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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() |