Pretty straightforward. Took advantage of itertools.pairwise.

def predict(history: list[int]) -> int:
    sequences = [history]
    while len(set(sequences[-1])) > 1:
        sequences.append([b - a for a, b in itertools.pairwise(sequences[-1])])
    return sum(sequence[-1] for sequence in sequences)

def main(stream=sys.stdin) -> None:
    histories   = [list(map(int, line.split())) for line in stream]
    predictions = [predict(history) for history in histories]
    print(sum(predictions))

Only thing that changed from the first part was that I used functools.reduce to take the differences of the first elements of the generated sequences (rather than the sum of the last elements for Part 1).

def predict(history: list[int]) -> int:
    sequences = [history]
    while len(set(sequences[-1])) > 1:
        sequences.append([b - a for a, b in itertools.pairwise(sequences[-1])])
    return functools.reduce(
        lambda a, b: b - a, [sequence[0] for sequence in reversed(sequences)]
    )

def main(stream=sys.stdin) -> None:
    histories   = [list(map(int, line.split())) for line in stream]
    predictions = [predict(history) for history in histories]
    print(sum(predictions))