| import gradio as gr |
| import tensorflow as tf |
| from tensorflow.keras.preprocessing.sequence import pad_sequences |
| import numpy as np |
| import pickle |
|
|
| |
| with open('tokenizer.pickle', 'rb') as handle: |
| tokenizer = pickle.load(handle) |
|
|
| |
| model = tf.keras.models.load_model("text_generation_model") |
|
|
| |
| user_input = input("ํ
์คํธ ์์ฑ์ ์์ํ ๋จ์ด๋ฅผ ์
๋ ฅํ์ธ์: ") |
|
|
| |
| def generate_text(seed_text, next_words, max_sequence_len): |
| for _ in range(next_words): |
| token_list = tokenizer.texts_to_sequences([seed_text])[0] |
| token_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre') |
| predicted = model.predict_classes(token_list, verbose=0) |
| |
| output_word = "" |
| for word, index in tokenizer.word_index.items(): |
| if index == predicted: |
| output_word = word |
| break |
| seed_text += " " + output_word |
| return seed_text |
|
|
| |
| generated_text = generate_text(user_input, next_words=50, max_sequence_len=25) |
| print(generated_text) |
|
|