def add_expense(db, cursor, users):
userlist = []
userid = {}
for i in users:
userlist.append(users[i].name)
userid[users[i].name] = users[i].id
user = input_group(
"Choose user who made the expense",
[
radio(
"Who made the expense?",
name="name",
options=userlist,
),
],
)
data = input_group(
"Expense details",
[
input("What store?", name="store", type=TEXT),
input("What is the amount?", name="amount", type=FLOAT),
],
)
add_to_db(db, cursor, "register",
(userid[user['name']], data['store'], data['amount'] * 100))
def predict():
year = input("Enter Year", type=NUMBER)
import_ = input("Import in millions", type=NUMBER)
export_ = input("Export in millions", type=NUMBER)
industrial_production = input("Industrial Production", type=FLOAT)
inflation_rate = input("Inflation rate (%)", type=FLOAT)
exchange_rate = input("Exchange rate (USD)", type=FLOAT)
population = input("Population Growth Rate in %", type=FLOAT)
stock_market = input("Stock market sales", type=FLOAT)
unemployment = input("Unemployment rate (%)", type=FLOAT)
sensex_index = input("Sensex Index(Gain/Loss)", type=FLOAT)
prediction = model.predict([[
year, import_, export_, industrial_production, inflation_rate,
exchange_rate, population, stock_market, unemployment, sensex_index
]])
output_1 = round(prediction[0], 2)
put_text('GDP increased from last year will be: ', output_1)
years = [
1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,
2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
2015, 2016, 2017, 2018, 2019, year
]
gdp_Values = [
1.06, 5.48, 4.75, 6.66, 7.57, 7.55, 4.05, 6.18, 8.85, 3.84, 4.82, 3.8,
7.86, 7.92, 7.92, 8.06, 7.66, 3.09, 7.86, 8.5, 5.24, 5.46, 6.39, 7.41,
8, 8.26, 7.04, 6.12, 4.18, output_1
]
plt.barh(years, gdp_Values, align='center')
plt.savefig('fig_.png')
img = open('fig_.png', 'rb').read()
put_image(img)
def start(self):
coll_info = input_group('第一步：采集人脸数据', [
input('face_name',
type=TEXT,
name='face_name',
required=True,
placeholder="请输入人脸名称，请使用英文标识"),
input('face_num',
type=NUMBER,
name='face_num',
required=True,
placeholder="请输入采集人脸数量",
value=5),
actions('action', [{
'label': '开始采集',
'value': 'start'
}],
name='action',
help_text='actions'),
])
if coll_info['action'] == 'start':
self.collect(coll_info['face_num'], coll_info['face_name'])
confirm = actions('第二步：人脸数据训练', ['开始训练'], help_text='训练人脸数据')
if confirm == '开始训练':
self.training()
confirm = actions('第三步：人脸检测功能', ['开始检测'], help_text='检测人脸功能')
if confirm == '开始检测':
self.recognition()
def main():
# 设置网站标题
set_env(title='自动打卡', output_animation=False)
# 说明
put_markdown(r""" # 杭电自动健康打卡系统
本系统尚不完善，存在许多bug,一切以实际效果为准.
