.idea/misc.xml

@@ -1,7 +1,4 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="Black">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (venv)" project-jdk-type="Python SDK" />
-    <option name="sdkName" value="Python 3.12 (price-builder-backend)" />
-  </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.12 (price-builder-backend)" project-jdk-type="Python SDK" />
 </project>

.idea/price-builder-backend.iml

@@ -4,7 +4,7 @@
     <content url="file://$MODULE_DIR$">
       <excludeFolder url="file://$MODULE_DIR$/venv" />
     </content>
-    <orderEntry type="jdk" jdkName="Python 3.12 (price-builder-backend)" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.9 (venv)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

controllers/controller.py

@@ -1,27 +0,0 @@
-from fastapi import APIRouter, HTTPException
-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, summary="Predict laptop price", description="Predict the price of a laptop based on its specifications.", response_description="The predicted price of the laptop.")
-def predict_price(data: LaptopCreate):
-    """
-    Predict the price of a laptop given its specifications.
-
-    - **processor**: Type of processor (e.g., i5, i7)
-    - **ram**: Amount of RAM in GB
-    - **os**: Operating system (e.g., Windows, MacOS)
-    - **ssd**: Size of SSD in GB
-    - **display**: Size of the display in inches
-    """
-    try:
-        return laptop_service.predict_price(data.dict())
-    except Exception as e:
-        raise HTTPException(status_code=400, detail=str(e))

main.py

@@ -1,7 +1,79 @@
-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 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
+# Определение Pydantic модели для входных данных
-app.include_router(controller.router)
+class LaptopData(BaseModel):
+    processor: str
+    ram: int
+    os: str
+    ssd: int
+    display: float
+
+def get_db():
+    db = SessionLocal()
+    try:
+        yield db
+    finally:
+        db.close()
+
+
+@app.post("/laptops/", response_model=LaptopResponse)
+def create_laptop(laptop: LaptopCreate, db: Session = Depends(get_db)):
+    db_laptop = Laptop(**laptop.dict())
+    db.add(db_laptop)
+    db.commit()
+    db.refresh(db_laptop)
+    return db_laptop
+
+
+@app.get("/laptops/", response_model=List[LaptopResponse])
+def read_laptops(skip: int = 0, limit: int = 10, db: Session = Depends(get_db)):
+    laptops = db.query(Laptop).offset(skip).limit(limit).all()
+    return laptops
+
+
+@app.get("/laptops/{laptop_id}", response_model=LaptopResponse)
+def read_laptop(laptop_id: int, db: Session = Depends(get_db)):
+    laptop = db.query(Laptop).filter(Laptop.id == laptop_id).first()
+    if laptop is None:
+        raise HTTPException(status_code=404, detail="Laptop not found")
+    return laptop
+
+
+# Эндпоинт для предсказания цены
+@app.post("/predict_price/")
+def predict_price(data: LaptopData):
+    input_data = data.dict()
+
+    # Преобразование данных в DataFrame
+    input_df = pd.DataFrame([input_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 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[feature_columns]
+
+    # Предсказание цены
+    predicted_price = model.predict(input_df)[0]
+
+    return {"predicted_price": round(predicted_price, 2)}

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'.")

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)

requirements.txt

@@ -1,9 +0,0 @@
-fastapi
-uvicorn
-sqlalchemy
-psycopg2-binary
-pydantic
-joblib
-pandas
-numpy
-python-dotenv

schemas.py

@@ -0,0 +1,19 @@
+from pydantic import BaseModel
+
+class LaptopBase(BaseModel):
+    title: str
+    price: float
+    processor: str
+    ram: int
+    ssd: int
+    display: float
+
+class LaptopCreate(LaptopBase):
+    price: float
+
+class LaptopResponse(LaptopBase):
+    id: int
+    price: float
+
+    class Config:
+        orm_mode = True

schemas/schemas.py

@@ -1,22 +0,0 @@
-from pydantic import BaseModel
-
-class LaptopCreate(BaseModel):
-    processor: str
-    ram: int
-    os: str
-    ssd: int
-    display: float
-
-class LaptopResponse(BaseModel):
-    id: int
-    processor: str
-    ram: int
-    os: str
-    ssd: int
-    display: float
-
-    class Config:
-        orm_mode = True
-
-class PredictPriceResponse(BaseModel):
-    predicted_price: float

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))