How do I execute my TensorFlow Realtime Object Detection model on a browser

Question

I'm a Machine learning beginner, working on my first ML project, I made a program that detects if people are wearing helmets in realtime (Using TensorFlow and OpenCV), now when I execute it, Python displays a new window using my camera, but now I want to execute it on a browser, just locally, I don't want to deploy it

This is the part of the code that does the execution and the display of the program :

while True: 
    ret, frame = cap.read()
    image_np = np.array(frame)
    
    input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)
    detections = detect_fn(input_tensor)
    
    num_detections = int(detections.pop('num_detections'))
    detections = {key: value[0, :num_detections].numpy()
                  for key, value in detections.items()}
    detections['num_detections'] = num_detections

    # detection_classes should be ints.
    detections['detection_classes'] = detections['detection_classes'].astype(np.int64)

    label_id_offset = 1
    image_np_with_detections = image_np.copy()

    viz_utils.visualize_boxes_and_labels_on_image_array(
                image_np_with_detections,
                detections['detection_boxes'],
                detections['detection_classes']+label_id_offset,
                detections['detection_scores'],
                category_index,
                use_normalized_coordinates=True,
                max_boxes_to_draw=5,
                min_score_thresh=.5,
                agnostic_mode=False)

    cv2.imshow('object detection',  cv2.resize(image_np_with_detections, (800, 600)))
    
    if cv2.waitKey(1) & 0xFF == ord('q'):
        cap.release()
        break

I don't know how to proceed from here, it works perfectly on my computer, but I don't know how I could switch to a browser, I don't know where to start from and would appreciate some help if possible

Answer 1

Try GoogleColab or Kaggle, both have what you need.

Answer 2

Try using Flask, you can execute it on a browser.