I’m trying to use faceNet keras and tensorflow to do facial recognition with mtcnn, but it’s giving me the error EOFError: EOF read where object expected and I wanted to know if this model can still be used.

I have already tried using other codes that do not need the Keras model and they worked with the indicated versions of Tensorflow and Keras

The versions I should use are:
TensorFlow Version: 2.16.1
Keras version: 3.3.3
Pyyhon Version 3.11.9

import os
from os import listdir
from PIL import Image as Img
from numpy import asarray
from numpy import expand_dims
from matplotlib import pyplot
from keras.models import load_model  #type: ignore
import numpy as np
import tensorflow as tf
from keras_facenet import FaceNet # type: ignore
import pickle
import cv2

HaarCascade = cv2.CascadeClassifier(cv2.samples.findFile(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'))

MyFaceNet = load_model("F:AndreFaceRecognitiontest/facenet_keras.h5")

folder='faces/'
database = {}

for filename in listdir(folder):

    path = folder + filename
    gbr1 = cv2.imread(folder + filename)
    
    wajah = HaarCascade.detectMultiScale(gbr1,1.1,4)
    
    if len(wajah)>0:
        x1, y1, width, height = wajah[0]         
    else:
        x1, y1, width, height = 1, 1, 10, 10
        
    x1, y1 = abs(x1), abs(y1)
    x2, y2 = x1 + width, y1 + height
    
    gbr = cv2.cvtColor(gbr1, cv2.COLOR_BGR2RGB)
    gbr = Img.fromarray(gbr)                  # konversi dari OpenCV ke PIL
    gbr_array = asarray(gbr)
    
    face = gbr_array[y1:y2, x1:x2]                        
    
    face = Img.fromarray(face)                       
    face = face.resize((160,160))
    face = asarray(face)
    
    face = face.astype('float32')
    mean, std = face.mean(), face.std()
    face = (face - mean) / std
    
    face = expand_dims(face, axis=0)
    signature = MyFaceNet.predict(face)
    
    database[os.path.splitext(filename)[0]]=signature

myfile = open("data.pkl", "wb")
pickle.dump(database, myfile)
myfile.close()

myfile = open("data.pkl", "rb")
database = pickle.load(myfile)
myfile.close()

def findFaces(frame):
    gbr1 = frame
    gbr = cv2.cvtColor(gbr1, cv2.COLOR_BGR2RGB)
    gbr = Img.fromarray(gbr)                  # konversi dari OpenCV ke PIL
    gbr_array = asarray(gbr)

    wajah = HaarCascade.detectMultiScale(gbr1,1.1,4)

    for (x1,y1,w,h) in wajah:
        x1, y1 = abs(x1), abs(y1)
        x2, y2 = x1 + w, y1 + h

        face = gbr_array[y1:y2, x1:x2]                        

        face = Img.fromarray(face)                       
        face = face.resize((160,160))
        face = asarray(face)

        face = face.astype('float32')
        mean, std = face.mean(), face.std()
        face = (face - mean) / std

        face = expand_dims(face, axis=0)
        signature = MyFaceNet.predict(face)

        min_dist=100
        identity=' '
        for key, value in database.items() :
            dist = np.linalg.norm(value-signature)
            if dist < min_dist:
                min_dist = dist
                identity = key

        cv2.putText(gbr1,identity, (x1,y1),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 0), 1, cv2.LINE_AA)
        cv2.rectangle(gbr1,(x1,y1),(x2,y2), (0,255,0), 2)

    return gbr1

cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    frame = findFaces(frame)
    cv2.imshow('Live Stream', frame)

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

cap.release()
cv2.destroyAllWindows()