main.py

import tensorflow as tf

from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional, Dropout
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from tensorflow import keras
import numpy as np
from keras.models import model_from_json
data = open('.\\Dataset1-Sithara.txt').read()
# open('enter location of dataset')
# print(data)

corpus = data.lower().split("\n")
corpus = list(set(corpus))
tokenizer = Tokenizer()
tokenizer.fit_on_texts(corpus)
total_words = len(tokenizer.word_index) + 1
# print(tokenizer.word_index)
input_sequences = []
for line in corpus:
    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)
    # print(n_gram_sequence)
max_sequence_len = max([len(x) for x in input_sequences])


json_file=open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model=model_from_json(loaded_model_json)
loaded_model.load_weights('model.h5')
model=loaded_model


seed_text=input()
next_words=10

for _ in range(next_words):
  token_list = tokenizer.texts_to_sequences([seed_text])[0]
  token_list = pad_sequences([token_list],maxlen=max_sequence_len-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)