You can use numba to JIT your current functions, which results in significant speed improvements (I've changed it to use numpy for ceil and sqrt as it is slightly faster than using math):

import numpy as np
import numba as nb

@nb.njit
def is_prime_number(number):
    if number % 2 == 0:
        return False
    root_of_number = np.ceil(np.sqrt(number))
    for i in range(3, root_of_number + 1, 2):
        if number % i == 0:
            return False
    return True

@nb.njit
def main(number):
    largest_prime_factor = 0

    for i in range(1, number + 1):
        if is_prime_number(i):
            if number % i == 0:
                if i > largest_prime_factor:
                    largest_prime_factor = i
        if i % 1000000 == 0:
            print(i)
    print(f"Largest prime factor of {number}: {largest_prime_factor}")

Timings:

%timeit main_op(7919)
%timeit main(7919)

Output:

3.83 ms ± 36.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
74.3 µs ± 245 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)