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#Import Fask class from flask library
from flask import Flask, render_template

#Associating that class to a object. __name__ special variable that will get the value of the name of the python script
app=Flask(__name__)

#Python script Python assign the name manin this string to the file

#Case 1: Script executed __name__ = "__main__"
@app.route('/')

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from flask import Flask, render_template, request
from flask.ext.sqlalchemy import SQLAlchemy
from send_email import send_email
from sqlalchemy.sql import func

app = Flask(__name__)
db = SQLAlchemy(app)
app.config['SQLALCHEMY_DATABASE_URI']='postgresql://postgres:skateboard@localhost/salary_collector'

class Data(db.Model):

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# Regression template

# Importing the Libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('Data.csv')
X = dataset.iloc[:, 1:2].values

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# Importing the dataset
dataset = read.csv('data.csv')

# Splitting the datase into the Training set and Test set
#install.packages('caTools')
library(caTools)
set.seed(123)
split = sample.split(dataset$DependentVariable, SplitRatio = 0.8)
training_set = subset(dataset, split == TRUE)
test_se = subset(dataset, split == FALSE)

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# Regression Template

# Importing the dataset
dataset = read.csv('Position_Salaries.csv')
dataset = dataset[2:3] # Take into consideration onle 2 and 3 columns

# Splitting the datase into the Training set and Test set
#install.packages('caTools')
#library(caTools)
#set.seed(123)

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import cv2, time

#1. Create an object. Zero for external camera
video=cv2.VideoCapture(0)

#7. Play the video (Indenting)

#8. a variable
a=0

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"""This app. will collect the data about Mowbray Court Hotel  from Booking.com
Prepared by Vytautas Bielinskas at Ekistics Property Advisors LLP"""

from selenium import webdriver
options = webdriver.chrome.options.Options()
options.add_argument("--disable-extensions")

chrome_path = r"C:\Users\user\Desktop\Python\JSscrapping\chromedriver.exe"
driver = webdriver.Chrome(chrome_path)

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import requests, re
from bs4 import BeautifulSoup

l = []

base_url = 'https://www.booking.com/searchresults.en-gb.html?label=gen173nr-1FCAEoggJCAlhYSDNiBW5vcmVmaIgBiAEBmAEZwgEKd2luZG93cyAxMMgBDNgBAegBAfgBC5ICAXmoAgM;sid=20c42c0b783aafadf5e96bb173c1c595;class_interval=1;dest_id=-2601889;dest_type=city;group_adults=2;group_children=0;label_click=undef;no_rooms=1;raw_dest_type=city;room1=A%2CA;sb_price_type=total;src=index;src_elem=sb;ss=London;ssb=empty;rows=15;offset='

def count_objects(base_url):
    r = requests.get(base_url + "00")
    c = r.content

vb100

import statsmodels.formula.api as sm
def backwardElimination(x, sl):
    numVars = len(x[0])
    for i in range(0, numVars):
        regressor_OLS = sm.OLS(y, x).fit()
        maxVar = max(regressor_OLS.pvalues).astype(float)
        if maxVar > sl:
            for j in range(0, numVars - i):
                if (regressor_OLS.pvalues[j].astype(float) == maxVar):
                    x = np.delete(x, j, 1)

vb100

import statsmodels.formula.api as sm
def backwardElimination(x, SL):
    numVars = len(x[0])
    temp = np.zeros((50,6)).astype(int)
    for i in range(0, numVars):
        regressor_OLS = sm.OLS(y, x).fit()
        maxVar = max(regressor_OLS.pvalues).astype(float)
        adjR_before = regressor_OLS.rsquared_adj.astype(float)
        if maxVar > SL:
            for j in range(0, numVars - i):