def setUp(self):
self.app = create_app('testing')
self.app_context = self.app.app_context()
self.app_context.push()
db.create_all()
Role.insert_roles()
self.client = self.app.test_client(use_cookies=True)
def setUp(self):
self.app = create_app('testing')
self.app_context = self.app.app_context()
self.app_context.push()
db.create_all()
def setUp(self):
db.create_all()
db.session.commit()
def init():
db.create_all()
from flask import render_template, url_for, flash, redirect
from app1 import app, db
from app1.forms import RegistrationForm
from app1.models import User
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split # Importing the necessary libraries
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
import numpy as np
db.create_all()
@app.route("/", methods=['GET', 'POST'])
def register():
form = RegistrationForm()
if form.validate_on_submit():
# user = User(oxygen_concentration=form.oxygen_concentration.data, dry_cough=form.dry_cough.data,
# septic_shock=form.septic_shock.data, age=form.age.data, breathe_rate=form.breathe_rate.data,
# prior_disease=form.prior_disease.data)
dataset = pd.read_csv(
'C:\ProgramFiles\JetBrains\PyCharmCommunityEdition2019.3.3\Covid19_Final_dataset.csv'
)
# Reading the covid-19 dataset using pandas
# print(dataset.head())
dataset = dataset.replace([np.inf, -np.inf], np.nan).dropna(axis=0)
# Cleaning the dataset to increase the performance
septic_shock = LabelEncoder()
dry_cough = LabelEncoder() # Labelling the string data as integers
prior_disease = LabelEncoder()
dataset['Septic_Shock'] = septic_shock.fit_transform(