def process(
self,
message: Message,
config: Optional[RasaNLUModelConfig] = None,
**kwargs: Any,
) -> None:
"""Process an incoming message."""
APOSTROPHE_OPTIONS = ["'", "`"]
# removing accent and lowercasing characters
message.text = unidecode(message.text.lower())
# remove apostrophe from the phrase (important be first than s_regex regex)
for APOSTROPHE in APOSTROPHE_OPTIONS:
message.text = message.text.replace(APOSTROPHE, "")
# set regex parameters
n_regex = r"\b(n|N)\1*\b"
s_regex = r"\b(s|S)\1*\b"
# set replace words
S_WORD = "sim"
N_WORD = "nao"
# replace regex by "sim"
message.text = re.sub(s_regex, S_WORD, message.text)
# replace regex by "nao"
message.text = re.sub(n_regex, N_WORD, message.text)
def process(self, message: Message, **kwargs: Any) -> None:
"""Process an incoming message."""
APOSTROPHE_OPTIONS = ["'", "`"]
# remove apostrophe from the phrase (important be first than s_regex regex)
for APOSTROPHE in APOSTROPHE_OPTIONS:
message.text = message.text.replace(APOSTROPHE, "")
PREPROCESS_FACTORY = PreprocessingFactory().get_preprocess(
self.language)
message.text = PREPROCESS_FACTORY.preprocess(message.text)
def process(self, message: Message, **kwargs: Any) -> None:
"""Process an incoming message."""
language_preprocessor = PreprocessingFactory(self.language).factory()
_message = language_preprocessor.preprocess(message)
message.text = _message.text
message.data = _message.data
def preprocess(self, example: Message) -> Message:
phrase = example.text
entities = example.data.get('entities')
phrase = self.emoji_handling(phrase)
phrase, entities = self.default_preprocessing(phrase, entities)
example.text = phrase
if entities:
example.data['entities'] = entities
return example
def tokenize(self, message: Message, attribute: Text) -> List[Token]:
# extract the visual information from the text information
if LANG_VISUAL_DELIMITER in message.text:
visual_data_string = message.text[message.text.index(LANG_VISUAL_DELIMITER) + 1 + len(LANG_VISUAL_DELIMITER):]
message.set('visual_info', visual_data_string.strip())
message.text = message.text[0: message.text.index(LANG_VISUAL_DELIMITER)]
doc = self.get_doc(message, attribute)
tokens = []
for t in doc:
# do not send the visual information to the feedforward layers
# that lead to the transformer module
if LANG_VISUAL_DELIMITER in t.text:
break
tokens.append(Token(t.text, t.idx, lemma=t.lemma_, data={POS_TAG_KEY: self._tag_of_token(t)}))
return tokens
def process(self, message: Message, **kwargs: Any) -> None:
print(
"Process an incoming message.....................................")
"""
This is the components chance to process an incoming
message. The component can rely on
any context attribute to be present, that gets created
by a call to :meth:`components.Component.pipeline_init`
of ANY component and
on any context attributes created by a call to
:meth:`components.Component.process`
of components previous to this one."""
input = message.text.lower()
print("msggggggggg..................:", input)
input = input.replace("+", " add ").replace("-", " subtract ").replace(
"*", " multiply ").replace("/", " divide ").replace(
"(", " left ").replace(")", " right ")
message.text = input
print("msgggg.............", message.text)
#duck.parse(message.text)
pass
def process(self, message: Message, **kwargs: Any) -> None:
for spell_typo, spell_correct in self.typos.items():
message.text = message.text.replace(spell_typo, spell_correct)
