import numpy as np
import json
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Embedding, Flatten, Dropout, LSTM
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.regularizers import l2
import pickle    

from db import *

db = read()
words = db['0'] + db['1']
labels = [0]*len(db['0']) + [1]*len(db['1'])

# Tokenize the words
tokenizer = Tokenizer(num_words=1000, lower=True)
tokenizer.fit_on_texts(words)
sequences = tokenizer.texts_to_sequences(words)
    
# Padding sequences to ensure uniform input size
word_sequences = pad_sequences(sequences, maxlen=1)
    
# Define the model
model = Sequential([
    Embedding(input_dim=1000, output_dim=8, input_length=1),
    Flatten(),
    Dense(1, activation='sigmoid')
])
    
# Compile the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    
# Train the model
model.fit(word_sequences, np.array(labels), epochs=30, verbose=2)
    
# Save the tokenizer and model
import pickle
with open('tokenizer.pkl', 'wb') as handle:
    pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL)
model.save('word_classifier_model.keras')