what is wrong with this code

import cv2
import torch
from PIL import Image
from transformers import DetrForObjectDetection, DetrImageProcessor
from PIL import ImageDraw
import numpy as np
import datetime

# Load the DETR model and image processor
model = DetrForObjectDetection.from_pretrained('facebook/detr-resnet-101')
processor = DetrImageProcessor.from_pretrained('facebook/detr-resnet-101')

# Open the webcam
cap = cv2.VideoCapture(0)

# Initialize person counter
person_counter = 0

# Initialize a set to keep track of the detected persons in the current frame
detected_persons = set()

while True:
    # Capture frame-by-frame from the webcam
    ret, frame = cap.read()

    # Convert the OpenCV BGR image to RGB
    image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))

    # Process the image with the image processor to create inputs compatible with the model
    inputs = processor(image, return_tensors="pt")

    # Run the model on the processed inputs to get the object detection results
    outputs = model(**inputs)

    # Convert outputs (bounding boxes and class logits) to COCO API format
    target_sizes = torch.tensor([image.size[::-1]])
    results = processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[0]

    # Get the bounding boxes, labels, and scores from the results
    bounding_boxes = results["boxes"]
    labels = results["labels"]
    scores = results["scores"]

    # Draw the bounding boxes on the image and display the labels and scores
    draw = ImageDraw.Draw(image)
    for score, label, box in zip(scores, labels, bounding_boxes):
        box = [round(i, 2) for i in box.tolist()]
        label_text = model.config.id2label[label.item()]
        confidence = round(score.item(), 3)
        label_text_with_confidence = f"{label_text} ({confidence})"
        draw.rectangle(box, outline="red", width=3)
        draw.text((box[0], box[1]), label_text_with_confidence, fill="red")

        # Check if the detected object is a person
        if label_text == "person":
            # Check if the person has already been counted in the current frame
            if label_text not in detected_persons:
                # Increment person counter
                person_counter += 1

                # Add the person to the set of detected persons in the current frame
                detected_persons.add(label_text)

                # Get the current time
                current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
                print("Danger Provide your response in exactly 4 paragraphs (separated nearby paragraphs with 3 aterisk symbols ***).
user