Simple Markov chain generator in Python

markov.py

import random

# Generates markov chains using sequences of characters
class MarkovChar:
	def __init__(self, seq_size):
		# key type: chracter sequence
		# value type: another map of character sequences to ints (occurance count)
		self.prob_map = {}
		self.seq_size = seq_size

	# takes a string and adds it to the probability tree
	def train(self, raw):
		idx = 0
		while True:
			seq = raw[idx : idx + self.seq_size]
			next_seq_end_idx = idx + self.seq_size * 2
			# are we at the end of the string?
			if next_seq_end_idx <= len(raw):
				next_seq = raw[idx + self.seq_size : next_seq_end_idx]
				if seq in self.prob_map:
					if next_seq in self.prob_map[seq]:
						self.prob_map[seq][next_seq] += 1
					else:
						self.prob_map[seq][next_seq] = 1
				else:
					self.prob_map[seq] = {next_seq : 1}
			else:
				if seq in self.prob_map:
					if None in self.prob_map[seq]:
						self.prob_map[seq][None] += 1
					else:
						self.prob_map[seq][None] = 1
				else:
					self.prob_map[seq] = {None : 1}
				break
			idx += self.seq_size

	# Uses the probability tree to generate a sentence.
	# max: the maximum number of characters generated before the function forcefully returns.
	# if max is zero or less, no character limit is present.
	def generate(self, char_limit = 0):
		# start with a random initial char.
		result = random.choice(list(self.prob_map.keys()))
		result_pos = 0
		while True:
			inner_dict = self.prob_map[result[result_pos:result_pos + self.seq_size]]
			inner_key_list = list(inner_dict.keys())
			MAX = 0
			for i in range(len(inner_key_list)):
				MAX += inner_dict[inner_key_list[i]]
			chosen_num = random.randint(0, MAX)
			# use the chosen num to choose a key to append to result
			def get_chosen_key():
				count = 0
				for i in range(len(inner_key_list)):
					count += inner_dict[inner_key_list[i]]
					if count >= chosen_num:
						return inner_key_list[i]
			chosen_key = get_chosen_key();
			if type(chosen_key) == type(None) or (len(result) > char_limit and char_limit > 0):
				return result
			else:
				result += chosen_key
			result_pos += self.seq_size


try:
	in_file = open('input.txt', 'r', encoding='utf8')
except FileNotFoundError:
	print('input.txt does not exist.')
	quit()

out_file = open('output.txt', 'w', encoding='utf8')

while True:
	try:
		seq_size = int(input('Enter a sequence size: '))
		if seq_size < 1:
			print("Sequence size must be greater than zero.")
			continue
		break;
	except ValueError:
		print("Invalid input.")
	

while True:
	try:
		char_limit = int(input('Character limit? (enter nothing to skip): '))
		if char_limit < 0:
			print('Character limit cannot be negative.')
			continue
		break
	except ValueError:
		char_limit = 0
		break;
	
mc = MarkovChar(seq_size)
for line in in_file:
	mc.train(line)
	
result = mc.generate(char_limit)
print()
print(result)
print()

out_file.write(result)
out_file.close()