使用说明见[自动打卡使用说明](https://jinjis.cn/index.php/archives/hduzi-dong-jian-kang-da-ka-xi-tong.html)
""",
lstrip=True)
# 输入框等交互 返回字典
data = input_group("信息", [
input("请输入token",
help_text='token获取方式见底部',
type=TEXT,
validate=check_token,
name='token'),
input("请输入省份",
help_text='城市获取方式见底部 默认为浙江省杭州市江干区',
type=NUMBER,
value='7',
name='p'),
input("请输入城市", type=NUMBER, value='9', name='c'),
input("请输入地区", type=NUMBER, value='4', name='a'),
input("Qmsg酱推送KEY", type=TEXT, help_text='推送说明见底部 不推送请填0', name='qq'),
])
list = []
for value in data.values():
list.append(value)
wb = load_workbook('data.xlsx')
ws = wb.active
ws.append(list)
wb.save('data.xlsx')
popup('提示', '信息保存成功！请不要重复提交信息 有问题请联系管理员')
def main():
height = input("请输入你的身高(cm)：", type=FLOAT)
weight = input("请输入你的体重(kg)：", type=FLOAT)
bmi_value, status = bmi(height, weight)
put_text(f'你的 BMI 值：{bmi_value:.1f}，身体状态：{status}')
def btn_click():
# toast('New messages', position='right', color='#2188ff', duration=0)
# put_markdown("> You click `%s` button" % btn_val)
# with popup('Choose Your Best'):
# input('Input your Name', type=TEXT)
# input('Input your age', type=NUMBER)
input('Input your age', type=NUMBER)
print("input Clicked")
def add_product(db, cursor):
data = input_group(
"product details",
[
input("Name?", name="name", type=TEXT),
input("Price", name="cost", type=NUMBER),
],
)
add_to_db(db, cursor, 'add_product', (data['name'], data['cost']))
def main():
info = input_group("HCL Online Exam for Machine Learning Engineer", [
input('Name ', name='name'),
input('ID NO. ', name='id'),
input('Age ', name='age', type=NUMBER),
actions('Start Exam', ['YES'], name='confirm')
])
q1 = radio(
"Which of the following is a widely used and effective machine learning algorithm based on the idea of bagging?",
["Decision Tree", "Regression", "Classification", "Random Forest"])
q2 = radio(
"To find the minimum or the maximum of a function, we set the gradient to zero because:",
[
"The value of the gradient at extrema of a function is always zero",
"Depends on the type of problem", "None of the above"
])
q3 = radio("Which of the following is a disadvantage of decision trees?", [
"Factor analysis", "Decision trees are robust to outliers",
"Decision trees are prone to be overfit", "None of the above"
])
q4 = radio(
"When performing regression or classification, which of the following is the correct way to preprocess the data?",
[
"Normalize the data -> PCA -> training",
"PCA -> normalize PCA output -> training",
"Normalize the data -> PCA -> normalize PCA output -> training"
])
q5 = radio(
"Which of the following techniques can not be used for normalization in text mining?",
[
"Stemming", "Lemmatization", "Stop Word Removal",
"None of the above"
])
c = 0
if q1 == "Random Forest":
c += 1
if q2 == "The value of the gradient at extrema of a function is always zero":
c += 1
if q3 == "Decision trees are prone to be overfit":
c += 1
if q4 == "Normalize the data -> PCA -> training":
c += 1
if q5 == "Stop Word Removal":
c += 1
if c > 3:
put_markdown("""# you are passed""")
if c <= 3:
put_markdown("""# you are failed""")
def search_food(mode):
food = {
'normal':
lambda mode: input(
'What do you want?', type=TEXT, placeholder='百頁豆腐', required=True),
'child':
lambda mode: input(
'What do you want?', type=TEXT, placeholder='蔬菜沙拉', required=True),
'dead':
lambda mode: input(
'What do you want?', type=TEXT, placeholder='香雞排', required=True)
}[mode](mode)
return food
def bmicalculator():
height = input("Please enter the height in cm", type=FLOAT)
weight = input("Please enter the weight in kg", type=FLOAT)
bmi = weight / (height / 100)**2
bmi_output = [(16, 'Severely underweight'), (18.5, 'underweight'),
(25, 'Normal'), (30, 'Overweight'), (35, 'Moderately obese'),
(float('inf'), 'severely obese')]
for tuple1, tuple2 in bmi_output:
if bmi <= tuple1:
put_text('your BMI is: %.1f and the person is:%s' % (bmi, tuple2))
break
def vaccine():
name = input("Enter your Name: ", type='text')
age = input("Enter your age:", type=NUMBER)
if age >= 18:
put_text('Check if your details')
put_table([['NAME', 'AGE'], [name, age]])
check = checkbox(options=['All Details are correct'])
if check:
