from flask import Flask, request, jsonify

import joblib

import pandas as pd

import pickle

import numpy as np

import re

import ast

import matplotlib.pyplot as plt

import seaborn as sns

from sklearn import model_selection

from sklearn.model_selection import train_test_split

from sklearn.feature_selection import SelectKBest, f_classif, f_regression

from sklearn import preprocessing

from sklearn.preprocessing import LabelEncoder

from sklearn import linear_model

from sklearn.linear_model import LinearRegression

from sklearn.tree import DecisionTreeRegressor

from sklearn.ensemble import RandomForestRegressor

from sklearn.ensemble import GradientBoostingRegressor

from sklearn.metrics import median_absolute_error

import optuna

from sklearn import ensemble

from sklearn import metrics

from sklearn.linear_model import Ridge

from sklearn.preprocessing import MinMaxScaler

from sklearn.metrics import mean_squared_error

from sklearn.model_selection import GridSearchCV

from sklearn.model_selection import cross_val_score

from sklearn.preprocessing import StandardScaler

from sklearn.decomposition import PCA

from sklearn.linear_model import Lasso

from sklearn.metrics import r2_score

import xgboost as xgb

import time

from sklearn.pipeline import Pipeline

from sklearn.base import BaseEstimator, TransformerMixin

import joblib

from transformers import (

StatusTransformer,

StreetTransformer,

PropertyTypeTransformer,

BathsTransformer,

CityStateTransformer,

HomeFactsTransformer,

CustomFeaturesTransformer,

SchoolsTransformer,

LotSizeSqftTransformer,

ZipcodeTransformer,

BedsTransformer,

StoriesTransformer,

CombinedMlsIDTransformer,

PoolTransformer,

YearBuiltTransformer,

RemodeledYearTransformer,

LotSizeTransformer,

PricePerSqftTransformer,

AvrDistanceTransformer,

MinDistanceTransformer,

AverageRatingTransformer,

CategoricalEncoder,

MlsIDFireplaceTransformer,

FeatureDropper

)

app = Flask(__name__)

# Загрузка pipeline и модели

pipeline = joblib.load('pipeline.pkl')

model = joblib.load('model.pkl')

@app.route('/')

def index():

return "Привет! Это мой веб-сервис для прогнозирования стоимости недвижимости."

@app.route('/upload', methods=['POST'])

def upload_file():

return jsonify({'error': 'Нет файла в запросе'}), 400

if __name__ == '__main__':

app.run('localhost', 5001)