def summarizeText(client, texto: str) -> None:
print(optionsTitle('--summarize'))
translatedText: str = traduzir('en', texto)
algoritimo = client.algo('nlp/Summarizer/0.1.8')
response: str = algoritimo.pipe(translatedText).result
print(traduzir('pt', response))
def summarizeText(client: 'Client', text: str) -> NoReturn:
from utils.translate import traduzir
from interface import texts
print(texts.optionsTitle('summarize'))
translatedText: str = traduzir('en', text)
algoritimo = client.algo('nlp/Summarizer/0.1.8')
response: str = algoritimo.pipe(translatedText).result
print(traduzir('pt', response))
def entityRecognition(client, text: str) -> None:
print(optionsTitle('--entity'))
translatedText: str
baseText: Dict[str, str]
response: Dict[str, list]
numberOfEntityFound: int
wordlist: List[dict]
if text:
translatedText = traduzir('en', text)
baseText = {"document": translatedText}
algoritimo = client.algo('StanfordNLP/NamedEntityRecognition/0.2.0')
response = algoritimo.pipe(baseText).result
numberOfEntityFound = len(response['sentences'])
wordlist = response['sentences'][numberOfEntityFound -
1]['detectedEntities']
if not wordlist:
print("Não foram encontradas nenhuma entidade.")
sys.exit(1)
else:
for name in wordlist:
print(
f'Nome: {name["word"]} \nEntidade: {traduzir("pt", name["entity"]).capitalize()}'
)
else:
print("Texto em branco.")
def feelingAnalisys(client, sentenca: str) -> None:
print(optionsTitle('--feeling'))
text: Dict[str, str] = {"sentence": traduzir('en', sentenca)}
algoritimo = client.algo('nlp/SocialSentimentAnalysis/0.1.4')
response: List[dict] = algoritimo.pipe(text).result
positive: int = math.floor(response[0]['positive'] * 100)
negative: int = math.floor(response[0]['negative'] * 100)
neutral: int = math.floor(response[0]['neutral'] * 100)
print("Positivi: {}%\nNegativo: {}%\nNeutro: {}%".format(
positive, negative, neutral))
def dateExtractor(client: 'Client', text: str) -> NoReturn:
from utils.translate import traduzir
from interface import texts
print(texts.optionsTitle('date'))
translatedText = traduzir('en', text)
algorithm = client.algo('PetiteProgrammer/DateExtractor/0.1.0')
response: List[str] = algorithm.pipe(translatedText).result
if len(response) > 0:
for date in response:
print(date)
else:
print('Nenhuma data encontrada')
def feelingAnalisys(client: 'Client', text: str) -> NoReturn:
import math
from utils.translate import traduzir
from interface import texts
print(texts.optionsTitle('feeling'))
translatedText: Dict[str, str] = {"sentence": traduzir('en', text)}
algoritimo = client.algo('nlp/SocialSentimentAnalysis/0.1.4')
response: List[dict] = algoritimo.pipe(translatedText).result
positive: int = math.floor(response[0]['positive'] * 100)
negative: int = math.floor(response[0]['negative'] * 100)
neutral: int = math.floor(response[0]['neutral'] * 100)
print("Positivi: {}%\nNegativo: {}%\nNeutro: {}%".format(
positive, negative, neutral))