cv2.face.LBPHFaceRecognizer_create().predict() is showing all users with my name!

I was making a Face Recognition System; I almost succeed but I noticed, my name is showing for Elon Musk! Where it should be shown as Unknown!

Face Recognizer.py:

import json
import cv2
import numpy as np

print("Please press ESC to close!")
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('Trainer/trainer.yml')
cascadePath = "haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath)

font = cv2.FONT_HERSHEY_SIMPLEX

id = 2

with open('index.json', 'r') as f:
    db = json.load(f)
names = {}
for i in db:
    for face in db['faces']:
        names[face] = db['faces'][face]

cam = cv2.VideoCapture(0, cv2.CAP_DSHOW)
cam.set(3, 640)
cam.set(4, 480)

minW = 0.1 * cam.get(3)
minH = 0.1 * cam.get(4)

while True:
    ret, img = cam.read()

    converted_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    faces = faceCascade.detectMultiScale(
        converted_image,
        scaleFactor=1.2,
        minNeighbors=5,
        minSize=(int(minW), int(minH)),
    )
    for (x, y, w, h) in faces:
        id, accuracy = recognizer.predict(converted_image[y:y + h, x:x + w])
        if (accuracy < 100):
            id = names[str(id)]
            cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
        else:
            id = "Unknown"
            cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
            if cv2.imwrite(f"Unknowns/{str(datetime.datetime.now().strftime('%Y-%m-%d-%X')).replace(':', '_')}.jpg", img):
                print("Image Saved!")
            winsound.Beep(2000, 500)
        cv2.putText(img, str(id), (x + 5, y - 5), font, 1, (255, 255, 255), 2)

    cv2.imshow('Face Detection', img)

        k = cv2.waitKey(10) & 0xff
        if k == 27:
            break
    cam.release()
    cv2.destroyAllWindows()

index.json:

{
    "faces": {
        "1": "Tahsin"
    }
}

This is how I'm taking the Samples!

import json
import cv2
import numpy as np
from PIL import Image
import os

cam = cv2.VideoCapture(0,
                       cv2.CAP_DSHOW)
cam.set(3, 640)
cam.set(4, 480)

detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

face_id = input("Enter a Numeric user ID:  ")
face_name = input("Enter a Name:  ")

print("Taking samples, look at camera... ")
count = 0
while True:
    ret, img = cam.read()
    converted_image = cv2.cvtColor(img,
                                   cv2.COLOR_BGR2GRAY)
    faces = detector.detectMultiScale(converted_image, 1.3, 5)

    for (x, y, w, h) in faces:
        cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
        count += 1

        cv2.imwrite("samples/face." + str(face_id) + '.' + str(count) + ".jpg", converted_image[y:y + h, x:x + w])

    k = cv2.waitKey(100) & 0xff
    if k == 27:
        break
    elif count >= 50:
        break
    cv2.imshow('image', img)

with open('index.json', 'r') as f:
    db = json.load(f)
if 'faces' in db:
    db['faces'][face_id] = face_name
else:
    db['faces'] = {}
    db['faces'][face_id] = face_name
with open('index.json', 'w') as f:
    json.dump(db, f, indent=4)
print("Samples taken!")
cam.release()
cv2.destroyAllWindows()

path = 'samples'

recognizer = cv2.face.LBPHFaceRecognizer_create()
detector = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")


def Images_And_Labels(path):

    imagePaths = [os.path.join(path, f) if f.split('.')[1] == face_id else None for f in os.listdir(path)]
    faceSamples = []
    ids = []

    for imagePath in imagePaths:
        if imagePath != None:
            gray_img = Image.open(imagePath).convert('L')
            img_arr = np.array(gray_img, 'uint8')

            id = int(os.path.split(imagePath)[-1].split(".")[1])
            faces = detector.detectMultiScale(img_arr)

            for (x, y, w, h) in faces:
                faceSamples.append(img_arr[y:y + h, x:x + w])
                ids.append(id)

    return faceSamples, ids


print("Training faces. It will take a few seconds. Wait ...")

faces, ids = Images_And_Labels(path)
recognizer.train(faces, np.array(ids))

recognizer.write('trainer/trainer.yml')

print("Model trained, Now we can recognize your face.")

Sample: Elon Musk is showing as Tahsin