text: # Import the necessary dependencies
  from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
  from tensorflow.keras.preprocessing.image import img_to_array
  from tensorflow.keras.models import load_model
  from imutils.video import VideoStream
  from playsound import playsound
  import numpy as np
  import requests
  import imutils
  import time
  import cv2
  import os
  
  # Create a function to detect faces from an image and return the coordinates of the bounding box and the perdiction score
  def detect_and_predict_mask(frame, faceNet, maskNet):
  	# Grab the dimensions of the frame and then construct a blob from it
  	(h, w) = frame.shape[:2]
  	blob = cv2.dnn.blobFromImage(frame, 1.0, (224, 224), (104.0, 177.0, 123.0))
  
  	# Pass the blob through the network and obtain the face detections
  	faceNet.setInput(blob)
  	detections = faceNet.forward()
  
  	# Initialize our list of faces, their corresponding locations, and the list of predictions from our face mask network
  	faces = []
  	locs = []
  	preds = []
  
  	# Loop over the detections
  	for i in range(0, detections.shape[2]):
  		# Extract the confidence (i.e., probability) associated with the detection
  		confidence = detections[0, 0, i, 2]
  
  		# Filter out weak detections by ensuring the confidence is greater than the minimum confidence
  		if confidence > 0.5:
  			# Compute the (x, y)-coordinates of the bounding box for the object
  			box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
  			(startX, startY, endX, endY) = box.astype("int")
  
  			# Ensure the bounding boxes fall within the dimensions of the frame
  			(startX, startY) = (max(0, startX), max(0, startY))
  			(endX, endY) = (min(w - 1, endX), min(h - 1, endY))
  
  			# Extract the face ROI, convert it from BGR to RGB channel ordering, resize it to 224 x 224, and preprocess it
  			face = frame[startY:endY, startX:endX]
  			face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
  			face = cv2.resize(face, (224, 224))
  			face = img_to_array(face)
  			face = preprocess_input(face)
  
  			# Add the face and bounding boxes to their respective lists
  			faces.append(face)
  			locs.append((startX, startY, endX, endY))
  
  	# Only make a predictions if at least one face was detected
  	if len(faces) > 0:
  		# For faster inference we'll make batch predictions on *all*
  		# faces at the same time rather than one-by-one predictions in the above `for` loop
  		faces = np.array(faces, dtype="float32")
  		preds = maskNet.predict(faces, batch_size=32)
  
  	# Return a 2-tuple of the face locations and their corresponding locations
  	return (locs, preds)
  
  # Load our serialized face detector model from disk
  prototxtPath = r"models\deploy.prototxt"
  weightsPath = r"models\res10_300x300_ssd_iter_140000.caffemodel"
  # Get the facenet ready
  faceNet = cv2.dnn.readNetFromCaffe(prototxtPath, weightsPath)
  
  # Get the URL from IPWebcam
  url = "http://<YOUR_IP_HERE>/shot.jpg"
  
  # Load the face mask detector model that has been trained, from disk
  maskNet = load_model("models/mask_detector.model")
  
  # Initialize the video stream
  print("[INFO] Starting Video Stream...")
  vs = VideoStream(src = 0).start()
  # Wait for 2 seconds for the camera to get ready
  time.sleep(2)
  
  # Loop over the frames from the video stream
  while True:
  	# Grab the frame from the threaded video stream and resize it to have a maximum width of 600 pixels
  	img_resp = requests.get(url)
  	img_arr = np.array(bytearray(img_resp.content), dtype = np.uint8)
  	frame = cv2.imdecode(img_arr, -1)
  	frame = cv2.resize(frame, (600, 600))
  	# Rotate the frame
  	frame = cv2.rotate(frame, cv2.ROTATE_90_CLOCKWISE)
  
  	# Detect faces in the frame and determine if they are wearing a face mask or not
  	(locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)
  
  	# Loop over the detected face locations and their corresponding locations
  	for (box, pred) in zip(locs, preds):
  		# Unpack the bounding box and predictions
  		(startX, startY, endX, endY) = box
  		(mask, withoutMask) = pred
  
  		# Determine the class label and color we'll use to draw the bounding box and text
  		label = "Mask" if mask > withoutMask else "No Mask"
  		if label == "Mask":
  			color = (0, 255, 0) 
  			
  		else:
  			color = (0, 0, 255)
  			# Play any alert sound(Playing sound makes the stream slower, so this is optional)
  			# playsound('audio/alarm.mp3')
  
  		# Include the probability in the label
  		label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)
  
  		# Display the label and bounding box rectangle on the output frame
  		cv2.putText(frame, label, (startX, startY - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
  		cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
  
  	# Show the output frame
  	cv2.imshow("Output", frame)
  	key = cv2.waitKey(1) & 0xFF
  
  	# If the `q` key was pressed, break from the loop
  	if key == ord("q"):
  		print("[INFO] Ending Video Stream...")
  		break
  
  # Do a bit of cleanup
  cv2.destroyAllWindows()
  vs.stop()
  