selection = radio("Select your vaccine",
['Moderna', 'Pfizer', 'JnJ'])
put_text('Your Vaccine Information is Recorded')
put_table([['NAME', 'AGE', 'Vaccine'], [name, age, selection]])
def bmi_calc():
height = input("Please enter your Height in cm :", type=FLOAT)
weight = input("Please enter your weight in kg :", type=FLOAT)
bmi = weight / (height / 100)**2
bmi_output = [(16, 'Severely underweight'), (18.5, 'Underweight'),
(25, 'Normal'), (30, 'Overweight'), (35, 'Moderately obese'),
(float('inf'), 'Severely obese')]
for tuple1, tuple2 in bmi_output:
if bmi <= tuple1:
put_text("Your bmi is", bmi, ",You are", tuple2)
break
def BMI_CALC():
height = input("Enter the height in cms", type=FLOAT)
weight = input("Enter the weight in kg", type=FLOAT)
bmi = weight / (height / 100)**2
bmi_output = [(16, 'severely underweight'), (18.5, 'underweight'),
(25, 'normal'), (30, 'overweight'), (35, 'moderately obese'),
(float('inf'), 'severely obese')]
for t1, t2 in bmi_output:
if bmi <= t1:
put_text("your bmi is :%.1f and the person is :%s " % (bmi, t2))
break
def bmi():
height = input("Input your height(cm)：", type=FLOAT)
weight = input("Input your weight(kg)：", type=FLOAT)
BMI = weight / (height / 100)**2
print(f"YOUR BMI = {BMI}")
top_status = [(16, 'Severely underweight'), (18.5, 'Underweight'),
(25, 'Normal'), (30, 'Overweight'), (35, 'Moderately obese'),
(float('inf'), 'Severely obese')]
for top, status in top_status:
if BMI <= top:
put_text('Your BMI: %.1f. Category: %s' % (BMI, status))
break
def change_username(db, cursor, users):
userlist = []
userid = {}
for i in users:
userlist.append(users[i].name)
userid[users[i].name] = users[i].id
user = input_group(
"Choose user who made the expense",
[
radio(
"Who made the expense?",
name="name",
options=userlist,
),
],
)
data = input_group(
"Expense details",
[
input("What's the new name?", name="newname", type=TEXT),
],
)
querie = f"UPDATE `users` SET `user_name` = '{data['newname']}' " \
f"WHERE `users`.`user_id` = '{userid[user['name']]}'"
cursor.execute(querie)
def addNetworkAction():
def checkPSK(psk):
if len(psk) < 8 and len(psk) > 0:
return "psk password is at less 8 charactor long"
with use_scope("result", clear=True):
data = input_group("Add New SSID/Password", [
input("Interface", name="intf", value="wan"),
input("SSID", name="ssid"),
input("psk", name="psk", validate=checkPSK),
])
print(type(data['intf']), type(data['ssid']),
type(data['psk']))
print(data['intf'], data['ssid'], data['psk'])
wi.addNetwork(str(data['intf']), str(data['ssid']),
str(data['psk']))
knownNetworkAction()
def predict():
sepallength = input("Enter the sepal length: ", type=FLOAT)
sepalwidth = input("Enter the sepal width: ", type=FLOAT)
petallength = input("Enter the petal length: ", type=FLOAT)
petalwidth = input("Enter the petal width: ", type=FLOAT)
prediction = model.predict(
[[sepallength, sepalwidth, petallength, petalwidth]])
if prediction == 0:
put_text("The prediction is setosa")
elif prediction == 1:
put_text("The prediction is versicolor")
else:
put_text("The prediction is virginica")
def predict():
Year = input("Enter the Model Year：", type=NUMBER)
Year = 2021 - Year
Present_Price = input("Enter the Present Price(in LAKHS)", type=FLOAT)
Kms_Driven = input("Enter the distance it has travelled(in KMS)：",
type=FLOAT)
Kms_Driven2 = np.log(Kms_Driven)
Owner = input(
"Enter the number of owners who have previously owned it(0 or 1 or 2 or 3)",
type=NUMBER)
Fuel_Type = select('What is the Fuel Type', ['Petrol', 'Diesel', 'CNG'])
if (Fuel_Type == 'Petrol'):
Fuel_Type = 239
elif (Fuel_Type == 'Diesel'):
Fuel_Type = 60
else:
Fuel_Type = 2
Seller_Type = select('Are you a dealer or an individual',
['Dealer', 'Individual'])
if (Seller_Type == 'Individual'):
Seller_Type = 106
else:
Seller_Type = 195
Transmission = select('Transmission Type', ['Manual Car', 'Automatic Car'])
if (Transmission == 'Manual Car'):
Transmission = 261
else:
Transmission = 40
prediction = model.predict([[
Present_Price, Kms_Driven2, Fuel_Type, Seller_Type, Transmission,
Owner, Year
]])
output = round(prediction[0], 2)
if output < 0:
put_text("Sorry You can't sell this Car")
else:
