How to share a Tensorflow Keras model with a Flask route function?

Does tensorflow maintains its own internal global state, which is broken by loading the model in one function and trying to use it in another?

Using singleton for storing model:

class Singleton(object):
    _instances = {}

    def __new__(class_, *args, **kwargs):
        if class_ not in class_._instances:
            class_._instances[class_] = super(Singleton, class_).__new__(class_, *args, **kwargs)
        return class_._instances[class_]


class Context(Singleton):
    pass

When I do:

@app.route('/file', methods=['GET', 'POST'])
def upload_file():
    if request.method == 'POST':
        file = request.files['file']
        if file and allowed_file(file.filename):
            # filename = secure_filename(file.filename)
            filename = file.filename
            filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
            file.save(filepath)

            context = Context()
            if context.loaded:

                img = cv2.imread(filepath)
                img = cv2.resize(img, (96, 96))
                img = img.astype("float") / 255.0
                img = img_to_array(img)
                img = np.expand_dims(img, axis=0)

                classes = context.model.predict(img)

def api_run():
    context = Context()
    context.model = load_model('model.h5')
    context.loaded = True

I'm getting some error: ValueError: Tensor Tensor("dense_1/Softmax:0", shape=(?, 2), dtype=float32) is not an element of this graph.

However if I will move context.model = load_model('model.h5') inside upload_file function then everything will work. Why is that happening? How to store model for later use?