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)