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| import tensorflow as tf | |
| from tensorflow.keras.preprocessing.text import Tokenizer | |
| from tensorflow.keras.preprocessing.sequence import pad_sequences | |
| import numpy as np | |
| # Default files | |
| path_to_file = 'amy-winehouse.txt' | |
| path_to_model = 'amy_winehouse.h5' | |
| # Open model and dataset | |
| model = tf.keras.models.load_model(path_to_model) | |
| #Get name of file | |
| name = path_to_file.split('.')[0] | |
| #print(name) | |
| data= open(path_to_file).read() | |
| corpus = data.lower().split('\n') | |
| #Tokenize data | |
| tokenizer = Tokenizer() | |
| tokenizer.fit_on_texts(corpus) | |
| total_words = len(tokenizer.word_index) + 1 | |
| word_index = tokenizer.word_index | |
| index_word = {index:word for word, index in tokenizer.word_index.items()} | |
| n_gram_sequences = [] | |
| for line in corpus: | |
| token_list = tokenizer.texts_to_sequences([line])[0] | |
| for i in range(1, len(token_list)): | |
| n_gram_sequences.append(token_list[:i+1]) | |
| #print(np.shape(n_gram_sequences)) | |
| #for seq in n_gram_sequences: | |
| # print ([index_word[w] for w in seq]) | |
| # pad sequences | |
| max_len = max([len(seq) for seq in n_gram_sequences]) | |
| #print(max_sequence_len, total_words) | |
| n_gram_sequences = np.array(pad_sequences(n_gram_sequences, padding='pre', maxlen=max_len)) | |
| # Generate next words with an initial prompt | |
| def predict_n_words(prompt, n_words): | |
| for _ in range(n_words): | |
| token_list = tokenizer.texts_to_sequences([prompt])[0] | |
| token_list = pad_sequences([token_list], padding='pre', maxlen=max_len-1,) | |
| predicted = np.argmax( model.predict(token_list), axis = 1) | |
| prompt += " " + index_word[predicted[0]] | |
| return prompt | |
| import gradio as gr | |
| demo = gr.Interface( | |
| fn=predict_n_words, | |
| inputs=[gr.Textbox(lines=2, placeholder="Prompt text here..."), gr.Slider(0, 500)], | |
| outputs="text", | |
| examples=[ | |
| ["I'll go back to black", 200], | |
| ["To see your eyes,", 200], | |
| ], | |
| title="Amy Winehouse RNN", | |
| description="Simple word-based RNN text generator trained on Amy Winehouse's songs", | |
| ) | |
| demo.launch() |