Sosees04ka/PIbd-32_Kashin_M.I_API_CourseWork
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Я дурак, в итоге пофиксил позитивные предсказания. Все робит ура ура ура

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maksim 2024-06-02 16:58:03 +04:00
parent 2787cf59ae
commit 10ee04f93f
1 changed files with 24 additions and 4 deletions
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28
model.py
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@ -18,13 +18,27 @@ model_cnn_positive= tf.keras.models.load_model('.//neural_network/models/model/b
# Загрузка токенизатора # Загрузка токенизатора
with open('.//neural_network/tokenization/tokenizer_negative.pickle', 'rb') as handle: with open('.//neural_network/tokenization/tokenizer_negative.pickle', 'rb') as handle:
tokenizer = pickle.load(handle) tokenizer_negative = pickle.load(handle)
# Загрузка названий классов # Загрузка названий классов
with open('.//neural_network/classification/class_names_negative.txt', 'r', encoding='utf-8') as file: with open('.//neural_network/classification/class_names_negative.txt', 'r', encoding='utf-8') as file:
class_names = [line.strip() for line in file.readlines()] class_names_negative = [line.strip() for line in file.readlines()]
def preprocess_text(text: str): # Загрузка токенизатора
with open('.//neural_network/tokenization/tokenizer_positive.pickle', 'rb') as handle:
tokenizer_positive = pickle.load(handle)
# Загрузка названий классов
with open('.//neural_network/classification/class_names_positive.txt', 'r', encoding='utf-8') as file:
class_names_positive = [line.strip() for line in file.readlines()]
def preprocess_text(text: str, type_mood: TypeMood):
if type_mood == TypeMood.NEGATIVE:
tokenizer = tokenizer_negative
elif type_mood == TypeMood.POSITIVE:
tokenizer = tokenizer_positive
else:
raise ValueError("Unsupported model type")
# Токенизация текста # Токенизация текста
sequences = tokenizer.texts_to_sequences([text]) sequences = tokenizer.texts_to_sequences([text])
# Преобразование последовательностей в фиксированной длине # Преобразование последовательностей в фиксированной длине
@ -34,21 +48,27 @@ def preprocess_text(text: str):
def predict_answer(question: str, type_mood: TypeMood, type_model: TypeModel) -> str: def predict_answer(question: str, type_mood: TypeMood, type_model: TypeModel) -> str:
if type_model == TypeModel.LSTM and type_mood == TypeMood.NEGATIVE: if type_model == TypeModel.LSTM and type_mood == TypeMood.NEGATIVE:
model = model_lstm_negative model = model_lstm_negative
class_names = class_names_negative
elif type_model == TypeModel.LSTM and type_mood == TypeMood.POSITIVE: elif type_model == TypeModel.LSTM and type_mood == TypeMood.POSITIVE:
model = model_lstm_positive model = model_lstm_positive
class_names = class_names_positive
elif type_model == TypeModel.GRU and type_mood == TypeMood.NEGATIVE: elif type_model == TypeModel.GRU and type_mood == TypeMood.NEGATIVE:
model = model_gru_negative model = model_gru_negative
class_names = class_names_negative
elif type_model == TypeModel.GRU and type_mood == TypeMood.POSITIVE: elif type_model == TypeModel.GRU and type_mood == TypeMood.POSITIVE:
model = model_gru_positive model = model_gru_positive
class_names = class_names_positive
elif type_model == TypeModel.CNN and type_mood == TypeMood.NEGATIVE: elif type_model == TypeModel.CNN and type_mood == TypeMood.NEGATIVE:
model = model_cnn_negative model = model_cnn_negative
class_names = class_names_negative
elif type_model == TypeModel.CNN and type_mood == TypeMood.POSITIVE: elif type_model == TypeModel.CNN and type_mood == TypeMood.POSITIVE:
model = model_cnn_positive model = model_cnn_positive
class_names = class_names_positive
else: else:
raise ValueError("Unsupported model type") raise ValueError("Unsupported model type")
# Предобработка вопроса # Предобработка вопроса
input_data = preprocess_text(question) input_data = preprocess_text(question, type_mood)
# Предсказание # Предсказание
prediction = model.predict(input_data)[0] prediction = model.predict(input_data)[0]
# Получение имени класса # Получение имени класса