def predict():
"""
Image Classification:
{
"type": "images",
"urls": ["https://my-url.com/image.jpg"],
}
Text Classification:
{
"type": "texts",
"texts": ["the text that i want to classify"],
}
Sequence Classification:
{
"type": "sequence",
"texts": ["the sequence that i want to label"],
}
"""
json = request.get_json()
_type = json['type']
if _type == "images":
urls = json["urls"]
x_train, images = utils.download_urls(urls)
predictions = label_app.predict(x_train)
elif _type == "text" or _type == "sequence":
texts = json["texts"]
predictions = label_app.predict(texts)
return jsonify({'predictions': predictions.tolist(), **json})
def score():
"""
Image Classification:
{
"type": "images",
"urls": ["https://my-url.com/image.jpg"],
}
Text Classification:
{
"type": "texts",
"texts": ["the text that i want to classify"],
}
Sequence Classification:
{
"type": "sequence",
"texts": ["the sequence that i want to label"],
}
"""
json = request.get_json()
_type = json['type']
if _type == "images":
urls = json["urls"]
x_train, images = utils.download_urls(urls)
scores = label_app.score(x_train)
elif _type == "text" or _type == "sequence":
texts = json["texts"]
scores = label_app.score(texts)
return jsonify({'scores': scores.tolist(), 'labels': label_app.label_helper.classes})