Question about DataCamp's hypothesis testing problem concerning p-values

I am taking a course in on online data science teaching website called DataCamp, and I had a question about one of their problems (which has to do with hypothesis testing). I will state the problem, their solution, and then my question about the solution. The statement of the problem is here:

The Civil Rights Act of 1964 was one of the most important pieces of legislation ever passed in the USA. Excluding "present" and "abstain" votes, 153 House Democrats and 136 Republicans voted yea. However, 91 Democrats and 35 Republicans voted nay. Did party affiliation make a difference in the vote?
To answer this question, you will evaluate the hypothesis that the party of a House member has no bearing on his or her vote. You will use the fraction of Democrats voting in favor as your test statistic and evaluate the probability of observing a fraction of Democrats voting in favor at least as small as the observed fraction of 153/244. (That’s right, at least as small as. In 1964, it was the Democrats who were less progressive on civil rights issues.) To do this, permute the party labels of the House voters and then arbitrarily divide them into "Democrats" and "Republicans" and compute the fraction of Democrats voting yea.

The correct python answer is here

# Construct arrays of data: dems, reps
dems = np.array([True] * 153 + [False] * 91)
reps = np.array([True] * 136 + [False] * 35)

def frac_yea_dems(dems, reps):
    """Compute fraction of Democrat yea votes."""
    frac = np.sum(dems) / len(dems)
    return frac

# Acquire permutation samples: perm_replicates
perm_replicates = draw_perm_reps(dems, reps, frac_yea_dems, 10000)

# Compute and print p-value: p
p = np.sum(perm_replicates <= 153/244) / len(perm_replicates)
print('p-value =', p)
plt.hist(perm_replicates,bins=50)
plt.show()

where

def draw_perm_reps(data_1, data_2, func, size=1):
    """Generate multiple permutation replicates."""

    # Initialize array of replicates: perm_replicates
    perm_replicates = np.empty(size)

    for i in range(size):
        # Generate permutation sample
        perm_sample_1, perm_sample_2 = permutation_sample(data_1,data_2)

        # Compute the test statistic
        perm_replicates[i] = func(perm_sample_1,perm_sample_2)

    return perm_replicates

def permutation_sample(data1, data2):
    """Generate a permutation sample from two data sets."""

    # Concatenate the data sets: data
    data = np.concatenate((data1,data2))

    # Permute the concatenated array: permuted_data
    permuted_data = np.random.permutation(data)

    # Split the permuted array into two: perm_sample_1, perm_sample_2
    perm_sample_1 = permuted_data[:len(data1)]
    perm_sample_2 = permuted_data[len(data1):]

    return perm_sample_1, perm_sample_2

When you run all of the above code, you get that p is really small, implying that the null hypothesis should be rejected.

What I do not understand is why they are summing perm_replicates less than or equal to 153/244. If the hypothesis they are testing is that the party of a house member has no bearing on his or her vote, shouldn’t they be doing like p = np.sum(153/244-delta <= perm_replicates <= 153/244+delta) / len(perm_replicates), where delta is some parameter determining the neighborhood around the empirical mean which determines our threshold for similarity between the means of the permutated data set means and the empirical mean?

Another way of saying it: if we replace reps with dems such that instead of perm_replicates = draw_perm_reps(dems, reps, frac_yea_dems, 10000), it’s perm_replicates = draw_perm_reps(dems, dems, frac_yea_dems, 10000), then the hypothesis will surely show that party affiliation does not make a difference in the vote (which is the null hypothesis). If you run this code, you will get a p value of 50% (which makes sense, the permutations all will give similar answers), but as the reps data set deviates from dems, you will either start increasing away from a 50% p value or decreasing away from a 50% p value, so p = np.sum(perm_replicates <= 153/244) / len(perm_replicates) being small (<<1) OR large (near 1) should cause you to reject the null hypothesis, and a p value near 1/2 should cause you to accept the null hypothesis, right???

There must be something very basic I’m missing here, maybe someone could try to answer my questions and qualms directly or you could just explain the answer independently and maybe I’ll understand your explanation to a point where my qualms become absurd to me. I appreciate any help though =D