web_tool/ModelSessionKerasExample.py [178:210]:
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    def save_state_to(self, directory):
        
        np.save(os.path.join(directory, "augment_x_train.npy"), np.array(self.augment_x_train))
        np.save(os.path.join(directory, "augment_y_train.npy"), np.array(self.augment_y_train))

        joblib.dump(self.augment_model, os.path.join(directory, "augment_model.p"))

        if self.augment_model_trained:
            with open(os.path.join(directory, "trained.txt"), "w") as f:
                f.write("")

        return {
            "message": "Saved model state", 
            "success": True
        }

    def load_state_from(self, directory):

        self.augment_x_train = []
        self.augment_y_train = []

        for sample in np.load(os.path.join(directory, "augment_x_train.npy")):
            self.augment_x_train.append(sample)
        for sample in np.load(os.path.join(directory, "augment_y_train.npy")):
            self.augment_y_train.append(sample)

        self.augment_model = joblib.load(os.path.join(directory, "augment_model.p"))
        self.augment_model_trained = os.path.exists(os.path.join(directory, "trained.txt"))

        return {
            "message": "Loaded model state", 
            "success": True
        }
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web_tool/ModelSessionRandomForest.py [120:152]:
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    def save_state_to(self, directory):
        
        np.save(os.path.join(directory, "augment_x_train.npy"), np.array(self.augment_x_train))
        np.save(os.path.join(directory, "augment_y_train.npy"), np.array(self.augment_y_train))

        joblib.dump(self.augment_model, os.path.join(directory, "augment_model.p"))

        if self.augment_model_trained:
            with open(os.path.join(directory, "trained.txt"), "w") as f:
                f.write("")

        return {
            "message": "Saved model state", 
            "success": True
        }

    def load_state_from(self, directory):

        self.augment_x_train = []
        self.augment_y_train = []

        for sample in np.load(os.path.join(directory, "augment_x_train.npy")):
            self.augment_x_train.append(sample)
        for sample in np.load(os.path.join(directory, "augment_y_train.npy")):
            self.augment_y_train.append(sample)

        self.augment_model = joblib.load(os.path.join(directory, "augment_model.p"))
        self.augment_model_trained = os.path.exists(os.path.join(directory, "trained.txt"))

        return {
            "message": "Loaded model state", 
            "success": True
        }
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