application.py

class convert_to_simlarity:
    def __init__(self):
        self.output = {"target": None, "texts": None}

    def from_texts(self, target, texts):
        self.output["target"] = target
        self.output["texts"] = texts
        self.output["sims"] = self.texts2similarity()

        return self.output

    def texts2similarity(self):
        # get futures per sentence
        body = [self.output["target"]] + self.output["texts"]
        raw_features = get_futures(BERT_PRAMS, body)

        # extract "[CLS]" features
        cls_features = []
        for raw_feature in raw_features:
            cls_feature = list(
                filter(
                    lambda layer:
                        layer["token"] == "[CLS]",
                        raw_feature["features"]
                )
            )
            cls_features.append(cls_feature[0])

        # compute cosine simlarity
        simlarities = calc_simlarity(cls_features[0], cls_features[1:])

        return simlarities


req = convert_to_simlarity()

################
### omission ###
################

application = Flask(__name__)

application.add_url_rule(
    "/sim",
    "similarity",
    (
        lambda: jsonify(
            {
                "type": "sentence similarity",
                "context": req.from_texts(
                    request.get_json()["target"],
                    request.get_json()["texts"],
                ),
            }
        )
    ),
    methods=["POST"],
)