def predict()

in notebooks/modelscript_tensorflow_lite.py [0:0]

• 19 lines of code
• 3 McCabe index (conditional complexity)

def predict(model, payload):
    try:
        img = Image.frombytes('RGB', (224,224), payload, 'raw')# For Multi model endpoints -> [payload[0]['body'].decode()]
        
        #img = np.frombuffer(data, dtype=np.uint8).reshape((224, 224))
        
        input_details = model.get_input_details()
        output_details = model.get_output_details()

        # check the type of the input tensor
        floating_model = input_details[0]['dtype'] == np.float32

        # NxHxWxC, H:1, W:2
        height = input_details[0]['shape'][1]
        width = input_details[0]['shape'][2]
        #img = Image.open(image_file).resize((width, height))

        # add N dim
        input_data = np.expand_dims(img, axis=0)

        if floating_model:
            input_data = (np.float32(input_data) - input_mean) / input_std

        model.set_tensor(input_details[0]['index'], input_data)

        model.invoke()

        output_data = model.get_tensor(output_details[0]['index'])
        results = np.squeeze(output_data)

        out = results.tolist()
    except Exception as e:
        out = str(e)
    return [json.dumps({'output':[out],'tfeager': tf.executing_eagerly()})]