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