put_text('You can sell this Car at price:', output)
put_markdown(
'**We can put some taken information here from the user in the form of table. Thanks..!!**'
)
def customize_text():
return input_group('Text Fonts and Size', [
select(label='Select your font',
options=FONTS,
value='Arial',
name='font'),
input("Select your text size", value='16', type=NUMBER, name='size')
])
def edit():
info = input_group('编辑音乐标签', [
input('标题', name='title', value=edited_info['title']),
input('专辑', name='album', value=edited_info['album']),
input('艺术家', name='artist', value=edited_info['artist']),
file_upload('封面图像', accept='image/*', name='img'),
],
cancelable=True)
if info is None:
return
if info.get('img'):
info['img'] = info['img']['content']
for k, v in info.items():
if v:
edited_info[k] = v
show_music_info(current_tag)
async def setup_raft(raft_addr, cluster):
"""初始化/连接 Raft 集群
:param raft_addr: 本节点用于Raft集群通信的地址；为None时表示加入现有集群，本节点地址由本节点第一位用户输入
:param cluster: 集群节点地址列表；为None时表示加入现有集群，集群节点地址由本节点第一位用户输入
:return: 本节点Raft集群通信地址
"""
global raft_server
mode = 'init'
if not raft_addr: # raft_addr 为None时，表示加入Raft集群
mode = 'join'
currhost = session.get_info().origin.rsplit(":", 1)[0].split("//",
1)[-1]
data = await input_group("加入Raft集群", [
input("当前节点的Raft通信端口", name="port"),
input("当前节点的Host地址",
name="host",
value=currhost,
help_text="其他节点需要可以通过此Host与当前节点通信"),
input("集群节点地址",
name="remote",
placeholder='host:ip',
help_text="填入集群中任一节点的地址即可")
])
raft_addr = '%s:%s' % (data['host'], data['port'])
cluster = join_cluster(raft_addr, data['remote'])
if not cluster:
put_markdown("### 加入集群失败")
return
raft_port = raft_addr.split(":", 1)[-1]
cfg = SyncObjConf(dynamicMembershipChange=True,
fullDumpFile=raft_addr + '.data',
onStateChanged=partial(onStateChanged, node=raft_addr),
bindAddress="0.0.0.0:%s" % raft_port)
raft_server = SyncObj(
raft_addr,
cluster,
consumers=[chat_msgs, node_user_cnt, node_webui_addr],
conf=cfg)
if mode == 'join':
send_msg(ADMIN_USER, '节点`%s`加入集群' % raft_addr, instant_output=False)
return raft_addr
def display_recommendations(document_scores):
clear()
global main_document_scores
try:
main_document_scores = document_scores.tolist()
put_text('' + str(len(main_document_scores)))
except Exception as ex:
put_text(ex)
try:
clear('BTV')
except:
pass
img = open('Images/DesiSafar Logo.jpg', 'rb').read()
put_image(img, width='900px')
put_markdown('# **IR Project - Group Number 4**')
recommendations = int(input('Enter number of recommendations you want : '))
# put_buttons(['Home'], onclick=[choices])
# put_buttons(['Check another algorithm'], onclick=[select_recommendation_system])
ds = document_scores
recommendations_index = ds.argsort()[-recommendations:][::-1]
print(recommendations_index)
cities = dataset2['City']
desc = dataset2['description']
displayed_recommendations = []
displayed_recommendations_index = {}
put_text('Scores calculated in time : ' + str(total_time))
for i in range(recommendations_index.shape[0]):
put_html('<hr>')
put_html('<hr>')
pic = 'Images/' + str(cities[recommendations_index[i]]) + '.jpg'
img = open(pic, 'rb').read()
put_image(img, width='1500px')
put_markdown("# *`%s`*" % cities[recommendations_index[i]])
t = desc[recommendations_index[i]]
t = t.strip()
t = t.replace('-', '')
put_text(t)
displayed_recommendations.append(cities[recommendations_index[i]])
displayed_recommendations_index[cities[
recommendations_index[i]]] = recommendations_index[i]
print('-------------------------------------------------------')
try:
displayed_recommendations.append('Home')
displayed_recommendations.append('Check another algorithm')
selected_recommendation = select('Explore :',
displayed_recommendations)
if (selected_recommendation == 'Home'):
choices()
if (selected_recommendation == 'Check another algorithm'):
select_recommendation_system()
display_details(
displayed_recommendations_index[selected_recommendation])
except Exception as ex:
put_text(ex)
def app():
pages = get_pages()
text_info = customize_text()
save_location = input("What is the name of your PDF file?", required=True)
create_pdf(pages, font='Arial', size=16, save_location=save_location)
put_text("Congratulations! A PDF file is generated for you.")
