def load_model(path_to_saved_model: str) -> NGramTextGenerator:
if not isinstance(path_to_saved_model, str):
raise ValueError
try:
with open(path_to_saved_model, 'r') as file:
model = json.load(file)
word_storage = WordStorage()
word_storage.storage = model['word_storage']
trie = NGramTrie(n_gram_size=int(model['n_gram_trie_size']),
encoded_text=('he', ) *
int(model['n_gram_trie_size']))
trie.n_grams = tuple([tuple(n_gram) for n_gram in model['n_grams']])
trie.n_gram_frequencies = {
tuple(map(int, key.split(', '))): value
for key, value in model['n_gram_trie_frequencies'].items()
}
trie.uni_grams = {(int(key), ): value
for key, value in model['uni_grams'].items()}
model_generator = NGramTextGenerator(word_storage, trie)
return model_generator
except FileNotFoundError as error:
raise FileNotFoundError from error
def load_model(path_to_saved_model: str) -> NGramTextGenerator:
if not isinstance(path_to_saved_model, str):
raise ValueError
with open(path_to_saved_model + '.json', 'r') as json_file:
generator_json = json.load(json_file)
words = WordStorage()
words.storage = generator_json['_word_storage']['storage']
trie = NGramTrie(generator_json['_n_gram_trie']['size'], (0, 1))
trie.encoded_text = generator_json['_n_gram_trie']['encoded_text']
trie.n_grams = tuple(
tuple(gram) for gram in generator_json['_n_gram_trie']['n_grams'])
trie.n_gram_frequencies = {
eval(key): value
for key, value in generator_json['_n_gram_trie']
['n_gram_frequencies'].items()
}
trie.uni_grams = {
eval(key): value
for key, value in generator_json['_n_gram_trie']
['uni_grams'].items()
}
return NGramTextGenerator(words, trie)