def inference():
if request.method == 'POST':
sex = request.form['sex']
smoker =request.form['smoker']
bmi = request.form['bmi']
region = request.form['region']
age=request.form['age']
children = request.form['kid']
if not sex:
flash('enter sex male or female')
else:
expense = read(sex,smoker,region,age, bmi,children)
#Gcloud
config ={
'user': '******',
'password': '******',
'host': '34.121.155.244',
'database':'insurancedb',
'client_flags': [ClientFlag.SSL],
'ssl_ca': 'ssl/server-ca.pem',
'ssl_cert': 'ssl/client-cert.pem',
'ssl_key': 'ssl/client-key.pem'
}
# now we establish our connection
with sql.connect(**config) as cnx:
cur = cnx.cursor() # initialize connection cursor
cur.execute("insert into data(sex,smoker,region,age,bmi,children,insurance) VALUES (%s,%s,%s,%s,%s,%s,%s)",(sex,smoker,region,age,bmi,children,expense))
cnx.commit()
cnx.close()
return render_template('inference.html',expense = expense)
return render_template('inference.html')
def predict(*args):
try:
clients_data = model.read(self.filename.get())
except:
tkMessageBox.showinfo(
"Error", "Select a weather forecast from the browser")
regr = model.load_regressor(self.picked_regressor.get())
try:
clients_Y = model.predict(clients_data, regr)
model.save_in_file(clients_Y)
model.show_plots(np.arange(24), clients_Y)
except ValueError:
tkMessageBox.showinfo("Error",
"Select a valid weather forecast")
def inference():
if request.method == 'POST':
sex = request.form['sex']
smoker = request.form['smoker']
bmi = request.form['bmi']
region = request.form['region']
age = request.form['age']
children = request.form['kid']
if not sex:
flash('enter sex male or female')
else:
expense = read(sex, smoker, region, age, bmi, children)
return render_template('/inference.html', expense=expense)
return render_template('/inference.html')
def accuracy_of_model(n):
sample_data = pd.read_csv('data/insurance.csv')
sum = 0
#print(sample_data)
for i in sample_data.head(n).itertuples():
sex = i[2]
smoker = i[5]
location = i[6]
age = i[1]
bmi = i[3]
kids = i[4]
expenses = float(i[7])
#print(sex, smoker, location, age, bmi, kids)
predicted = float(read(sex, smoker, location, age, bmi, kids))
accuracy = abs(predicted - expenses) / expenses
sum += accuracy
print(accuracy)
#print(predicted)
avg_accuracy = 100 - (sum / n)
print(avg_accuracy)
return avg_accuracy
#cut data from images
# cut1 = observation.Datacut(img1, 500, 500)
# cut2 = observation.Datacut(img2, 300, 300)
# cut3 = observation.Datacut(img3, 400, 400)
cut1 = observation.Datacut(img1, 660, 760)
cut2 = observation.Datacut(img1, 660, 1040)
cut3 = observation.Datacut(img1, 660, 1200)
#create list of datacuts
#NOTE: at present,
datacuts = [cut1, cut2, cut3]
#load initial atmospheric model
init_model = model.read('init_model.dat')
#check validity of initial atmosphere model
init_model.check_validity()
#write out model to a text file that is input to the fortran
#adding/doubling code
init_model.write_ft_input()
#
#Note: can also do init_model.write_ft_input(filename='workmodl000')
#write out illumination geometry data for observation into
#format that can be read by fortran code
interface.write_geom_model(datacuts, filename = 'geom.dat')
#note: "geom.dat" is not very descriptive, but this is the default
#string that the fortran code automatically uses. Not sure how to
template()
option = cnt.choise()
if option not in ['c', 'r', 'u', 'd', 'e']:
print("Opção Inválida!!!")
#Create ----------------------------------------------------------
if option == "c":
status = db.create()
if status == True:
print("Deu certo...")
else:
print("Deu Ruim...")
#Read ------------------------------------------------------------
if option == "r":
rows = db.read()
print(
"--------------------------------------------------------------------------------------------"
)
for row in range(len(rows)):
print("Serviço: {} | Usuário: {} | Senha: {} | ID: {} ".
format(rows[row][1], rows[row][2], rows[row][3],
rows[row][0]))
print(
"--------------------------------------------------------------------------------------------"
)
#Update ------------------------------------------------------------
if option == "u":
db.update()
def get(self, name):
phone = model.read(name)
if phone is None:
return '', 404
return phone[0]
def combine_path_read(model, path_pre, path_name, path_postfix):
path = join_path(path_pre, path_name, path_postfix)
lines = model.read(path)
return lines
def check_login(email):
table_title = 'pkp_companies'
list = ['email']
values = [email]
result = read(table_title, list, values)
return result
# cut2 = observation.Datacut(img2, 300, 300)
# cut3 = observation.Datacut(img3, 400, 400)
#NOTE: remember that this accepts coordinates in Python, which is row-major
#whereas IDL is column-major.
cut1 = observation.Datacut(img1, 660, 1000)
cut2 = observation.Datacut(img1, 660, 1440)
cut3 = observation.Datacut(img1, 660, 1600)
#create list of datacuts
#NOTE: at present,
datacuts = [cut1, cut2, cut3]
#load initial atmospheric model
init_model = model.read('test_model.dat')
#check validity of initial atmosphere model
init_model.check_validity()
#write out model to a text file that is input to the fortran
#adding/doubling code
init_model.write_ft_input()
#
#Note: can also do init_model.write_ft_input(filename='workmodl000')
#write out illumination geometry data for observation into
#format that can be read by fortran code
interface.write_geom_model(datacuts, filename = 'geom.dat')
#note: "geom.dat" is not very descriptive, but this is the default
#string that the fortran code automatically uses. Not sure how to
#cut data from images
# cut1 = observation.Datacut(img1, 500, 500)
# cut2 = observation.Datacut(img2, 300, 300)
# cut3 = observation.Datacut(img3, 400, 400)
cut1 = observation.Datacut(img1, 600, 600)
cut2 = observation.Datacut(img1, 700, 700)
cut3 = observation.Datacut(img1, 500, 500)
#create list of datacuts
#Note: at present, datacuts must have at least 3 cuts or else
#fortran code will reject the geom.dat file
datacuts = [cut1, cut2, cut3]
#load initial atmospheric model
init_model = model.read('imodel000.dat')
#check validity of initial atmosphere model
init_model.check_validity()
#write out model to a text file that is input to the fortran
#adding/doubling code
init_model.write_ft_input()
#
#Note: can also do init_model.write_ft_input(filename='workmodl000')
#write out illumination geometry data for observation into
#format that can be read by fortran code
interface.write_geom_model(datacuts, filename = 'geom.dat')
#note: "geom.dat" is not very descriptive, but this is the default
#string that the fortran code automatically uses. Not sure how to