def voting():
name = input('Enter your name', type="text")
age = input('Enter your age', type=NUMBER)
if age >= 18:
put_text('Check your details..')
put_table([['NAME', 'AGE'], [name, age]])
check = checkbox(options=['All details are correct.'])
if check:
selction = radio("Select your party", ['Congress', 'BJP', 'AAP'])
records = {'name': name, 'age': age, 'vote_for': selction}
coll.insert_one(records)
put_text('Thanks, Your response has been recorded.')
keep_voting = radio('Keep Voting', ['Yes', 'No'])
if keep_voting == 'Yes':
voting()
else:
return style(
put_text(
'Voting has been ended, We will announce the result soon..'
), 'color:green')
else:
style(put_text('You are not eligible for voting..'), 'color:red')
keep_voting = radio('Keep Voting', ['Yes', 'No'])
if keep_voting == 'Yes':
voting()
else:
return style(
put_text(
'Voting has been ended, We will announce the result soon..'
), 'color:green')
def predict():
Year = input("Enter model Year: ", type=NUMBER)
Year = 2021 - Year
Present_Price = input("Enter Present Price(in LAKHS): ", type=FLOAT)
Kms_Driven = input("Enter distance it has travelled(in KMS): ", type=FLOAT)
Kms_Driven2 = np.log(Kms_Driven)
Owner = input(
"Enter number of owners who have previously owned it(0 or 1 or 2 or 3): ",
type=NUMBER)
Fuel_type = select('What is the fuel type', ['Petrol', 'Diesel', 'CNG'])
if (Fuel_type == 'Petrol'):
Fuel_type = 239
elif (Fuel_type == 'Diesel'):
Fuel_type = 60
else:
Fuel_type = 2
Seller_Type = select('Dealer or Individual?', ['Dealer', 'Individual'])
if Seller_Type == 'Individual':
Seller_Type = 106
else:
Seller_Type = 195
Transmission = select('Transmission Type', ['Manual Car', 'Automatic Car'])
if Transmission == 'Manual Car':
Transmission = 261
else:
Transmission = 40
prediction = model.predict([[
Present_Price, Kms_Driven2, Fuel_type, Seller_Type, Transmission,
Owner, Year
]])
output = round(prediction[0], 2)
if output < 0:
put_text('Sorry you can not sell this car')
else:
put_text('Sell this car at ', output, 'price')
def main():
put_markdown('''
# Salary Prediction Web App (`Using PyWebIO`)
''',
lstrip=True)
model_inputs = input_group("Enter the following information", [
input("Rating of the Job", name='rating', type=FLOAT),
select("Job Sector",
name='job_sec',
options=[(i, i) for i in [
'Information Technology', 'Business Services', 'Education',
'Finance', 'Government', 'Travel & Tourism', 'Health Care'
]]),
select("Job Role",
name='job_role',
options=[('Data Scientist', 'data scientist'),
('Data Engineer', 'data engineer'),
('Analyst', 'analyst'),
('Machine Learning Engineer', 'mle'),
('Director', 'director'), ('Manager', 'manager')]),
radio("Are you familiar with Python?",
name='py_choice',
options=[('Yes', 1), ('No', 0)]),
radio("Are you familiar with R?",
name='r_choice',
options=[('Yes', 1), ('No', 0)]),
radio("Are you familiar with Tableau?",
name='t_choice',
options=[('Yes', 1), ('No', 0)]),
radio("Are you familiar with Power Bi?",
name='pi_choice',
options=[('Yes', 1), ('No', 0)]),
radio("Are you familiar with Machine Learning?",
name='ml_choice',
options=[('Yes', 1), ('No', 0)]),
radio("Are you familiar with Deep Learning?",
name='dl_choice',
options=[('Yes', 1), ('No', 0)]),
])
prediction_df = pd.DataFrame(data=[[
model_inputs[i] for i in [
'job_sec', 'job_role', 'py_choice', 'r_choice', 't_choice',
