На самом деле много чего добавил. Например все виды моделей, что если если меньше 0,2 вероятность то Not Found ну и вывод всех классов для Андрей

database.py

@@ -1,9 +1,10 @@
 from typing import Optional
 from sqlalchemy.ext.asyncio import create_async_engine, async_sessionmaker
 from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column
-from sqlalchemy import Column, DateTime
+from sqlalchemy import Column, DateTime, Enum
 from sqlalchemy.sql import func
 from datetime import datetime
+from enums import TypeMood, TypeModel
 
 engine = create_async_engine("sqlite+aiosqlite:///questions.db")
 new_session = async_sessionmaker(engine, expire_on_commit=False)
@@ -16,7 +17,8 @@ class QuestionOrm(Model):
 
     id: Mapped[int] = mapped_column(primary_key=True)
     email_user: Mapped[str]
-    type_question: Mapped[bool]
+    type_mood: Mapped[TypeMood] = mapped_column(Enum(TypeMood))
+    type_model: Mapped[TypeModel] = mapped_column(Enum(TypeModel))
     question: Mapped[str]
     answer: Mapped[Optional[str]]
     question_time: Mapped[datetime] = mapped_column(DateTime, server_default=func.now())

enums.py

@@ -0,0 +1,10 @@
+from enum import Enum
+
+class TypeMood(str, Enum):
+    POSITIVE = "Posititve"
+    NEGATIVE = "Negative"
+
+class TypeModel(str, Enum):
+    LSTM = "LSTM"
+    GRU = "GRU"
+    CNN = "CNN"

model.py

@@ -4,8 +4,17 @@ import tensorflow as tf
 from keras.src.legacy.preprocessing.text import Tokenizer
 from keras.src.utils import pad_sequences
 
+from enums import TypeMood, TypeModel
+
 # Загрузка модели
-model = tf.keras.models.load_model('.//neural_network/models/model/best_model_lstm_negative.keras')
+model_lstm_negative = tf.keras.models.load_model('.//neural_network/models/model/best_model_lstm_negative.keras')
+model_gru_negative = tf.keras.models.load_model('.//neural_network/models/model/best_model_gru_negative.keras')
+model_cnn_negative = tf.keras.models.load_model('.//neural_network/models/model/best_model_cnn_negative.keras')
+
+model_lstm_positive = tf.keras.models.load_model('.//neural_network/models/model/best_model_lstm_positive.keras')
+model_gru_positive = tf.keras.models.load_model('.//neural_network/models/model/best_model_gru_positive.keras')
+model_cnn_positive= tf.keras.models.load_model('.//neural_network/models/model/best_model_cnn_positive.keras')
+
 
 # Загрузка токенизатора
 with open('.//neural_network/tokenization/tokenizer_negative.pickle', 'rb') as handle:
@@ -18,21 +27,31 @@ with open('.//neural_network/classification/class_names_negative.txt', 'r', enco
 def preprocess_text(text: str):
     # Токенизация текста
     sequences = tokenizer.texts_to_sequences([text])
-    # Преобразование последовательностей в фиксированной длины
+    # Преобразование последовательностей в фиксированной длине
     padded_sequences = pad_sequences(sequences, maxlen=90)  # 90 - длина последовательности, используемая при обучении
     return padded_sequences
 
-def predict_answer(question: str) -> str:
+def predict_answer(question: str, type_mood: TypeMood, type_model: TypeModel) -> str:
+    if type_model == TypeModel.LSTM and type_mood == TypeMood.NEGATIVE:
+        model = model_lstm_negative
+    elif type_model == TypeModel.LSTM and type_mood == TypeMood.POSITIVE:
+        model = model_lstm_positive
+    elif type_model == TypeModel.GRU and type_mood == TypeMood.NEGATIVE:
+        model = model_gru_negative
+    elif type_model == TypeModel.GRU and type_mood == TypeMood.POSITIVE:
+        model = model_gru_positive
+    elif type_model == TypeModel.CNN and type_mood == TypeMood.NEGATIVE:
+        model = model_cnn_negative
+    elif type_model == TypeModel.CNN and type_mood == TypeMood.POSITIVE:
+        model = model_cnn_positive
+    else:
+        raise ValueError("Unsupported model type")
+
     # Предобработка вопроса
-    print("Вопрос:", question)
     input_data = preprocess_text(question)
-    print("Предобработанные данные:", input_data)
     # Предсказание
-    prediction = model.predict(input_data)
+    prediction = model.predict(input_data)[0]
-    print("Предсказание:", prediction)
-    # Определение индекса класса с наибольшей вероятностью
-    predicted_index = np.argmax(prediction[0])
     # Получение имени класса
+    predicted_index = np.argmax(prediction)
     predicted_class = class_names[predicted_index]
-    print("Предсказанный класс:", predicted_class)
+    return predicted_class, prediction
-    return predicted_class  # Возвращаем имя предсказанного класса

