def run_predictions(sess, image_data, softmax_tensor, QP, idx, classNo, sheet, style, gt_label_list, wb):
    global row1
    # Input the image, obtain the softmax prob value（one shape=(1,1008) vector）
#    predictions = sess.run(softmax_tensor,{'DecodeJpeg/contents:0': image_data}) # n, m, 3
    predictions = sess.run(softmax_tensor,{'Cast:0': image_data}) # n, m, 3
    # (1, 1008)->(1008,)
    predictions = np.squeeze(predictions)

    # ID --> English string label.
    node_lookup = NodeLookup()
    N = -1008

    # Current_rank = -1
    current_rank = -1


    # print(gt_label_list[998:1005])
    # print(gt_label_list[idx], (idx))
    #（top-5)
    top_5 = predictions.argsort()[N:][::-1]
    for rank, node_id in enumerate(top_5):
        human_string = node_lookup.id_to_string(node_id)
        score = predictions[node_id]
        # if rank < 5:
        #     print('%d: %s (score = %.5f)' % (1 + rank, human_string, score))

        if(gt_label_list[idx+1] == human_string):
            print('%d: %s (score = %.5f)' % (1 + rank, human_string, score))

            # Write the rank and the score
            row = idx+1
            # col = 4 + 2 * int((QP % 22) / 5)
            col = 4

            # Append the coloumn
            for i in range(22, QP, 5):
                col = col + 2

            # Set the current rank (rank starts from 0)
            current_rank = 1 + rank

            # print(row, col)
            sheet.write(row, col, current_rank, style)
            sheet.write(row, 1 + col, score.item(), style)
            wb.save(path_to_excel)
            # Stop looping once you find it in the rank
            break
    col1 = 3
    # Append the coloumn
    for i in range(22, QP, 5):
        col1 = col1 + 3
    sheet1.write(row1, col1, current_rank, style1)
    sheet1.write(row1, 1 + col1, score.item(), style1)
    for idx1, rank_top5 in zip(range(row1+1,row1+6), top_5):
        sheet1.write(idx1, col1-1, int(rank_top5), style1)
        sheet1.write(idx1, col1, node_lookup.id_to_string(rank_top5), style1)
        sheet1.write(idx1, col1+1, float(predictions[rank_top5]), style1)
        print('Top-5 -- NOde_ID: %d : %s (score = %.20f)' % (int(rank_top5), node_lookup.id_to_string(rank_top5), float(predictions[rank_top5])))
    # wb1.save(path_to_excel1)
    return current_rank