merge comp

controllers/controller.py

@@ -0,0 +1,20 @@
+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

@@ -1,16 +1,7 @@
-from fastapi import FastAPI, Depends, HTTPException
+from fastapi import FastAPI
-from sqlalchemy.orm import Session
+from controllers import controller
-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

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

@@ -0,0 +1,40 @@
+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))