def main(): st.beta_set_page_config(page_title="IPO", page_icon="📈", layout="wide") st.title('Stock Charts of Last 100 IPOs') with st.spinner('Loading data...'): ipo_url = 'https://www.iposcoop.com/last-100-ipos/' r = requests.get(ipo_url) df_list = pd.read_html(r.text) df = df_list[0] st.dataframe(df) st.markdown('Data source: https://www.iposcoop.com/last-100-ipos') st.markdown('---') with st.spinner('Loading stock charts...'): for t in reversed(df.Symbol): url = f'https://finviz.com/chart.ashx?t={t}&ty=c&ta=1&p=d&s=l' st.image(url, use_column_width=True) st.markdown('---') st.markdown('Visualization: https://finviz.com/') # Hide footer hide_footer_style = """ <style> .reportview-container .main footer {visibility: hidden;} """ # Hide hamburger menu st.markdown(hide_footer_style, unsafe_allow_html=True) hide_menu_style = """ <style> #MainMenu {visibility: hidden;} </style> """ st.markdown(hide_menu_style, unsafe_allow_html=True)
def main(): title = "DeepL Indent Shaper" st.beta_set_page_config(page_title=title) st.title(title) sentence_from = st.text_area("Input sentence", height=300) sentence_to = shape_sentence(sentence_from) st.markdown(""" ### Result ``` {res} ``` """.format(res=sentence_to)) st.markdown(""" <a href="{url}" style="font-size: 20px;" target="_blank"> <button style="color: #FFFFFF; background: #0D2036; border-radius: 5px; width: 100%; height: 40px;"> Translate in DeepL </button> </a> """.format(url=build_deepl_url(sentence_to, src="en", dst="ja")), unsafe_allow_html=True) st.markdown(""" --- ### Links * Twitter: [@morio_prog](https://twitter.com/morio_prog) * GitHub: [morioprog/deepl_indent_shaper](https://github.com/morioprog/deepl_indent_shaper) """, unsafe_allow_html=True)
def main(state=None): st.beta_set_page_config( page_title="MongoDB TimePass", layout="centered", initial_sidebar_state="auto", ) logo_uri = get_base_64_img( Path(__file__).parent / "assets" / "timepass-logo.png") st.sidebar.markdown(logo_uri, unsafe_allow_html=True) CONN_URI = st.sidebar.text_input("Connection URI") if CONN_URI == "": st.sidebar.info("Input a connection URI") st.stop() current_page = st.sidebar.radio("Go To", list(PAGE_MAP)) if state.db_client is None or state.CONN_URI != CONN_URI: # different sessions can have differnet DB Connections state.db_client = MongoDBClient(CONN_URI) state.CONN_URI = CONN_URI PAGE_MAP[current_page](state=state).write()
def main(): st.beta_set_page_config( page_title="PWP - Open Source", page_icon='https://user-images.githubusercontent.com/52009346/93438445-9c26e400-f8cd-11ea-9183-b6df80ddd318.png' ) st.image('https://user-images.githubusercontent.com/52009346/69100304-2eb3e800-0a5d-11ea-9a3a-8e502af2120b.png', use_column_width=True) selection = main_selection() if selection == 'Wellbore 3D': add_well_profile_app() if selection == 'Data Collector': add_petrodc_app() if selection == 'Temperature Distribution': add_pwptemp_app() if selection == 'Load Cases': add_pwploads_app() if selection == 'Torque & Drag': add_torque_drag_app() if selection in ['Temperature Distribution', 'Load Cases']: under_construction() if selection == 'Visualize Well Logs': add_well_logs_app() add_footer() add_side_bar()
def main(): # Define as configurações da página st.beta_set_page_config(page_title="Trabalho 1 - Sistemas Operacionais", ) st.title("Trabalho 1 - Escalonamento de Processos") st.write("---------------") st.write("**Disciplina:** Sistemas Operacionais") st.write("**Aluno:** Diego Santos Seabra") st.write("**Matrícula:** 0040251") st.write("---------------") visualizacao = st.selectbox(label='Escolha o que deseja visualizar', options=opcoes) st.write("---------------") if visualizacao == '1. Apresentação': apresentacao() if visualizacao == '2. Definições e Conceitos': definicoes_conceitos() if visualizacao == '3. Round Robin - Vantagens e Desvantagens': vd_round_robin() if visualizacao == '4. Round Robin - Demonstração': algo_round_robin() if visualizacao == '5. Menor Primeiro - Vantagens e Desvantagens': vd_menor_primeiro() if visualizacao == '6. Menor Primeiro - Demonstração': algo_menor_primeiro() if visualizacao == '7. Obrigado!': obrigado()
