model.py

from tensorflow.keras.models import model_from_json
from tensorflow.python.keras.backend import set_session
import numpy as np

import tensorflow as tf

config = tf.compat.v1.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.15
session = tf.compat.v1.Session(config=config)
set_session(session)


class FacialExpressionModel(object):

    EMOTIONS_LIST = ["calling", "clapping",
                     "cycling",
                     "dancing", "drinking",
                     "eating", "fighting",
                     "hugging", "laughing",
                     "listening_to_music",
                     "running",
                     "sitting",
                     "sleeping",
                     "texting",
                     "using_laptop"]

    def __init__(self, model_json_file, model_weights_file):
        # load model from JSON file
        with open(model_json_file, "r") as json_file:
            loaded_model_json = json_file.read()
            self.loaded_model = model_from_json(loaded_model_json)

        # load weights into the new model
        self.loaded_model.load_weights(model_weights_file)
        # self.loaded_model.compile()
        # self.loaded_model._make_predict_function()

    def predict_emotion(self, img):
        global session
        set_session(session)
        self.preds = self.loaded_model.predict(img)
        return FacialExpressionModel.EMOTIONS_LIST[np.argmax(self.preds)]