When I try to declare a global variable it throws the error Statement expected

Good evening! I need a global variable in a function to be used in another function, however, when I try to declare this variable as a global variable, it throws the error "Statement expected, found Py:EQ", this in the line where the global code snippet is id, confidence = recognizer.predict(faceimage) specifically above the = sign on line 53. How do I fix this error?

# install opencv "pip install opencv-python"
import cv2

# distance from camera to object(face) measured
# centimeter
Known_distance = 76.2

# width of face in the real world or Object Plane
# centimeter
Known_width = 14.3

# Colors
GREEN = (0, 255, 0)
RED = (0, 0, 255)
WHITE = (255, 255, 255)
BLACK = (0, 0, 0)

# defining the fonts
fonts = cv2.FONT_HERSHEY_COMPLEX

# face detector object
face_detector = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")

# focal length finder function
def Focal_Length_Finder(measured_distance, real_width, width_in_rf_image):
     # finding the focal length
     focal_length = (width_in_rf_image * measured_distance) / real_width
     return focal_length


# distance estimation function
def Distance_finder(Focal_Length, real_face_width, face_width_in_frame):
     distance = (real_face_width * Focal_Length) / face_width_in_frame

     # return the distance
     return distance

def microFacialExpressions(recognizer, width, height):
     font = cv2.FONT_HERSHEY_COMPLEX_SMALL
     detectorFace = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
     camera = cv2.VideoCapture(0)
     recognizer = cv2.face.EigenFaceRecognizer_create()
     recognizer.read("classifierEigen.yml")
     width, height = 220, 220
     while(True):
         connected, image = camera.read()
         # Grayscale conversion
         grayimage = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
         facesDetected = detectorFace.detectMultiScale(GrayImage,scaleFactor=1.5, minSize=(100, 100))
         for (x, y, l, a) in facesDetected:
             faceimage = cv2.resize(greyimage[y:y + a, x:x + l], (width, height))
             cv2.rectangle(image, (x, y), (x + l, y + a), (0,0,255), 2)
             global id, confidence = recognizer.predict(faceimage)
             #If ID is equal to 1, issue the message "Safe to exit" if not, issue the message "Hostile area"
             if id == 1:
                 warning="Safe to exit"
             else:
                 warning = "Hostile area"
         cv2.putText(image, warning, (x,y +(a+30)), font, 2, (0,0,255))

         return warning


def face_data(image):
     face_width = 0 # making face width to zero

     # converting color image to gray scale image
     gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

     # detecting face in the image
     faces = face_detector.detectMultiScale(gray_image, 1.3, 5)

     # looping through the faces detect in the image
     # getting coordinates x, y , width and height
     for (x, y, h, w) in faces:
         # draw the rectangle on the face
         cv2.rectangle(image, (x, y), (x + w, y + h), GREEN, 2)

         # getting face width in the pixels
         face_width = w

     # return the face width in pixel
     return face_width


# reading reference_image from directory
ref_image = cv2.imread("Ref_image.jpg")

# find the face width(pixels) in the reference_image
ref_image_face_width = face_data(ref_image)

# get the focal by calling "Focal_Length_Finder"
# face width in reference(pixels),
# Known_distance(centimeters),
# known_width(centimeters)
Focal_length_found = Focal_Length_Finder(
     Known_distance, Known_width, ref_image_face_width)

print(Focal_length_found)

# show the reference image
cv2.imshow("ref_image", ref_image)

# initialize the camera object so that we
# can get frame from it
cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)

# looping through frame, incoming from
# camera/video
while True:

     # reading the frame from camera
     _, frame = cap.read()

     # calling face_data function to find
     # the width of face(pixels) in the frame
     face_width_in_frame = face_data(frame)

     # check if the face is zero then not
     # find the distance
     if face_width_in_frame != 0:
         # finding the distance by calling function
         # Distance finder function need
         # these arguments the Focal_Length,
         # known_width(centimeters),
         # and Known_distance(centimeters)
         Distance = Distance_finder(
             Focal_length_found, Known_width, face_width_in_frame)

         if Distance <= 50 and id:
             print("Level S Alert!")

         # draw line as background of text
         cv2.line(frame, (30, 30), (230, 30), RED, 32)
         cv2.line(frame, (30, 30), (230, 30), BLACK, 28)

         # Drawing Text on the screen
         cv2.putText(
             frame, f"Distance: {round(Distance, 2)} CM", (30, 35),
             fonts, 0.6, GREEN, 2)

     # show the frame on the screen
     cv2.imshow("frame", frame)

     # quit the program if you press 'q' on keyboard
     if cv2.waitKey(1) == ord("q"):
         break

# closing the camera
cap.release()

# closing the windows that are opened
cv2.destroyAllWindows()