import numpy as np
from keras.models import Sequential
from keras.layers import Embedding, LSTM, Dense
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences

# Load the text
with open('text.txt', 'r', encoding='utf-8') as file:
    text = file.read()

tokenizer = Tokenizer()
tokenizer.fit_on_texts([text])
total_words = len(tokenizer.word_index) + 1

# Create the sequence of training data
input_sequences = []
for line in text.split('\n'):
    token_list = tokenizer.texts_to_sequences([line])[0]
    for i in range(1, len(token_list)):
        n_gram_sequence = token_list[:i+1]
        input_sequences.append(n_gram_sequence)

# Padding sequences
max_sequence_length = max([len(seq) for seq in input_sequences])
input_sequences = pad_sequences(input_sequences, maxlen=max_sequence_length, padding='pre')

# Create input and output data
X, y = input_sequences[:, :-1], input_sequences[:, -1]
y = np.eye(total_words)[y]

# Create the model
model = Sequential()
model.add(Embedding(total_words, 50, input_length=max_sequence_length-1))
model.add(LSTM(100))
model.add(Dense(total_words, activation='softmax'))

# Compile the model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# Train the model
history = model.fit(X, y, epochs=100, verbose=2)

print(f"Final Loss on Training Data: {history.history['loss'][-1]}")

# Generate text
seed_text = "Amidst the golden hues of autumn leaves"
next_words = 100

for _ in range(next_words):
    token_list = tokenizer.texts_to_sequences([seed_text])[0]
    token_list = pad_sequences([token_list], maxlen=max_sequence_length-1, padding='pre')
    predicted = model.predict_classes(token_list, verbose=0)
    output_word = ""
    for word, index in tokenizer.word_index.items():
        if index == predicted:
            output_word = word
            break
    seed_text += " " + output_word

print(seed_text)