def generate_command(self):
generated_trump = Generator.generate_seq(
self.model_trump, self.tokenizer_trump, self.seq_length_trump,
self.trump_seed.get('1.0', 'end-1c'), 10, 30)
generated_clinton = Generator.generate_seq(
self.model_clinton,
self.tokenizer_clinton, self.seq_length_clinton,
self.clinton_seed.get('1.0', 'end-1c'), 10, 30)
self.trump_text.configure(state='normal')
self.trump_text.delete('1.0', 'end')
self.trump_text.insert('end', Generator.postprocess(generated_trump))
self.trump_text.configure(state='disabled')
self.clinton_text.configure(state='normal')
self.clinton_text.delete('1.0', 'end')
self.clinton_text.insert('end',
Generator.postprocess(generated_clinton))
self.clinton_text.configure(state='disabled')
# Save trained model and tokenizer for later usage
Trainer.save_model(model, MODEL_FILENAME)
Trainer.save_tokenizer(tokenizer, TOKENIZER_FILENAME)
# Second step: Generate Tweets with the trained model
# # Open the sequenced data (if not open already)
# doc = Trainer.load_file(SEQUENCE_FILENAME)
# lines = doc.split('\n')
# # Make all letters lower case
# lines = [word.lower() for word in lines]
# Get sequence length (50 in this case)
seq_length = len(lines[0].split()) - 1
# Load model and tokenizer
model = Generator.load_trained_model(MODEL_FILENAME)
tokenizer = Generator.load_tokenizer(TOKENIZER_FILENAME)
# Select and print a random seed text (we can make up our own seed text as well,
# better results are expected with this approach tho. The tokenizer ignores word that are not known to him)
seed_text = lines[randint(0, len(lines))]
print(seed_text + '\n')
# Generate and print a sequence with a word length of minimum 10 and maximum 30
generated = Generator.generate_seq(model, tokenizer, seq_length, seed_text,
MIN_OUTPUT_WORD_COUNT,
MAX_OUTPUT_WORD_COUNT)
# Post-process and print generated text
print(Generator.postprocess(generated))
