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import gradio as gr |
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import json |
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import numpy as np |
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import tensorflow as tf |
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from tensorflow.keras.preprocessing.text import Tokenizer |
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from tensorflow.keras.preprocessing.sequence import pad_sequences |
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vocab_size = 103 |
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max_seq_len = 18 |
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model = tf.keras.Sequential([ |
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tf.keras.layers.Embedding(vocab_size, 120, input_length=max_seq_len-1), |
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tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(120)), |
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tf.keras.layers.Dense(vocab_size, activation='softmax') |
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]) |
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model.load_weights("model_weight/") |
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word_token = json.load("word_token.json") |
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def text_generator(seed_text,n): |
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for _ in range(n): |
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token_list = tokenizer.texts_to_sequences([seed_text])[0] |
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token_list = pad_sequences([token_list], maxlen=max_seq_len-1, padding='pre') |
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predicted = np.argmax(model.predict(token_list), axis=-1) |
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output_word = "" |
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output_word = word_token[predicted[0]] |
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if "0" in output_word[-1]: |
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output_word = output_word[:-1] |
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seed_text += " " + output_word +"." |
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break |
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seed_text += " " + output_word |
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return seed_text |
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iface = gr.Interface( |
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fn=text_generator, |
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inputs=["text","slider"], |
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outputs="text", |
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title="Poem Bot", |
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live=False |
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) |
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iface.launch() |
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