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})