def main(): st.beta_set_page_config(page_title="SML Dashboard", page_icon=None, layout='wide', initial_sidebar_state='auto') st.title("Social Media Listening Dashboard") st.sidebar.title("Login page") st.markdown("This application is a Streamlit dashboard used " "for Social Media Listening") st.sidebar.markdown("Please select Login and enter username & password") st.header("Login Status") menu = ["Home", "Login"] choice = st.sidebar.selectbox("Menu", menu) if choice == "Home": st.subheader("Home") st.info( "To access the dashboard select the login option in the sidebar") elif choice == "Login": username = st.sidebar.text_input("User Name") password = st.sidebar.text_input("Password", type='password') if st.sidebar.checkbox("Login"): known_hash = user_df['Password'][user_df['Username'] == username].iloc[0] if pbkdf2_sha256.verify(password, known_hash): # the above line returns True or False. If True is returned, we show the main dashboard st.success("Logged in as {}".format(username)) labels = [ "People who like streamlit", "People who don't know about streamlit" ] values = [80, 20] # pull is given as a fraction of the pie radius fig = go.Figure(data=[ go.Pie( labels=labels, values=values, textinfo='label+percent', insidetextorientation='radial', pull=[0, 0, 0.2, 0] #,hole = 0.2 ) ]) fig.update_layout(title_text="Demo streamlit app") st.plotly_chart(fig, sharing='streamlit') else: st.warning("Incorrect Username/Password")
def header(): # Define as configurações da página st.beta_set_page_config(page_title="Trabalho 2 - Sistemas Operacionais", ) st.title("Trabalho 2 - Substituição de Páginas") st.write("---------------") st.write("**Disciplina:** Sistemas Operacionais") st.write("**Aluno:** Diego Santos Seabra") st.write("**Matrícula:** 0040251") st.write("---------------")
def main(): st.beta_set_page_config(page_title="SRT Logs Analyzer") st.markdown("<h1 style='text-align: center;'>SRT Log Analyzer</h1>", unsafe_allow_html=True) st.markdown("### Upload And Analyse CSV Log File") file_buffer = st.file_uploader("Choose a CSV Log File...", type="csv", encoding=None) if file_buffer: uploaded_file = io.TextIOWrapper(file_buffer) if uploaded_file is not None: df = pd.read_csv(uploaded_file) # Checking if the number of the columns is 30 if df.shape[1] != 30: st.warning( f"The uploaded CSV file is not properly formatted SRT Log File." ) st.warning( f"The uploaded file has only {df.shape[1]} columns instead of 30!" ) else: # Dropping the SocketID column, since it is not informative df.drop(["SocketID"], axis=1, inplace=True) # Checking if the log file is for sender or receiving device if df.byteSent.iloc[0] != 0: sender = True st.markdown("### SRT Sender Log:") else: sender = False st.write("### SRT Receiver Log:") # Removing redundant columns df, num_rows, num_cols = df_format(df, sender) min_rtt, max_rtt, avg_rtt = rtt_calc(df) # Printing some general stats of the line st.write(f"Number of Columns: {num_cols}") st.write(f"Number of Rows: {num_rows}") st.write(f"Log Duration: {df.Time.iloc[-1]}") st.write(f"Defined Latency: {df.RCVLATENCYms.iloc[-1]} ms") st.write(f"Minimal RTT: {min_rtt} ms") st.write(f"Maximal RTT: {max_rtt} ms") st.write(f"Average RTT: {avg_rtt} ms") # Generating the Drop-Down Menu with the Different Analysis drop_down_menu(df, sender)
def css_widget(file_name, logo_file_name, page_title='MyAPP', page_icon='🛠', layout='centered'): st.beta_set_page_config(page_title=page_title, page_icon=page_icon, layout=layout, initial_sidebar_state='auto') style = '<style>{}</style>'.format(load_file(file_name)) style = style.replace('LOGO_WIDE', encode_file(logo_file_name, 'image/png')) style_materials = "<style><link href='https://fonts.googleapis.com/icon?family=Material+Icons' rel='stylesheet'></style>" style_showcontrols = """<style>.toolbar, .instructions{ visibility: visible;display: block;}</style>""" st.markdown(style + style_materials + style_showcontrols, unsafe_allow_html=True)
