реади2

antonov_dmitry_lab_7/for_test.py

@@ -1,34 +0,0 @@
-import numpy as np
-from keras.models import load_model
-from keras_preprocessing.sequence import pad_sequences
-from keras_preprocessing.text import Tokenizer
-
-from antonov_dmitry_lab_7.lab7 import tokenizer, max_sequence_length
-
-# Step 3: Load the pre-trained model
-model = load_model('my_model.h5')  # Replace with the actual path to your model file
-
-# Recreate the Tokenizer and compile the model (in case the model was not compiled before saving)
-with open('small.txt', 'r') as file:
-    text = file.read()
-
-tokenizer = Tokenizer()
-tokenizer.fit_on_texts([text])
-total_words = len(tokenizer.word_index) + 1
-def generate_text(seed_text, next_words, model, max_sequence_length):
-    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 = np.argmax(model.predict(token_list), axis=-1)
-        output_word = ""
-        for word, index in tokenizer.word_index.items():
-            if index == predicted:
-                output_word = word
-                break
-        seed_text += " " + output_word
-    return seed_text
-
-
-# Generate text using the loaded model (same as before)
-generated_text = generate_text("Once upon a", 50, model, max_sequence_length)
-print(generated_text)