repository.py

@@ -3,17 +3,24 @@ from sqlalchemy.orm import joinedload
 from datetime import datetime
 
 from database import new_session, QuestionOrm
+from enums import TypeMood, TypeModel
 from schemas import SQuestionAdd, SQuestion
 from model import predict_answer
 
 class QuestionRepository:
     @classmethod
-    async def add_one(cls, data: SQuestionAdd) -> int:
+    async def add_one(cls, data: SQuestionAdd, type_mood: TypeMood, type_model: TypeModel) -> int:
         async with new_session() as session:
             question_dict = data.model_dump()
 
             # Предсказание ответа с помощью модели
-            answer = predict_answer(question_dict["question"])
+            predicted_class, prediction = predict_answer(question_dict["question"], type_mood, type_model)
+
+            # Проверка вероятностей классов
+            if max(prediction) < 0.2:
+                answer = "Not Found"
+            else:
+                answer = predicted_class
 
             question_dict["answer"] = answer
             question_dict["question_time"] = datetime.now()
@@ -41,3 +48,12 @@ class QuestionRepository:
             question_models = result.scalars().all()
             question_schemas = [SQuestion.model_validate(question_model) for question_model in question_models]
             return question_schemas
+
+    @classmethod
+    async def find_by_email(cls, email_user: str) -> list[SQuestion]:
+        async with new_session() as session:
+            query = select(QuestionOrm).where(QuestionOrm.email_user == email_user)
+            result = await session.execute(query)
+            question_models = result.scalars().all()
+            question_schemas = [SQuestion.model_validate(question_model) for question_model in question_models]
+            return question_schemas

router.py

@@ -1,6 +1,8 @@
 from typing import Annotated
 from fastapi import APIRouter, Depends
+from typing import List
 
+from enums import TypeMood, TypeModel
 from repository import QuestionRepository
 from schemas import SQuestionAdd, SQuestion, SQuestionId
 
@@ -9,14 +11,34 @@ router = APIRouter(
     tags=["Questions"],
 )
 
+@router.get("/class_negative")
+async def get_class_names() -> List[str]:
+    with open(".//neural_network/classification/class_names_negative.txt", "r", encoding="utf-8") as file:
+        class_names = [line.strip() for line in file.readlines()]
+    return class_names
+
+@router.get("/class_positive")
+async def get_class_names() -> List[str]:
+    with open(".//neural_network/classification/class_names_positive.txt", "r", encoding="utf-8") as file:
+        class_names = [line.strip() for line in file.readlines()]
+    return class_names
+
 @router.post("")
 async def add_question(
     question: Annotated[SQuestionAdd, Depends()],
+    type_mood: TypeMood,  # Добавлен параметр type_mood
+    type_model: TypeModel,  # Добавлен параметр type_model
 ) -> SQuestionId:
-    question_id = await QuestionRepository.add_one(question)
+    question_id = await QuestionRepository.add_one(question, type_mood, type_model)  # Передача параметров type_mood и type_model
     return {"ok": True, "question_id": question_id}
 
 @router.get("")
 async def get_questions() -> list[SQuestion]:
     questions = await QuestionRepository.find_all()
     return questions
+
+
+@router.get("/{email_user}")
+async def get_questions_by_email(email_user: str) -> list[SQuestion]:
+    questions = await QuestionRepository.find_by_email(email_user)
+    return questions

schemas.py

@@ -1,10 +1,12 @@
 from typing import Optional
 from pydantic import BaseModel, ConfigDict
 from datetime import datetime
+from enums import TypeMood, TypeModel
 
 class SQuestionAdd(BaseModel):
     email_user: str
-    type_question: bool
+    type_mood: TypeMood
+    type_model: TypeModel
     question: str
 
 class SQuestion(SQuestionAdd):