def main(): # parse argument args = parse_args() if args.env == "local": base_dir = os.path.join(".") elif args.env == "heroku": base_dir = os.path.join("https://raw.githubusercontent.com", "saeeeeru", "Last-Row", "master") else: exit(9) # get image image_path = os.path.join(base_dir, "reports", "figure", "profile.JPG") md_path = os.path.join(base_dir, "scripts", "PROFILE.md") if args.env == "local": image = Image.open(image_path) with open(md_path, "r") as fi: profile_md = fi.read() else: profile_md = requests.get(md_path).content.decode(encoding="utf-8") image = requests.get(image_path).content # set layout st.beta_set_page_config( page_title="LiverpoolAnalyzer", page_icon=image, # layout="wide", initial_sidebar_state="expanded") # set sidebar mode, play = set_sidebar(base_dir, args) # instance class liverpool_analyzer = LiverpoolAnalyzer(mode, play, base_dir, args) if mode == "Annimation with Stretch Index": liverpool_analyzer.plot_pitch_control() elif mode == "Player Pitch Control Impact": liverpool_analyzer.plot_pitch_control() liverpool_analyzer.player_pitch_control_impact() elif mode == "Analysis Report": liverpool_analyzer.show_analysis_report() else: exit()
def main(): st.beta_set_page_config(page_title="DWD Stations") st.write("# DWD stations near IMK-IFU/ KIT 🏔🌦") data_resolution = select_data_resolution() max_station_distance = select_max_station_distance() observation_years = select_observation_years() closest_stations = find_close_stations( dist=max_station_distance, res=data_resolution ) filtered_stations = filter_by_dates(closest_stations, *observation_years) st.write(f"Number of stations: {len(filtered_stations)}") station_map = create_map(filtered_stations, tereno_stations, max_station_distance) folium_static(station_map)
def main(): """ function responsable for run streamlit app """ st.beta_set_page_config(page_title='Mann Kendall') st.title(body='Mann Kendall Solution') file_upload = st.sidebar.file_uploader(label="Upload Excel File", encoding=None, type=["xlsx", "xls"]) if file_upload: results, df = cache_generate_mann_kendall(file_upload) st.sidebar.markdown(get_table_download_link(results), unsafe_allow_html=True) plot_online(results, df)
def main(): """A calculator app with Streamlit components""" st.beta_set_page_config( page_title= "Fx", # String or None. Strings get appended with "• Streamlit". page_icon="📼", # String, anything supported by st.image, or None. layout= "centered", # Can be "centered" or "wide". In the future also "dashboard", etc. initial_sidebar_state="auto") # Can be "auto", "expanded", "collapsed" # ================== Using st.beta_columns ================== # col1, col2 = st.beta_columns([3, 1]) # first column 3x the size of second choice = "Simple" with col2: # Need to run selections first! choice = st.radio("", ["Simple", "Advanced"]) with col1: if choice == "Simple": st.header("📠Simple Calculator") html_component(path="html/simple_calc.html") elif choice == "Advanced": st.header("📺 Video Stream") html_component(path="html/webcam2.html", width=500, height=800) st.subheader("random dataframe") # ================== Mutate data with st.table() ================== # df1 = create_df(size=(1, 5)) my_table = st.table(df1) if st.button('add rows'): df2 = pd.DataFrame(np.random.randn(3, 5), columns=(f'col{i}' for i in range(5))) my_table.add_rows(df2)
def main(): st.beta_set_page_config( page_title= "Video capture", # String or None. Strings get appended with "• Streamlit". page_icon="📼", # String, anything supported by st.image, or None. layout= "centered", # Can be "centered" or "wide". In the future also "dashboard", etc. initial_sidebar_state="auto") # Can be "auto", "expanded", "collapsed" # ================== Using st.beta_columns ================== # col1, col2 = st.beta_columns([4, 1]) # first column 4x the size of second with col2: st.button("refresh") with col1: st.header("📺 Video Stream") st.text( 'Jeremy Ellis - Webcam capture on Codepen\nhttps://codepen.io/rocksetta/pen/BPbaxQ' ) st.text('Streamlit html component below') html_component(path="webcam2.html", width=600, height=600)
def main(): st.beta_set_page_config( page_title='Assessing the Readiness', page_icon='https://i.ibb.co/vxwPL94/image.png></a>', layout='wide') # Download external dependencies. # Create a text element and let the reader know the data is loading. data_load_state = st.text('Loading... It might takes a while') # Load 10,000 rows of data into the dataframe. data = load_data() # Notify the reader that the data was successfully loaded. data_load_state.text("") # Render the readme as markdown using st.markdown. # readme_text = st.markdown("Make sure it has the structure as seen below with the exact same column names" # ", same structure for scoring points, same structure for players that participated, and " # "make