def classify(X):
global classifier
model_name = 'TC_GENERAL_V131'
model_path = ModelFetcher.get_model_path(model_name)
if not classifier:
if not os.path.exists(model_path):
ModelFetcher.download(model_name)
classifier = TextClassifier.load(model_path)
sentence = Sentence(X)
classifier.predict(sentence)
labels = sentence.labels
return labels
def sentiment(X):
global classifier
model_name = 'SA_BANK_V131'
model_path = ModelFetcher.get_model_path(model_name)
if not classifier:
if not os.path.exists(model_path):
ModelFetcher.download(model_name)
classifier = TextClassifier.load(model_path)
sentence = Sentence(X)
classifier.predict(sentence)
labels = sentence.labels
if not labels:
return None
labels = [label.value for label in labels]
return labels
def sentiment(X):
global classifier
model_name = 'SA_GENERAL_V131'
model_path = ModelFetcher.get_model_path(model_name)
if not classifier:
if not os.path.exists(model_path):
ModelFetcher.download(model_name)
classifier = TextClassifier.load(model_path)
sentence = Sentence(X)
classifier.predict(sentence)
labels = sentence.labels
try:
label_map = {'POS': 'positive', 'NEG': 'negative'}
label = label_map[labels[0]]
return label
except Exception:
return None
def download_model(model):
ModelFetcher.download(model)
