controllers/controller.py

@@ -1,20 +0,0 @@
-from fastapi import APIRouter, Depends, HTTPException
-from sqlalchemy.orm import Session
-from typing import List
-from schemas.schemas import LaptopCreate, LaptopResponse, PredictPriceResponse
-from services.service import LaptopService
-import os
-
-router = APIRouter()
-
-# Инициализация сервиса
-MODEL_PATH = os.getenv("MODEL_PATH", "laptop_price_model.pkl")
-FEATURE_COLUMNS_PATH = os.getenv("FEATURE_COLUMNS_PATH", "feature_columns.pkl")
-laptop_service = LaptopService(model_path=MODEL_PATH, feature_columns_path=FEATURE_COLUMNS_PATH)
-
-@router.post("/predict_price/", response_model=PredictPriceResponse)
-def predict_price(data: LaptopCreate):
-    try:
-        return laptop_service.predict_price(data.dict())
-    except Exception as e:
-        raise HTTPException(status_code=400, detail=str(e))

main.py

@@ -1,7 +1,16 @@
-from fastapi import FastAPI
+from fastapi import FastAPI, Depends, HTTPException
-from controllers import controller
+from sqlalchemy.orm import Session
+from typing import List
+from models.models import Laptop
+from schemas import LaptopCreate, LaptopResponse
+from database import SessionLocal, engine, Base
+from pydantic import BaseModel
+import joblib
+import pandas as pd
+import numpy as np
+
+# Загрузка модели и списка признаков
+model = joblib.load('laptop_price_model.pkl')
+feature_columns = joblib.load('feature_columns.pkl')
 
 app = FastAPI()
-
-# Подключение маршрутов
-app.include_router(controller.router)

models/models.py

@@ -5,8 +5,9 @@ class Laptop(Base):
     __tablename__ = "laptops"
 
     id = Column(Integer, primary_key=True, index=True)
-    processor = Column(String, index=True)
+    title = Column(String, index=True)
+    price = Column(Float)
+    processor = Column(String)
     ram = Column(Integer)
-    os = Column(String, index=True)
     ssd = Column(Integer)
     display = Column(Float)

services/modelBuilder.py

@@ -7,7 +7,7 @@ import joblib
 import re
 
 # Шаг 1: Загрузка данных
-df = pd.read_csv('../laptops.csv')
+df = pd.read_csv('laptops.csv')
 
 # Шаг 2: Проверка и очистка имен столбцов
 print("Имена столбцов до очистки:")
@@ -134,10 +134,10 @@ for name, mdl in models.items():
     print(f"{name} - MAE: {mae}, RMSE: {rmse}, R²: {r2}")
 
 # Шаг 12: Сохранение модели
-joblib.dump(model, '../laptop_price_model.pkl')
+joblib.dump(model, 'laptop_price_model.pkl')
 print("\nМодель сохранена как 'laptop_price_model.pkl'.")
 
 # Дополнительно: Сохранение колонок, полученных после One-Hot Encoding, для использования в бэкенде
 feature_columns = X.columns.tolist()
-joblib.dump(feature_columns, '../feature_columns.pkl')
+joblib.dump(feature_columns, 'feature_columns.pkl')
 print("Сохранены названия признаков в 'feature_columns.pkl'.")

services/service.py

@@ -1,40 +0,0 @@
-import pandas as pd
-import joblib
-from typing import List, Dict
-from schemas.schemas import LaptopCreate, LaptopResponse, PredictPriceResponse
-
-class LaptopService:
-    def __init__(self, model_path: str, feature_columns_path: str):
-        try:
-            self.model = joblib.load(model_path)
-        except FileNotFoundError:
-            raise Exception(f"Model file not found at {model_path}")
-        except Exception as e:
-            raise Exception(f"Error loading model: {str(e)}")
-
-        try:
-            self.feature_columns = joblib.load(feature_columns_path)
-        except FileNotFoundError:
-            raise Exception(f"Feature columns file not found at {feature_columns_path}")
-        except Exception as e:
-            raise Exception(f"Error loading feature columns: {str(e)}")
-
-    def predict_price(self, data: Dict[str, any]) -> PredictPriceResponse:
-        # Преобразование данных в DataFrame
-        input_df = pd.DataFrame([data])
-
-        # Применение One-Hot Encoding к категориальным признакам
-        input_df = pd.get_dummies(input_df, columns=['processor', 'os'], drop_first=True)
-
-        # Добавление отсутствующих признаков, если они есть
-        for col in self.feature_columns:
-            if col not in input_df.columns and col != 'price':
-                input_df[col] = 0
-
-        # Упорядочивание колонок согласно обучающей выборке
-        input_df = input_df[self.feature_columns]
-
-        # Предсказание цены
-        predicted_price = self.model.predict(input_df)[0]
-
-        return PredictPriceResponse(predicted_price=round(predicted_price, 2))