with tf.Session(graph=graph) as sess:
    while True:
        a = []
        try:
            files = os.listdir(folder_name)

            for f in files:
                try:
                    #CALLING IMAGE READ FUNCTION
                    t = sess.run(read_tensor_from_image_file(
                        (folder_name+"/"+f),
                        input_height=input_height,
                        input_width=input_width,
                        input_mean=input_mean,
                        input_std=input_std))

                    #GETTING INFERENCE
                    results = sess.run(output_operation.outputs[0], {
                        input_operation.outputs[0]: t
                    })



                    results = np.squeeze(results)

                    top_k = results.argsort()[-5:][::-1]

def read_tensor_from_image_file(file_name,
                            input_height=299,
                            input_width=299,
                            input_mean=0,
                            input_std=255):
input_name = "file_reader"
output_name = "normalized"
file_reader = tf.read_file(file_name, input_name)
if file_name.endswith(".png"):
    image_reader = tf.image.decode_png(
        file_reader, channels=3, name="png_reader")
elif file_name.endswith(".gif"):
    image_reader = tf.squeeze(
        tf.image.decode_gif(file_reader, name="gif_reader"))
elif file_name.endswith(".bmp"):
    image_reader = tf.image.decode_bmp(file_reader, name="bmp_reader")
else:
    image_reader = tf.image.decode_jpeg(
        file_reader, channels=3, name="jpeg_reader")
float_caster = tf.cast(image_reader, tf.float32)
dims_expander = tf.expand_dims(float_caster, 0)
resized = tf.image.resize_bilinear(
    dims_expander, [input_height, input_width])
normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])


return normalized