price-builder-backend/services/service.py

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, poly_path: str, scaler_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)}")

        try:
            self.poly_transformer = joblib.load(poly_path)
            self.scaler = joblib.load(scaler_path)
        except FileNotFoundError:
            raise Exception("Polynomial transformer or scaler file not found.")
        except Exception as e:
            raise Exception(f"Error loading polynomial transformer or scaler: {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
        for col in self.feature_columns:
            if col not in input_df.columns and col != 'price':
                input_df[col] = 0

        # Упорядочивание колонок
        input_df = input_df[self.feature_columns]

        # Преобразование с использованием PolynomialFeatures
        input_poly = self.poly_transformer.transform(input_df)

        # Масштабирование данных
        input_scaled = self.scaler.transform(input_poly)

        # Предсказание цены
        predicted_price = self.model.predict(input_scaled)[0]

        return PredictPriceResponse(predicted_price=round(predicted_price, 2))