From e83da8582dd26a7f740fbc1270db241b65c4fa2e Mon Sep 17 00:00:00 2001
From: maxnes3 <draw_max@mail.ru>
Date: Sun, 13 Oct 2024 16:54:23 +0400
Subject: [PATCH] com

---
 main.py          | 65 +-----------------------------------------------
 requirements.txt |  9 +++++++
 2 files changed, 10 insertions(+), 64 deletions(-)

diff --git a/main.py b/main.py
index b11b34b..fa4cae6 100644
--- a/main.py
+++ b/main.py
@@ -1,7 +1,7 @@
 from fastapi import FastAPI, Depends, HTTPException
 from sqlalchemy.orm import Session
 from typing import List
-from models import Laptop
+from models.models import Laptop
 from schemas import LaptopCreate, LaptopResponse
 from database import SessionLocal, engine, Base
 from pydantic import BaseModel
@@ -14,66 +14,3 @@ model = joblib.load('laptop_price_model.pkl')
 feature_columns = joblib.load('feature_columns.pkl')
 
 app = FastAPI()
-
-# Определение Pydantic модели для входных данных
-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)}
diff --git a/requirements.txt b/requirements.txt
index e69de29..da37b04 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -0,0 +1,9 @@
+fastapi
+uvicorn
+sqlalchemy
+psycopg2-binary
+pydantic
+joblib
+pandas
+numpy
+python-dotenv
\ No newline at end of file