Inspite of my model being properly trained and dumped in an pickle file and getting an unwanted error while testing the file using cv2.VideoCapture

Question

My model is already trained and is saved in model_trained.pkl file. And I'm trying to test the same using video captures. But getting error "FileNotFoundError: Unsuccessful TensorSliceReader constructor: Failed to find any matching files for ram://727e9043-6e37-4005-96d7-940496dd4979/variables/variables You may be trying to load on a different device from the computational device. Consider setting the experimental_io_device option in tf.saved_model.LoadOptions to the io_device such as '/job:localhost'. "

import numpy as np
import cv2
import pickle

#############################################

frameWidth = 640  # CAMERA RESOLUTION
frameHeight = 480
brightness = 180
threshold = 0.90  # PROBABLITY THRESHOLD
font = cv2.FONT_HERSHEY_SIMPLEX
##############################################

# SETUP THE VIDEO CAMERA
cap = cv2.VideoCapture(1)
cap.set(3, frameWidth)
cap.set(4, frameHeight)
cap.set(10, brightness)
# IMPORT THE TRANNIED MODEL
pickle_in = open("model_trained.pkl", "rb")  ## rb = READ BYTE
model = pickle.load(pickle_in)


def grayscale(img):
    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    return img

def equalize(img):
    img = cv2.equalizeHist(img)
    return img

def preprocessing(img):
    img = grayscale(img)
    img = equalize(img)
    img = img / 255
    return img

def getCalssName(classNo):
    if classNo == 0:
        return 'Speed Limit 20 km/h'
    elif classNo == 1:
        return 'Speed Limit 30 km/h'
    elif classNo == 2:
        return 'Speed Limit 50 km/h'
    elif classNo == 3:
        return 'Speed Limit 60 km/h'
    elif classNo == 4:
        return 'Speed Limit 70 km/h'
    elif classNo == 5:
        return 'Speed Limit 80 km/h'
    elif classNo == 6:
        return 'End of Speed Limit 80 km/h'
    elif classNo == 7:
        return 'Speed Limit 100 km/h'
    elif classNo == 8:
        return 'Speed Limit 120 km/h'
    elif classNo == 9:
        return 'No passing'
    elif classNo == 10:
        return 'No passing for vechiles over 3.5 metric tons'
    elif classNo == 11:
        return 'Right-of-way at the next intersection'
    elif classNo == 12:
        return 'Priority road'
    elif classNo == 13:
        return 'Yield'
    elif classNo == 14:
        return 'Stop'
    elif classNo == 15:
        return 'No vehicles'
    elif classNo == 16:
        return 'Vehicles over 3.5 metric tons prohibited'
    elif classNo == 17:
        return 'No entry'
    elif classNo == 18:
        return 'General caution'
    elif classNo == 19:
        return 'Dangerous curve to the left'
    elif classNo == 20:
        return 'Dangerous curve to the right'
    elif classNo == 21:
        return 'Double curve'
    elif classNo == 22:
        return 'Bumpy road'
    elif classNo == 23:
        return 'Slippery road'
    elif classNo == 24:
        return 'Road narrows on the right'
    elif classNo == 25:
        return 'Road work'
    elif classNo == 26:
        return 'Traffic signals'
    elif classNo == 27:
        return 'Pedestrians'
    elif classNo == 28:
        return 'Children crossing'
    elif classNo == 29:
        return 'Bicycles crossing'
    elif classNo == 30:
        return 'Beware of ice/snow'
    elif classNo == 31:
        return 'Wild animals crossing'
    elif classNo == 32:
        return 'End of all speed and passing limits'
    elif classNo == 33:
        return 'Turn right ahead'
    elif classNo == 34:
        return 'Turn left ahead'
    elif classNo == 35:
        return 'Ahead only'
    elif classNo == 36:
        return 'Go straight or right'
    elif classNo == 37:
        return 'Go straight or left'
    elif classNo == 38:
        return 'Keep right'
    elif classNo == 39:
        return 'Keep left'
    elif classNo == 40:
        return 'Roundabout mandatory'
    elif classNo == 41:
        return 'End of no passing'
    elif classNo == 42:
        return 'End of no passing by vechiles over 3.5 metric tons'


while True:
    # READ IMAGE
    success, imgOrignal = cap.read()

    # PROCESS IMAGE
    img = np.asarray(imgOrignal)
    img = cv2.resize(img, (320, 320))
    img = preprocessing(img)
    cv2.imshow("Processed Image", img)
    img = img.reshape(1, 32, 32, 1)
    cv2.putText(imgOrignal, "CLASS: ", (20, 35), font, 0.75, (0, 0, 255), 2, cv2.LINE_AA)
    cv2.putText(imgOrignal, "PROBABILITY: ", (20, 75), font, 0.75, (0, 0, 255), 2, cv2.LINE_AA)

    # PREDICT IMAGE
    predictions = model.predict(img)
    classIndex = model.predict_classes(img)
    probabilityValue = np.amax(predictions)
    if probabilityValue > threshold:

    # print(getCalssName(classIndex))
        cv2.putText(imgOrignal, str(classIndex) + " " + str(getCalssName(classIndex)), (120, 35), font, 0.75, (0, 0, 255), 2,
            cv2.LINE_AA)
        cv2.putText(imgOrignal, str(round(probabilityValue * 100, 2)) + "%", (180, 75), font, 0.75, (0, 0, 255), 2, cv2.LINE_AA)
    cv2.imshow("Result", imgOrignal)

    if cv2.waitKey(1) and 0xFF == ord('q'):
        break

My CNN model is trained. And I'm trying to test it using the pkl file where I've dumped my model. For testing I'm using cv2 videocaptures but the code is throwing me an unwanted error. What should I change in the code?