'pi_choice', 'ml_choice', 'dl_choice', 'rating'
]
]],
columns=[
'Sector', 'job_sim', 'python_yn', 'R_yn',
'tableau', 'power bi', 'ml', 'dl',
'Rating'
])
expectedSalary = prediction(prediction_df)
put_markdown("### Predicted Salary: {}k Dollars".format(expectedSalary))
def mathemticaloperation():
a = input("Enter the firt number：", type=FLOAT)
b = input("Enter the second number：", type=FLOAT)
c=0
operation = radio("Choose one operation", options=['+', '*', '/', '%'])
operation_name=""
if operation=="+":
operation_name="Addition"
c=a+b
elif operation=="*":
operation_name="Multiplication"
c=a*b
elif operation=="/":
operation_name="Division"
c=a/b
else:
operation_name="Modulus"
c=a%b
put_text('The operation selected is: %s. and the output is: %.1f' % (operation_name, c))
def main():
"""BMI Calculation
计算BMI指数的简单应用
"""
put_markdown("""# BMI指数
[`BMI指数`](https://baike.baidu.com/item/%E4%BD%93%E8%B4%A8%E6%8C%87%E6%95%B0/1455733)（Body Mass Index，BMI），是用体重千克数除以身高米数的平方得出的数字，是国际上常用的衡量人体胖瘦程度以及是否健康的一个标准。
成年人的BMI值处于以下阶段
| 体形分类 | BMI值范围 |
| -------- | --------- |
| 极瘦 | BMI<14.9 |
| 偏瘦 | 14.9≤BMI<18.4 |
| 正常 | 18.4≤BMI<22.9 |
| 过重 | 22.9≤BMI<27.5 |
| 肥胖 | 27.5≤BMI<40 |
| 非常肥胖 | BMI≥40 |
## BMI指数计算器
本程序的源代码[链接](https://github.com/wang0618/PyWebIO/blob/dev/demos/bmi.py)
""",
strip_indent=4)
info = input_group('计算BMI：', [
input("请输入你的身高(cm)", name="height", type=FLOAT),
input("请输入你的体重(kg)", name="weight", type=FLOAT),
])
BMI = info['weight'] / (info['height'] / 100)**2
top_status = [(14.9, '极瘦'), (18.4, '偏瘦'), (22.9, '正常'), (27.5, '过重'),
(40.0, '肥胖'), (float('inf'), '非常肥胖')]
for top, status in top_status:
if BMI <= top:
put_markdown('你的 BMI 值: `%.1f`，身体状态：`%s`' % (BMI, status))
break
def sign_up_page():
clear()
img = open('Images/DesiSafar Logo.jpg', 'rb').read()
put_image(img, width='900px')
put_markdown('# **Sign UP**')
# data = input_group("Basic info", [
# input('Input your name', name='name'),
# input('Input your age', name='age', type=NUMBER)
# ], validate=check_form)
# put_markdown("`data = %r`" % data)
info = input_group("User info", [
input('Name', name='name', required=True),
input(
'Input your age',
name='age',
type=NUMBER,
required=True,
),
input('password', type=PASSWORD, name='password', required=True),
select('Select your state', [
'Andaman And Nicobar Islands', 'Andhra Pradesh',
'Arunanchal Pradesh', 'Assam', 'Bihar', 'Chandigarh',
'Chattisgarh', 'Dadra And Nagar Haveli', 'Delhi', 'Goa', 'Haryana',
'Himachal Pradesh', 'Jammu & Kashmir', 'Jharkhand', 'Karnataka',
'Kerala', 'Lakshadweep Island', 'Madhya Pradesh', 'Maharashtra',
'Manipur', 'Meghalaya', 'Mizoram', 'Nagaland', 'Odisha',
'Pondicherry', 'Punjab', 'Rajasthan', 'Sikkim'
'Tamil Nadu', 'Tripura', 'Uttar Pradesh', 'Uttarakhand',
'West Bengal', 'Gujarat', 'Telangana', 'Daman & Diu', 'Ladakh'
],
name='state',
required=True)
],
validate=check_form)
# put_markdown("`info = %r`" % info)
# put_text(info['name'],info['age'],info['password'],info['state'])
insert_details(info)
pywebio.session.hold()
def whoAreYou():
with use_scope('whoAreYou', if_exist='remove'):
global name
img = open('eatago_logo.jpg', 'rb').read()
style(put_image(img, width='300px'), 'display: flex'
'justify-content: center')
if name == '':
name = input("What is your name?",
type=TEXT,
placeholder='Annie',
required=True)
choose_eat_type(name)
