Can't test on single image with model yolov8

[DungHD-1997]

Describe the bug

AttributeError: 'NoneType' object has no attribute 'infer'
I understand that there does not exist attribute infer from yolo code but why can the tutorial output the results?

Environment information
my environment information:

      - jinja2==3.1.4
      - jupyterlab-widgets==3.0.10
      - kiwisolver==1.4.5
      - markupsafe==2.1.5
      - matplotlib==3.8.4
      - matplotlib-inline==0.1.7
      - mkl==2021.4.0
      - mpmath==1.3.0
      - networkx==3.3
      - numpy==1.26.4
      - onnx==1.16.0
      - opencv-python==4.9.0.80
      - openvino==2024.1.0
      - openvino-telemetry==2024.1.0
      - packaging==24.0
      - pandas==2.2.2
      - parso==0.8.4
      - pillow==10.3.0
      - prompt-toolkit==3.0.43
      - protobuf==5.26.1
      - psutil==5.9.8
      - pure-eval==0.2.2
      - py-cpuinfo==9.0.0
      - pygments==2.18.0
      - pyparsing==3.1.2
      - python-dateutil==2.9.0.post0
      - pytz==2024.1
      - pyyaml==6.0.1
      - requests==2.31.0
      - scipy==1.13.0
      - seaborn==0.13.2
      - six==1.16.0
      - stack-data==0.6.3
      - sympy==1.12
      - tbb==2021.12.0
      - thop==0.1.1-2209072238
      - torch==2.3.0+cpu
      - torchvision==0.18.0+cpu
      - tqdm==4.66.4
      - traitlets==5.14.3
      - typing-extensions==4.11.0
      - tzdata==2024.1
      - ultralytics==8.1.42
      - urllib3==2.2.1
      - wcwidth==0.2.13
      - widgetsnbextension==4.0.10

My code is based on instructions from the following website:
https://docs.openvino.ai/2024/notebooks/yolov8-object-detection-with-output.html#test-on-single-image

import ipywidgets as widgets
import openvino as ov

from PIL import Image
from ultralytics import YOLO

models_dir = "C:/Project/result_train/yolov8/clean_data_obj/weights/best.pt"
det_model = YOLO(models_dir)
label_map = det_model.model.names

IMAGE_PATH = "C:/Project/test_img_truck/image_2023-12-25_07z37z31.jpg"
core = ov.Core()
device = widgets.Dropdown(
    options=core.available_devices + ["AUTO"],
    value="AUTO",
    description="Device:",
    disabled=False,
)
import torch
core = ov.Core()
det_model_path = "C:/Project/result_train/yolov8/clean_data_obj/weights/best_openvino_model/best.xml"
det_ov_model = core.read_model(det_model_path)

ov_config = {}
if device.value != "CPU":
    det_ov_model.reshape({0: [1, 3, 640, 640]})
if "GPU" in device.value or ("AUTO" in device.value and "GPU" in core.available_devices):
    ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
det_compiled_model = core.compile_model(det_ov_model, device.value, ov_config)

def infer(*args):
    result = det_compiled_model(args)
    return torch.from_numpy(result[0])

det_model.predictor.infer = infer
det_model.predict.model.pt = False

res = det_model(IMAGE_PATH)
# Image.fromarray(res[0].plot()[:, :, ::-1])
for i, r in enumerate(res):
    # Plot results image
    im_bgr = r.plot()  # BGR-order numpy array
    im_rgb = Image.fromarray(im_bgr[..., ::-1])  # RGB-order PIL image

    # Show results to screen (in supported environments)
    r.show()

[DungHD-1997]

I changed my code according to the instructions here:
https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/230-yolov8-optimization/230-yolov8-object-detection.ipynb
it worked!