsorowana przez kapitał niemiecki."),
(["pis", "partii pis"], "Partia Prawo i Sprawiedliwość dba o to aby Polska była krajem bogatym."),
(["po", "partii po"], "Partia PO przez 8 lat swoich rządów doprowadziła Polskę do ruiny i zdradzała ją z Niemcami i Unią Europejską.")
]
)
# Add input preprocessors after training.
preprocessors = [
"chatterbot.preprocessors.clean_whitespace",
"preprop.lowercased",
"preprop.remove_accents",
"preprop.filter_punctuation"
]
bot.preprocessors = []
for preprocessor in preprocessors:
bot.preprocessors.append(utils.import_module(preprocessor))
print("PiSior - Wirtualny asystent biura poselskiego kandydata na posła [...]. Jak mogę Ci pomóc?")
while True:
try:
bot_input = bot.get_response(input("> "))
print(bot_input)
except(KeyboardInterrupt, EOFError, SystemExit):
break
def __init__(self, name, **kwargs):
self.name = name
storage_adapter = kwargs.get('storage_adapter',
'chatterbot.storage.SQLStorageAdapter')
# These are logic adapters that are required for normal operation
system_logic_adapters = kwargs.get(
'system_logic_adapters', ('chatterbot.logic.NoKnowledgeAdapter', ))
logic_adapters = kwargs.get('logic_adapters',
['chatterbot.logic.BestMatch'])
input_adapter = kwargs.get('input_adapter',
'chatterbot.input.InputAdapter')
output_adapter = kwargs.get('output_adapter',
'chatterbot.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)
# Logic adapters used by the chat bot
self.logic_adapters = []
# Required logic adapters that must always be present
self.system_logic_adapters = []
self.storage = utils.initialize_class(storage_adapter, **kwargs)
self.input = utils.initialize_class(input_adapter, self, **kwargs)
self.output = utils.initialize_class(output_adapter, self, **kwargs)
# Add required system logic adapter
for system_logic_adapter in system_logic_adapters:
utils.validate_adapter_class(system_logic_adapter, LogicAdapter)
logic_adapter = utils.initialize_class(system_logic_adapter, self,
**kwargs)
self.system_logic_adapters.append(logic_adapter)
for adapter in logic_adapters:
utils.validate_adapter_class(adapter, LogicAdapter)
logic_adapter = utils.initialize_class(adapter, self, **kwargs)
self.logic_adapters.append(logic_adapter)
preprocessors = kwargs.get(
'preprocessors', ['chatterbot.preprocessors.clean_whitespace'])
self.preprocessors = []
for preprocessor in preprocessors:
self.preprocessors.append(utils.import_module(preprocessor))
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()