sure to use the same date format. Any changes to this structure will break the " # "application. ") # Once we have the dependencies, add a selector for the app mode on the sidebar. st.sidebar.title("Menu") option = st.sidebar.selectbox('Please select a page', ('Home', 'Problem Statistics', 'Content Statistics', 'User Statistics', 'User Activities', 'Check Proficiency')) if option == "Home": home.load(data) elif option == "Problem Statistics": # with st.spinner('Cleaning data...'): # data = data_preprocessing.clean(data) problem_statistics.load(data) elif option == "Content Statistics": content_statistics.load(data) elif option == "User Statistics": user_statistics.load(data) elif option == "User Activities": user_activities.load(data) elif option == "Check Proficiency": predictions.load(data)
)""" st.dataframe(DF_meals() .loc[dates] .pipe(select_level, nutrition_detail_levels[nutrition_detail_level]) [nutrition_information_kinds[nutrition_information_kind]] ) st.dataframe(DF_calisthenics() .loc[dates] ) st.plotly_chart( Plot_body(rolling_window), use_container_width=True, ) st.plotly_chart( Plot_nutrition(nutrition_information_kind, rolling_window), use_container_width=True, ) if __name__ == '__main__': st.beta_set_page_config( page_title='Lifestyle', page_icon=None, layout='wide', initial_sidebar_state='auto' ) main()
""" import base64 import datetime import os from math import ceil, floor from os import cpu_count from typing import List, Tuple import pandas as pd import streamlit as st from PIL import Image from get_azure_data import calculate_price, get_table st.beta_set_page_config(layout="wide") st.title("ACT Now! Estimated Azure Costing Tool for Bonsai Experiments") pd.set_option("display.float_format", lambda x: "%.3f" % x) @st.cache def load_image(img): im = Image.open(os.path.join(img)) return im st.image(load_image("imgs/bonsai-logo.png"), width=70) st.markdown( """_This is a simple calculator for running Azure Batch Jobs with [`batch-orchestration`](https://github.com/BonsaiAI/batch-orchestration)._ It relies on the public pricing information available on [azureprice.net](https://azureprice.net/)."""
import streamlit as st # <3 import pickle from sklearn.feature_extraction.text import CountVectorizer # processamento de textos # Lendo os picles que tirei do hambúrguer model = pickle.load(open('model_clf.pkl', 'rb')) count_vect = pickle.load(open('count_vect.pkl', 'rb')) # Configurações gerais da página st.beta_set_page_config( page_title="EM | Previsão de Categoria de Ebooks Infantis", page_icon="📚", # polimento e chiquezas layout="centered", initial_sidebar_state="expanded", ) # Funções para formatação html (arquivo .css) def local_css(file_name): with open(file_name) as f: st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True ) # último parâmetro: permite formatação html def remote_css(url): st.markdown(f'<link href="{url}" rel="stylesheet">', unsafe_allow_html=True) #local_css("style.css") #remote_css('https://fonts.googleapis.com/icon?family=Material+Icons')
import numpy as np import pandas as pd import streamlit as st import matplotlib.pyplot as plt import seaborn as sns sns.set_style("white") import adjustText from adjustText import adjust_text from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA import gseapy as gp from gseapy.plot import gseaplot, heatmap import matplotlib.gridspec as gridspec st.beta_set_page_config(layout="centered") initial_sidebar_state = "expanded" def _max_width_(): max_width_str = f"max-width: 1300px;" st.markdown( f""" <style> .reportview-container .main .block-container{{ {max_width_str} }} </style> """, unsafe_allow_html=True, )
def main(): st.beta_set_page_config(page_title='Lights Out!', page_icon="💡", initial_sidebar_state='collapsed') update_style() st.markdown("""<h1 style="text-align:center">Lights Out!</h1>""", unsafe_allow_html=True) st.markdown(""" <p style="text-align:justify">Street lighting comprises of 14.8% (9,935 GJ) of total energy used by the City of Hobart, and is a major contributor to electrical and greenhouse gas usage in all cities. The City of Hobart spends approximately $1.5 - $2 million dollars a year and would like to investigate strategies to reduce their energy costs and light pollution.