80 lines
2.5 KiB
Python
80 lines
2.5 KiB
Python
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from fastapi import FastAPI, Depends, HTTPException
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from sqlalchemy.orm import Session
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from typing import List
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from models import Laptop
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from schemas import LaptopCreate, LaptopResponse
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from database import SessionLocal, engine, Base
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from pydantic import BaseModel
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import joblib
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import pandas as pd
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import numpy as np
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# Загрузка модели и списка признаков
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model = joblib.load('laptop_price_model.pkl')
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feature_columns = joblib.load('feature_columns.pkl')
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app = FastAPI()
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# Определение Pydantic модели для входных данных
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class LaptopData(BaseModel):
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processor: str
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ram: int
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os: str
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ssd: int
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display: float
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def get_db():
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db = SessionLocal()
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try:
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yield db
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finally:
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db.close()
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@app.post("/laptops/", response_model=LaptopResponse)
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def create_laptop(laptop: LaptopCreate, db: Session = Depends(get_db)):
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db_laptop = Laptop(**laptop.dict())
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db.add(db_laptop)
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db.commit()
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db.refresh(db_laptop)
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return db_laptop
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@app.get("/laptops/", response_model=List[LaptopResponse])
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def read_laptops(skip: int = 0, limit: int = 10, db: Session = Depends(get_db)):
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laptops = db.query(Laptop).offset(skip).limit(limit).all()
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return laptops
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@app.get("/laptops/{laptop_id}", response_model=LaptopResponse)
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def read_laptop(laptop_id: int, db: Session = Depends(get_db)):
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laptop = db.query(Laptop).filter(Laptop.id == laptop_id).first()
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if laptop is None:
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raise HTTPException(status_code=404, detail="Laptop not found")
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return laptop
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# Эндпоинт для предсказания цены
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@app.post("/predict_price/")
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def predict_price(data: LaptopData):
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input_data = data.dict()
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# Преобразование данных в DataFrame
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input_df = pd.DataFrame([input_data])
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# Применение One-Hot Encoding к категориальным признакам
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input_df = pd.get_dummies(input_df, columns=['processor', 'os'], drop_first=True)
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# Добавление отсутствующих признаков, если они есть
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for col in feature_columns:
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if col not in input_df.columns and col != 'price':
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input_df[col] = 0
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# Упорядочивание колонок согласно обучающей выборке
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input_df = input_df[feature_columns]
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# Предсказание цены
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predicted_price = model.predict(input_df)[0]
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return {"predicted_price": round(predicted_price, 2)}
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