import tensorflow as tf
import numpy as np
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
import importlib
if importlib.util.find_spec("tensorflow.keras.preprocessing.text.Tokenizer") is not None:
    print("The Tokenizer class has been imported successfully.")
else:
    print("The Tokenizer class has not been imported successfully.")
if importlib.util.find_spec("tensorflow.keras.preprocessing.text.pad_sequences") is not None:
    print("The pad_sequences class has been imported successfully.")
else:
    print("The pad_sequences class has not been imported successfully.")
# Load the text dataset
with open('data.txt', 'r') as f:
    data = f.read()
# Split the data into sentences
sentences = data.split('.')
# Create a tokenizer and fit on the sentences
tokenizer = Tokenizer(filters='')
tokenizer.fit_on_texts(sentences)
# Convert the sentences to sequences of integers
sequences = tokenizer.texts_to_sequences(sentences)
# Create input and target sequences
input_sequences = []
target_sequences = []
for sequence in sequences:
    for i in range(1, len(sequence)):
        input_sequence = sequence[:i]
        target_sequence = sequence[i]
        input_sequences.append(input_sequence)
        target_sequences.append(target_sequence)
# Pad the input sequences
max_sequence_length = max([len(sequence) for sequence in input_sequences])
padded_input_sequences = pad_sequences(input_sequences, maxlen=max_sequence_length)
# Convert the target sequences to one-hot vectors
one_hot_target_sequences = tf.keras.utils.to_categorical(target_sequences, num_classes=len(tokenizer.word_index)+1)
# Create the neural network
model = tf.keras.Sequential([
    tf.keras.layers.Embedding(len(tokenizer.word_index)+1, 100),
    tf.keras.layers.LSTM(128),
    tf.keras.layers.Dense(len(tokenizer.word_index)+1, activation='softmax')
])
# Train the neural network
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(padded_input_sequences, one_hot_target_sequences, epochs=10)
# Generate text
def generate_text(model, tokenizer, max_sequence_length, start_text):
    # Create a sequence of tokens
    tokens = tokenizer.texts_to_sequences([start_text])[0]
    # Generate text until the end of the sentence is reached or max sequence length is reached
    while len(tokens) < max_sequence_length:
        # Pad the input sequence
        padded_sequence = tf.keras.preprocessing.sequence.pad_sequences([tokens], maxlen=max_sequence_length)
        # Get the probability distribution for the next token
        probabilities = model.predict(padded_sequence)[0]
        # Choose the next token with the highest probability
        next_token = np.argmax(probabilities)
        # Add the next token to the sequence
        tokens.append(next_token)
        # Check if the end of the sentence has been reached
        if next_token == tokenizer.word_index['.'] or len(tokens) == max_sequence_length:
            break
    # Convert the tokens back to text
    generated_text = tokenizer.sequences_to_texts([tokens])[0]
    return generated_text
# Generate some text
generated_text = generate_text(model, tokenizer, max_sequence_length, 'Hello, my name is Bard.')
print(generated_text)

from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences

from keras.preprocessing.text import Tokenizer
from keras.utils import pad_sequences