</p> <p style="text-align:justify">There are over 5,000 street lights in the City of Hobart, where only 300 are managed by the city. The rest of the lights are managed by TasNetworks and are unmetered. This means regardless if the light is on, off, or dimmed, the council pays the full 10 hours a day.</p> <p style="text-align:justify">We wanted to investigate 4 main ideas:</p> <li>Which bulbs are the most effective, and is it cost-effective to swap bulbs?</li> <li>Is it worth metering individual poles to reduce electricity costs from dimming with and without sensors?</li> <li>Are solar poles worth the cost?</li> <li>Can we make use of IoT and wireless technologies to make our community more sustainable?</li> <p style="text-align:justify">To investigate this, we looked into street light data set from the City of Hobart, data on historical energy usage and foot traffic in the CBD, as well as potentially intergrate street into a Smart City.</p> """, unsafe_allow_html=True) LIGHT_FILE = 'light.csv' light = pd.read_csv(LIGHT_FILE) st.markdown( """<h2 style="text-align:center">Idea 1: Phasing out old MV lamps</h2>""", unsafe_allow_html=True) st.write(""" <p style="text-align:justify">We wanted to evaluate each type of bulb for their operational cost over 25,000 hours by standardising their wattage to 1700 lumens. Assuming an electrical cost of $0.4/kWh, we can see the LED and HPS bulbs are twice as efficient as MV and CFL bulbs. MV and CFL bulbs are the prime candida tes for replacement given their wattage to lumens inefficiency.</p> <p style="text-align:justify">Assuming the fixed cost of changing a bulb to a different type is $700 and that we hope to recoup our expenses in 5 years, we would need a wattage difference of 96 W to justify swapping to a more efficient LED bulb.</p> """, unsafe_allow_html=True) st.plotly_chart(plot_cost(), use_container_width=True) st.header('Lighting Map') st.write(""" <p style="text-align:justify">We visualised each street light in Hobart to identify trends. We noticed that high wattage HPS and WV lights were used on highways and major roads, with LED and CFL mainly used on minor roads in residential areas.</p> <p style="text-align:justify">We recommend swapping out 400 W MV to 250 W LED bulbs on major roads.</p> <p style="text-align:justify">We recommend swapping out 125 W and 150 W MV to 18W LED bulbs on minor roads.</p> <p style="text-align:justify">Possible MV to LED swap outs have been located in the map below.</p> """, unsafe_allow_html=True) st.plotly_chart(plot_lighting_map(light), use_container_width=True) st.markdown( 'Below you can select which lamp type to view its distribution in Hobart:' ) lamp_type = st.selectbox('', ['MV', 'CFL', 'LED', 'HPS']) lamp_type_desc_dict = { 'MV': """ <p style="text-align:justify">MV bulbs are the least efficient and we recommend swapping them when applicable.</p> <p style="text-align:justify">We recommend swapping 400 W MV to 250 W LED bulbs on major roads. This is assuming that 250 W LED lights have the equivalent luminosity to 250 W HPS lights. This has the potential saving of $219 in electrical costs per annum, and there are 155 possible replacements.</p> <p style="text-align:justify">We recommend swapping out 125W and 150 W MV to 18 W LED bulbs on minor roads.</p> """, 'CFL': """ <p style="text-align:justify">Due to the low wattage of CFL bulbs, there are no gains from swapping them out into LEDs.</p> """, 'LED': """ <p style="text-align:justify">LED lights are our lights of choice due to efficiency, life-span and ability to be dimmed. They are the most used lights in smart cities and is the recommended light for future-proofing. The light is directed to the ground with minimal light pollution. For these reasons we recommend swapping into LED lamps.</p> """, 'HPS': """ <p style="text-align:justify">The efficiency of HPS and LED lamps are similar, there is no cost-benefit of swapping them to LEDs. Dimming is not ideal for HPS bulbs as they take time to reach maximum luminosity and it may reduce their life-span.