AIpoet.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Sep  5 11:42:36 2024

@author: jeffmarstell
"""

import random
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import sequential
from tensorflow.keras.layers import LSTM, Dense, Activation
from tensorflow.keras.optimizers import RMSprop

filepath = tf.keras.utils.get_file('shakespeare.txt', 'https://storage.googleapis.com/download.tensorflow.org/data/shakespeare.txt')

text = open(filepath, 'rb').read().decode(encoding='utf-8').lower()

text = text[300000:800000]

characters = sorted(set(text))

char_to_index = dict((c, i) for i, c in enumerate(characters))
index_to_char = dict((i, c) for i, c in enumerate(characters))

SEQ_LENGTH = 40
STEP_SIZE = 3

"""
sentences = []
next_characters = []

for i in range(0, len(text) - SEQ_LENGTH, STEP_SIZE):
    sentences.append(text[i: i+SEQ_LENGTH])
    next_characters.append(text[i+SEQ_LENGTH])
    
x = np.zeros((len(sentences), SEQ_LENGTH, len(characters)), dtype=np.bool)   
y = np.zeros((len(sentences), len(characters)), dtype=np.bool)

for i, sentence in enumerate(sentences):
    for t, character in enumerate(sentences):
        x[i, t, char_to_index[character]] = 1
    y[i, char_to_index[next_characters[i]]] = 1
    
model = sequential()
model.add(LSTM(128, input_shape=(SEQ_LENGTH, len(characters))))
model.add(Dense(len(characters)))
model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy', optimizer=RMSprop(lr=0.01))

model.fit(x, y, batch_size=256, epochs=4)

model.save('textgenerator.model')"""

model = tf.keras.models.load_model('textgenerator.model')
        
        
def sample(preds, temperature=1.0):
    preds = asarray(preds).astype('float64')
    preds = np.log(preds) / temperature
    exp_preds = np.exp(preds)
    preds = exp_preds / np.sum(exp_preds)
    probas = np.random.multinominal(1, preds, 1)
    return np.argmax(probas)

def generate_text(length, temperature):
    start_index = random.randit(0, len(text) -SEQ_LENGTH - 1)
    generated = ' '
    sentence = text[start_index + SEQ_LENGTH]
    generated += sentence
    for i in range(length):
        x = np.zeros((1, SEQ_LENGTH, len(characters)))
        for t, character in enumerate(sentence):
            x[0, t, char_to_index[character]] = 1
            
        predictions = model.predict(x, verbose=0)[0]
        next_index = sample(predictions, temperature)
        next_character = index_to_char[next_index]
        
        generated += next_character
        sentence = sentence[1:] + next_character
    return generated

print('......0.2.....')
print(generate_text(300, 0.2))   

print('......0.4.....')
print(generate_text(300, 0.4))   

print('......0.6.....')
print(generate_text(300, 0.6)) 

print('......0.8.....')
print(generate_text(300, 0.8))