ChatBot是 1.1.0版本
# 导入库文件
from chatterbot import ChatBot
from chatterbot.trainers import ChatterBotCorpusTrainer
import os
# 创建一个ChatBot人工智能框架
bot = ChatBot('Candice')
# 使用语料库数据训练
trainer = ChatterBotCorpusTrainer(bot)
# 在英文训练数据基础上
trainer.train("chatterbot.corpus.english")
# 对话规则
while True:
message = input("\t\t\tYou:")
if message.strip() != "Bey":
reply = bot.get_response(message)
print("Candice:", reply)
if message.strip() == "Bey":
print("Candice:Bye")
break
报错信息
D:\venv\Scripts\python.exe D:\chat1.py
Traceback (most recent call last):
File "D:\chat1.py", line 7, in <module>
bot = ChatBot('Candice')
File "D:\venv\lib\site-packages\chatterbot\chatterbot.py", line 28, in __init__
self.storage = utils.initialize_class(storage_adapter, **kwargs)
File "D:\venv\lib\site-packages\chatterbot\utils.py", line 33, in initialize_class
return Class(*args, **kwargs)
File "D:\venv\lib\site-packages\chatterbot\storage\sql_storage.py", line 20, in __init__
super().__init__(**kwargs)
File "D:\venv\lib\site-packages\chatterbot\storage\storage_adapter.py", line 23, in __init__
'tagger_language', languages.ENG
File "D:\venv\lib\site-packages\chatterbot\tagging.py", line 26, in __init__
self.nlp = spacy.load(self.language.ISO_639_1.lower())
File "D:\venv\lib\site-packages\spacy\__init__.py", line 27, in load
return util.load_model(name, **overrides)
File "D:\venv\lib\site-packages\spacy\util.py", line 139, in load_model
raise IOError(Errors.E050.format(name=name))
OSError: [E050] Can't find model 'en'. It doesn't seem to be a shortcut link, a Python package or a valid path to a data directory.
Process finished with exit code 1
import logging
from chatterbot.storage import StorageAdapter
from chatterbot.logic import LogicAdapter
from chatterbot.search import TextSearch, IndexedTextSearch
from chatterbot import utils
class ChatBot(object):
"""
A conversational dialog chat bot.
"""
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 = []
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 get_response(self, statement=None, **kwargs):
"""
Return the bot's response based on the input.
:param statement: An statement object or string.
:returns: A response to the input.
:rtype: Statement
:param additional_response_selection_parameters: Parameters to pass to the
chat bot's logic adapters to control response selection.
:type additional_response_selection_parameters: dict
:param persist_values_to_response: Values that should be saved to the response
that the chat bot generates.
:type persist_values_to_response: dict
"""
Statement = self.storage.get_object('statement')
additional_response_selection_parameters = kwargs.pop('additional_response_selection_parameters', {})
persist_values_to_response = kwargs.pop('persist_values_to_response', {})
if isinstance(statement, str):
kwargs['text'] = statement
if isinstance(statement, dict):
kwargs.update(statement)
if statement is None and 'text' not in kwargs:
raise self.ChatBotException(
'Either a statement object or a "text" keyword '
'argument is required. Neither was provided.'
)
if hasattr(statement, 'serialize'):
kwargs.update(**statement.serialize())
tags = kwargs.pop('tags', [])
text = kwargs.pop('text')
input_statement = Statement(text=text, **kwargs)
input_statement.add_tags(*tags)
# Preprocess the input statement
for preprocessor in self.preprocessors:
input_statement = preprocessor(input_statement)
# Make sure the input statement has its search text saved
if not input_statement.search_text:
input_statement.search_text = self.storage.tagger.get_text_index_string(input_statement.text)
if not input_statement.search_in_response_to and input_statement.in_response_to:
input_statement.search_in_response_to = self.storage.tagger.get_text_index_string(input_statement.in_response_to)
response = self.generate_response(input_statement, additional_response_selection_parameters)
# Update any response data that needs to be changed
if persist_values_to_response:
for response_key in persist_values_to_response:
response_value = persist_values_to_response[response_key]
if response_key == 'tags':
input_statement.add_tags(*response_value)
response.add_tags(*response_value)
else:
setattr(input_statement, response_key, response_value)
setattr(response, response_key, response_value)
if not self.read_only:
self.learn_response(input_statement)
# Save the response generated for the input
self.storage.create(**response.serialize())
return response
def generate_response(self, input_statement, additional_response_selection_parameters=None):
"""
Return a response based on a given input statement.
:param input_statement: The input statement to be processed.
"""
Statement = self.storage.get_object('statement')
results = []
result = None
max_confidence = -1
for adapter in self.logic_adapters:
if adapter.can_process(input_statement):
output = adapter.process(input_statement, additional_response_selection_parameters)
results.append(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)
)
class ResultOption:
def __init__(self, statement, count=1):
self.statement = statement
self.count = count
# If multiple adapters agree on the same statement,
# then that statement is more likely to be the correct response
if len(results) >= 3:
result_options = {}
for result_option in results:
result_string = result_option.text + ':' + (result_option.in_response_to or '')
if result_string in result_options:
result_options[result_string].count += 1
if result_options[result_string].statement.confidence < result_option.confidence:
result_options[result_string].statement = result_option
else:
result_options[result_string] = ResultOption(
result_option
)
most_common = list(result_options.values())[0]
for result_option in result_options.values():
if result_option.count > most_common.count:
most_common = result_option
if most_common.count > 1:
result = most_common.statement
response = Statement(
text=result.text,
in_response_to=input_statement.text,
conversation=input_statement.conversation,
persona='bot:' + self.name
)
response.confidence = result.confidence
return response
def learn_response(self, statement, previous_statement=None):
"""
Learn that the statement provided is a valid response.
"""
if not previous_statement:
previous_statement = statement.in_response_to
if not previous_statement:
previous_statement = self.get_latest_response(statement.conversation)
if previous_statement:
previous_statement = previous_statement.text
previous_statement_text = previous_statement
if not isinstance(previous_statement, (str, type(None), )):
statement.in_response_to = previous_statement.text
elif isinstance(previous_statement, str):
statement.in_response_to = previous_statement
self.logger.info('Adding "{}" as a response to "{}"'.format(
statement.text,
previous_statement_text
))
# Save the input statement
return self.storage.create(**statement.serialize())
def get_latest_response(self, conversation):
"""
Returns the latest response in a conversation if it exists.
Returns None if a matching conversation cannot be found.
"""
from chatterbot.conversation import Statement as StatementObject
conversation_statements = list(self.storage.filter(
conversation=conversation,
order_by=['id']
))
# Get the most recent statement in the conversation if one exists
latest_statement = conversation_statements[-1] if conversation_statements else None
if latest_statement:
if latest_statement.in_response_to:
response_statements = list(self.storage.filter(
conversation=conversation,
text=latest_statement.in_response_to,
order_by=['id']
))
if response_statements:
return response_statements[-1]
else:
return StatementObject(
text=latest_statement.in_response_to,
conversation=conversation
)
else:
# The case that the latest statement is not in response to another statement
return latest_statement
return None
class ChatBotException(Exception):
pass