</p> """ } st.markdown(lamp_type_desc_dict[lamp_type], unsafe_allow_html=True) st.plotly_chart(plot_lamp_hist(light, lamp_type), use_container_width=True) st.markdown( """<h2 style="text-align:center">Idea 2: Metering, dimming and sensors</h2>""", unsafe_allow_html=True) st.markdown(""" <p style="text-align:justify">For the council to see monetary benefit from dimming with or without sensors, individual metering would need to be installed on each light pole. We have focused our attention on dimming in residential areas, as they are most likely the areas with the most downtime at night. We have chosen not to consider dimming on highways and major roads due to safety reasons.</p> <p style="text-align:justify">Dimming already efficient 18 W LED lights in residential areas have minuscule gains of 3 hours saved per night. This is an annual saving of $8 per meter installed per year. Given an expensive upfront meter box installation cost, we cannot recommend dimming with or without sensors on a purely financial level. However, dimming can reduce greenhouse gas emissions, improve lightbulb lifespan and reduce light pollution.</p> """, unsafe_allow_html=True) st.markdown( """<h2 style="text-align:center">Idea 3: Adapting to solar</h2>""", unsafe_allow_html=True) st.markdown(""" <p style="text-align:justify">Solar poles are powered by sun, a renewable energy source.</p> <p style="text-align:justify">We wanted to investigate and weigh up the benefits and costs of installing solar poles as a permanent electrical solution.</p> <p style="text-align:justify">Advantages:</p> <li>Solar poles take street lights off the grid permanently, which means permanent electricity savings from usage per kilowatt hour as well as network charges</li> <li>The lights are invulnerable to electricity outages, which provides added safety to the city</li> <li>Solar poles have an average lifespan of 20 years and have low maintenance costs associated with it</li> <li>Some solar poles also have a sensor option available, which can reduce light pollution in areas that are deserted at night</li> <p style="text-align:justify">Disadvantages:</p> <li>High upfront cost</li> <li>Solar energy is dependant on weather factors such as UV index, cloud coverage and daylight hours</li> <p style="text-align:justify">Our recommendation plan is a 4 phase solar pole rollout for the City of Hobart. We looked at major foot traffic areas to help determine which locations would provide the greatest social benefit to help attract people towards the city centre. We also made sure each phase was within the City of Hobart's annual street light budget, making this a realistic plan. Using this information, we created a visualization that shows the streets involved during each phase and the annual savings from reduced energy and network costs.</p> """, unsafe_allow_html=True) year_dict = { 'Year 1': [1], 'Year 2': [2], 'Year 3': [3], 'Year 4': [4], 'All Years': [1, 2, 3, 4], } st.markdown(""" <p style="text-align:justify">In the drop down box you can select the phases to see the spread of solar poles in the city, as well as the annual savings per pole.</p> """, unsafe_allow_html=True) year_select = st.selectbox('', list(year_dict.keys())) solar_df = pd.read_csv('./solar_pole_table.csv') st.plotly_chart(plot_solar(solar_df, year_select), use_container_width=True) solar_df = solar_df.drop(['Longitude', 'Latitude'], axis=1)[solar_df['Implementation year'].isin( year_dict[year_select])] solar_df = df_float_formatter(solar_df, formatter="{:.2f}") solar_df st.markdown( """<h2 style="text-align:center">Idea 4: Embracing a smart future</h2>""", unsafe_allow_html=True) st.markdown(""" <p style="text-align:justify">We know cities are constantly on the lookout for smart solutions to enhance city vibrancy and promote local businesses by attracting local and overseas tourists. With this in mind, we propose building on the sustainable solar poles and adding in street friendly features such as a solar charging station for small devices.<br> We also believe we can harness the power of IoT by connecting each solar pole to a cloud. Councils are able to utilise these wireless connections by feeding in inputs such as weather forecasts, UV index, cloud coverage. This allows the council to adjust the dimming to accommodate for special public events such as New Years, which may draw large crowds out into the city late at night. This flexibility provides the citizens with safe lighting, as well as meeting the council's energy and sustainability requirements.