import numpy as np
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.sequence import pad_sequences
import pickle
    
# Load the tokenizer and model
with open('tokenizer.pkl', 'rb') as handle:
    tokenizer = pickle.load(handle)
model = load_model('word_classifier_model.keras')
    
def classify_word(word):
    # Tokenize and pad the input word
    sequence = tokenizer.texts_to_sequences([word])
    padded_sequence = pad_sequences(sequence, maxlen=1)
        
    # Predict using the model
    prediction = model.predict(padded_sequence)
    #return 1 if prediction >= 0.5 else 0
    #return 1 if prediction >= 0.4 else 0
    return f'{round(prediction[0][0]*100,3)}%'
    
while True:
    word = input('>> ')
    if word == 'exit':
        break
    result = classify_word(word)
    print(f"The word '{word}' is a: {result}")