From 7c3020625c27ccb16144a185835fddea10284230 Mon Sep 17 00:00:00 2001
From: maksim <kashin20031984@mail.ru>
Date: Sat, 25 May 2024 17:38:24 +0400
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---
 model.py         |  33 +++++++++++++++++++++++++++++++++
 repository.py    |   7 +++++++
 requirements.txt | Bin 572 -> 610 bytes
 3 files changed, 40 insertions(+)
 create mode 100644 model.py

diff --git a/model.py b/model.py
new file mode 100644
index 0000000..42de820
--- /dev/null
+++ b/model.py
@@ -0,0 +1,33 @@
+import pickle
+import numpy as np
+import tensorflow as tf
+from keras.src.legacy.preprocessing.text import Tokenizer
+from keras.src.utils import pad_sequences
+
+# Загрузка модели
+model = tf.keras.models.load_model('best_model_lstm_negative.keras')
+
+# Загрузка токенизатора
+with open('tokenizer_lstm_lstm_negative.pickle', 'rb') as handle:
+    tokenizer = pickle.load(handle)
+
+def preprocess_text(text: str):
+    # Токенизация текста
+    sequences = tokenizer.texts_to_sequences([text])
+    # Преобразование последовательностей в фиксированной длины
+    padded_sequences = pad_sequences(sequences, maxlen=90)  # 90 - длина последовательности, используемая при обучении
+    return padded_sequences
+
+def predict_answer(question: str) -> str:
+    # Предобработка вопроса
+    print("Вопрос:", question)
+    input_data = preprocess_text(question)
+    print("Предобработанные данные:", input_data)
+    # Предсказание
+    prediction = model.predict(input_data)
+    print("Предсказание:", prediction)
+    # Преобразование предсказания в строку для сохранения
+    prediction_str = np.array2string(prediction[0], separator=',')
+    print("Строковое представление предсказания:", prediction_str)
+    return prediction_str  # Возвращаем строковое представление предсказания
+
diff --git a/repository.py b/repository.py
index 447e4b2..dd8281b 100644
--- a/repository.py
+++ b/repository.py
@@ -2,12 +2,19 @@ from sqlalchemy import select
 
 from database import new_session, QuestionOrm
 from schemas import SQuestionAdd, SQuestion
+from model import predict_answer
+
 
 class QuestionRepository:
     @classmethod
     async def add_one(cls, data: SQuestionAdd) -> int:
         async with new_session() as session:
             question_dict = data.model_dump()
+
+            # Предсказание ответа с помощью модели
+            answer = predict_answer(question_dict["question"])
+
+            question_dict["answer"] = answer
             question = QuestionOrm(**question_dict)
             session.add(question)
             await session.flush()
diff --git a/requirements.txt b/requirements.txt
index 712cf652d3530a59c89d60688e1f3af52aa7ccc2..3b537bb31a7cb9d10c79fac4ef9e2cfa70ad9971 100644
GIT binary patch
delta 46
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delta 7
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