</p> """, unsafe_allow_html=True) smart_city = Image.open(os.path.join('asset', 'smart_city.png')) st.image(smart_city, caption='How poles can be intergrated into Smart City', use_column_width=True) st.header('About') st.markdown(""" <p style="text-align:justify"> This web app is part of a submission for GovHack 2020 Hackathon, made by <a href="https://www.linkedin.com/in/arnab-mukherjee-data/">Arnab Mukherjee</a>, <a href="https://www.linkedin.com/in/emily-shen/">Emily Shen</a>, <a href="https://www.linkedin.com/in/hengwang322/">Heng Wang</a>, and <a href="https://www.linkedin.com/in/alfred-zou/">Alfred Zou</a>. <br> Feel free to check out our <a href="https://hackerspace.govhack.org/projects/lights_out">project page</a> and <a href="https://github.com/hengwang322/lights_out">the repository</a>! </p>""", unsafe_allow_html=True)
import streamlit as st import random import sys titles = ["Hello", "Hi", "Howdy"] icons = [":shark:", ":cat:", ":hamburger:", ":bomb:"] st.beta_set_page_config( page_title=random.choice(titles), page_icon=random.choice(icons), ) """ # Hello world! """ x = st.slider("Foo", 0, 100) st.write("Selected:", x) print("Selected:", x) sys.stdout.flush()
import pandas as pd import yfinance as yf from datetime import datetime, date import h2o from h2o.automl import H2OAutoML #import matplotlib.pyplot as plt import streamlit as st import plotly.express as px #h2o.init() st.beta_set_page_config(page_title="StockFit", page_icon="📈", layout='wide') st.title("StockFit") st.sidebar.subheader("Stock Settings:") ticker = st.sidebar.text_input('Ticker', value="AAPL") date_start = st.sidebar.date_input("Start", value=datetime(2016, 1, 1)) date_end = st.sidebar.date_input("End", value=datetime.today(), max_value=datetime.today()) st.sidebar.subheader("Model Settings:") tick = yf.Ticker(ticker) df = tick.history(start=date_start, end=date_end) try: if df.shape[0] > 0: try: st.header(tick.info["shortName"]) st.image(tick.info['logo_url']) except: st.header(ticker.upper()) chart = px.line(df, y="Close") chart.update_layout(title=f"{ticker.upper()} Stock Close Price Chart",
import os import streamlit as st import pandas as pd from pathlib import Path st.beta_set_page_config(page_title='MBOT', page_icon="🚀", layout='centered', initial_sidebar_state='collapsed') st.title('MBot Setup') ''' Michigan Bot *(MBOT)* is a small mobile robot used with the Graduate Robotics Systems Laboratory course at University of Michigan. Below is my intitial work completed on the robot.\n ***Please forgive the cheesy music in my videos 😊***\n ## **Assembly** *Timelapse of robot assembly.* ''' # assembly timelapse st.video('https://www.youtube.com/watch?v=HLtRFjogLS4') ''' ## **Simple Driving** *MBOT driving around using the teleop_simple program.* ''' # teleop video st.video('https://www.youtube.com/watch?v=lfkwy5WPypM') '''
import streamlit as st import tensorflow.keras from PIL import Image, ImageOps import numpy as np import time st.beta_set_page_config( page_title="Auto Vaidya", layout="centered", initial_sidebar_state="collapsed", ) # Just making sure we are not bothered by File Encoding warnings st.set_option('deprecation.showfileUploaderEncoding', False) def main(): menu = ['Home', 'Contact'] choice = st.sidebar.selectbox("Menu", menu) if choice == "Home": # Let's set the title of our awesome web app st.title('Auto Vaidya') # Now setting up a header text st.subheader("Automating Healthcare one problem at a time") def your_image_classifier(image): ''' Function that takes the path of the image as input and returns the closest predicted label as output ''' # Disable scientific notation for clarity
from pytube import YouTube, Stream import streamlit as st import time import streamlit.components.v1 as components import webbrowser import os from pathlib import Path ###################################### Code ########################################## st.beta_set_page_config(page_title='Youtube Video Downloader', page_icon='▶', layout='centered', initial_sidebar_state='collapsed') def local_css(filename): with open('style.css') as f: st.markdown('<style>{}</style>'.format(f.read()), unsafe_allow_html=True) local_css("style.css") path_to_download_folder = str(os.path.join(Path.home(), "Downloads")) st.markdown( "<h1 style='text-align: center;'><div><span class='highlight blue'><span style='cursor: default'>Youtube Video Downloader No ADS!!</h1>", unsafe_allow_html=True) try:
