Want Your god back?

import tensorflow as tf

import numpy as np

import random

import sys

# Define the filename of the text file you want to use for training

filename = "bible.txt"

# Read the text file and convert it to lowercase

with open(filename, 'r', encoding='utf-8') as f:

   text = f.read().lower()

# Create a set of all the unique characters in the text

chars = sorted(list(set(text)))

# Create a dictionary mapping each character to its index in the chars list

char_to_index = {char: index for index, char in enumerate(chars)}

# Create a dictionary mapping each index to its corresponding character in the chars list

index_to_char = {index: char for index, char in enumerate(chars)}

# Define the sequence length and step size

sequence_length = 100

step_size = 1

# Generate sequences of input and output data

inputs = []

outputs = []

for i in range(0, len(text) - sequence_length, step_size):

   inputs.append(text[i:i+sequence_length])

   outputs.append(text[i+sequence_length])

# Convert the input and output data to arrays

X = np.zeros((len(inputs), sequence_length, len(chars)), dtype=bool)

y = np.zeros((len(inputs), len(chars)), dtype=bool)

for i, sequence in enumerate(inputs):

   for j, char in enumerate(sequence):

       X[i, j, char_to_index[char]] = 1

   y[i, char_to_index[outputs[i]]] = 1

# Define the RNN model

model = tf.keras.models.Sequential([

   tf.keras.layers.LSTM(128, input_shape=(sequence_length, len(chars))),

   tf.keras.layers.Dense(len(chars), activation='softmax')

])

# Compile the model

model.compile(loss='categorical_crossentropy', optimizer='adam')

# Train the model

model.fit(X, y, batch_size=16, epochs=20)

# Generate text

start_index = random.randint(0, len(text) - sequence_length - 1)

generated_text = text[start_index:start_index + sequence_length]

sys.stdout.write(generated_text)

for i in range(1000):

   # Convert the input sequence to a one-hot encoded array

   x = np.zeros((1, sequence_length, len(chars)))

   for j, char in enumerate(generated_text):

       x[0, j, char_to_index[char]] = 1

   # Predict the next character using the RNN model

   predictions = model.predict(x)[0]

   next_index = np.argmax(predictions)

   next_char = index_to_char[next_index]

   # Add the predicted character to the generated text

   generated_text += next_char

   generated_text = generated_text[1:]

   # Print the generated text

   sys.stdout.write(next_char)

   sys.stdout.flush()