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):
        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))