pep8

chatter/chatterbot/__main__.py

@@ -1,6 +1,5 @@
 import sys
 
-
 if __name__ == '__main__':
     import importlib
 

chatter/chatterbot/corpus.py

@@ -5,7 +5,6 @@ View the corpus on GitHub at https://github.com/gunthercox/chatterbot-corpus
 
 from chatterbot_corpus import Corpus
 
-
 __all__ = (
     'Corpus',
 )

chatter/chatterbot/ext/sqlalchemy_app/models.py

@@ -29,7 +29,6 @@ class ModelBase(object):
 
 Base = declarative_base(cls=ModelBase)
 
-
 tag_association_table = Table(
     'tag_association',
     Base.metadata,

chatter/chatterbot/input/microsoft.py

@@ -23,10 +23,10 @@ class Microsoft(InputAdapter):
 
         # NOTE: Direct Line client credentials are different from your bot's
         # credentials
-        self.direct_line_token_or_secret = kwargs.\
+        self.direct_line_token_or_secret = kwargs. \
             get('direct_line_token_or_secret')
 
-        authorization_header = 'BotConnector  {}'.\
+        authorization_header = 'BotConnector  {}'. \
             format(self.direct_line_token_or_secret)
 
         self.headers = {
@@ -64,7 +64,7 @@ class Microsoft(InputAdapter):
     def get_most_recent_message(self):
         import requests
 
-        endpoint = '{host}/api/conversations/{id}/messages'\
+        endpoint = '{host}/api/conversations/{id}/messages' \
             .format(host=self.directline_host,
                     id=self.conversation_id)
 

chatter/chatterbot/input/variable_input_type_adapter.py

@@ -5,11 +5,10 @@ from chatter.chatterbot.input import InputAdapter
 
 
 class VariableInputTypeAdapter(InputAdapter):
-
     JSON = 'json'
     TEXT = 'text'
     OBJECT = 'object'
-    VALID_FORMATS = (JSON, TEXT, OBJECT, )
+    VALID_FORMATS = (JSON, TEXT, OBJECT,)
 
     def detect_type(self, statement):
 

chatter/chatterbot/logic/multi_adapter.py

@@ -51,7 +51,7 @@ class MultiLogicAdapter(LogicAdapter):
             if adapter.can_process(statement):
 
                 output = adapter.process(statement)
-                results.append((output.confidence, output, ))
+                results.append((output.confidence, output,))
 
                 self.logger.info(
                     '{} selected "{}" as a response with a confidence of {}'.format(

chatter/chatterbot/storage/storage_adapter.py

@@ -24,12 +24,12 @@ class StorageAdapter(object):
         # The string must be lowercase
         model_name = model_name.lower()
 
-        kwarg_model_key = '%s_model' % (model_name, )
+        kwarg_model_key = '%s_model' % (model_name,)
 
         if kwarg_model_key in self.kwargs:
             return self.kwargs.get(kwarg_model_key)
 
-        get_model_method = getattr(self, 'get_%s_model' % (model_name, ))
+        get_model_method = getattr(self, 'get_%s_model' % (model_name,))
 
         return get_model_method()
 
@@ -157,7 +157,8 @@ class StorageAdapter(object):
 
     class EmptyDatabaseException(Exception):
 
-        def __init__(self, value='The database currently contains no entries. At least one entry is expected. You may need to train your chat bot to populate your database.'):
+        def __init__(self,
+                     value='The database currently contains no entries. At least one entry is expected. You may need to train your chat bot to populate your database.'):
             self.value = value
 
         def __str__(self):

chatter/chatterbot/trainers.py

@@ -393,7 +393,6 @@ class UbuntuCorpusTrainer(Trainer):
 
         file_kwargs = {}
 
-
         # Specify the encoding in Python versions 3 and up
         file_kwargs['encoding'] = 'utf-8'
         # WARNING: This might fail to read a unicode corpus file in Python 2.x

chatter/info.json

@@ -1,10 +1,30 @@
 {
-    "author" : ["Bobloy"],
+  "author": [
-    "bot_version" : [3,0,0],
+    "Bobloy"
-    "description" : "Create an offline chatbot that talks like your average member using Machine Learning",
+  ],
-    "hidden" : false,
+  "bot_version": [
-    "install_msg" : "Thank you for installing Chatter!",
+    3,
-    "requirements" : ["sqlalchemy<1.3,>=1.2", "python-twitter<4.0,>=3.0", "python-dateutil<2.7,>=2.6", "pymongo<4.0,>=3.3", "nltk<4.0,>=3.2", "mathparse<0.2,>=0.1", "chatterbot-corpus<1.2,>=1.1"],
+    0,
-    "short" : "Local Chatbot run on machine learning",
+    0
-    "tags" : ["chat", "chatbot", "cleverbot", "clever","bobloy"]
+  ],
+  "description": "Create an offline chatbot that talks like your average member using Machine Learning",
+  "hidden": false,
+  "install_msg": "Thank you for installing Chatter!",
+  "requirements": [
+    "sqlalchemy<1.3,>=1.2",
+    "python-twitter<4.0,>=3.0",
+    "python-dateutil<2.7,>=2.6",
+    "pymongo<4.0,>=3.3",
+    "nltk<4.0,>=3.2",
+    "mathparse<0.2,>=0.1",
+    "chatterbot-corpus<1.2,>=1.1"
+  ],
+  "short": "Local Chatbot run on machine learning",
+  "tags": [
+    "chat",
+    "chatbot",
+    "cleverbot",
+    "clever",
+    "bobloy"
+  ]
 }