#panda is the datareader from web import pandas as pd import pandas_datareader.data as web import requests import plotly.figure_factory as ff import plotly.graph_objects as go from plotly.subplots import make_subplots import plotly.express as px import numpy as np from PIL import Image ###From her set General Settings like App/Tab Icon and Name st.beta_set_page_config(page_title='CLUE Stock Analyzing App', page_icon='logo.jpg') ###From her import Stock List @st.cache def stock_list(stock_list): df = pd.read_excel('output_2.xlsx', index_col=0) # df.drop(['Change in %', 'Last Price', 'Name.1', 'Letzter Preis', 'Änderung'], inplace=True, axis=1) df_li = df.dropna() return df_li df_li = stock_list(stock_list) ### From her Stock List Overview and day price and change
import joblib, os import numpy as np from PIL import Image import requests import urllib import datetime, time WIDTH = 300 HEIGHT = 300 IMG_PATH = "imgs" URI = 'https://raw.githubusercontent.com/Chloejay/image_caption_app/master/app/' st.set_option("deprecation.showfileUploaderEncoding", False) st.beta_set_page_config( page_title="Image translation app", page_icon="", layout="wide", initial_sidebar_state="expanded", ) @st.cache(show_spinner=False) def get_file_content_as_string(app_file_path): url = URI + app_file_path response = urllib.request.urlopen(url) return response.read().decode("utf-8") def run_the_app(): st.markdown("## `#TODO`")
'(https://requests.readthedocs.io/en/master/)', '(https://pandas.pydata.org/)', '(https://pandas.pydata.org/docs/)', '(https://www.streamlit.io/)', '(https://docs.streamlit.io/en/stable/)', '(https://plotly.com/)', '(https://plotly.com/python/)') import pandas as pd import streamlit as st import plotly.express as px import requests from io import BytesIO from pyvalet import ValetInterpreter from custom import download_link, load_time_series # ------------------------------------------------------------------------------- st.beta_set_page_config( # Configure page title, icon, layout, and sidebar state page_title='Bank of Canada Open Data Explorer', page_icon=':bank:', layout='wide', initial_sidebar_state='expanded') # Retrieve and display the Bank of Canada's logo on the top of the sidebar boc_logo = requests.get('https://logos-download.com/wp-content/uploads/' '2016/03/Bank_of_Canada_logo.png') boc_logo = BytesIO(boc_logo.content) st.sidebar.image(boc_logo, use_column_width=True) # Define the web application title st.title(':bank: Bank of Canada :maple_leaf: [Open Data]' '(https://github.com/tylercroberts/pyvalet) Explorer :mag:') # ------------------------------------------------------------------------------- # Introductory expander section covering overall application details with st.beta_expander(label='Application details', expanded=True):
import pandas as pd import numpy as np import io from io import BytesIO, StringIO import streamlit as st from VizSerie import VizSerie from Model import Model st.beta_set_page_config( page_title="Magento 1 - Analise de relatório de vendas") def viz(data): data_viz = VizSerie(data) """ ## Como estão minhas vendas ao longo do tempo? *Você pode utilizar os filtros abaixo para vizualizar do tempo determinado até o atual* """ data_viz.simplePlotSeries() """ ## Vendas em diferentes series temporais """ data_viz.plotSubSales() """ ## Qual a minha média de vendas nos dias da semana? """ data_viz.plotSalesByWeekDays() """ ## Médias de vendas ao longo das semanas do mês?
import streamlit as st import os import io import json import pandas as pd import numpy as np import folium from folium import plugins from streamlit_folium import folium_static # SetUp for website st.beta_set_page_config( page_title="El Rugido en MX" ) def local_css(file_name): with io.open(file_name) as f: st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True) local_css("style.css") st.title("¿Dónde rugen los Tigres (6652)") # Some basic description of the project st.header("¿Cómo funciona?") st.markdown(""" Los miembros de **Tigres 6652** han llenado un [formulario](https://forms.gle/ozU7gcf7KhAXt8mA6), espero, con el que pudimos localizar sus puntos en el mapa y cuando tu cargas esta página, se revisa automáticamente que estén todos los puntos actualizados o si hay nuevas respuestas.