作者： [email protected]

https://cowtransfer.com/s/1b9875c3c3fa40

Mikebrady/shairport-sync：AirPlay 和 AirPlay 2 音频播放器 (github.com)

如果树莓派没有连接显示器，可能会出现添加了启动文件后，开机不会自动启动的问题，此时需要修改/boot/config.txt文件，

sudo nano /boot/config.txt


找到hdmi_force_hotplug=1这一行，把前面的#号删除掉

创建一个服务文件：打开一个文本编辑器，创建一个新的服务文件，比如streamlit.service。
编辑服务文件：在服务文件中，输入以下内容：

[Unit]
Description=Streamlit App

[Service]
ExecStart=/home/pi/.local/bin/streamlit run /home/pi/下载/my_login_main.py
WorkingDirectory=/home/pi/下载
Restart=always
User=pi

[Install]
WantedBy=multi-user.target

请确保将/path/to/your/streamlit/app替换为你实际的Streamlit应用程序所在的路径，将your_app.py替换为你的应用程序文件名。将<your_username>替换为你的用户名。
保存服务文件：保存服务文件，并将其命名为streamlit.service。
将服务文件移动到系统服务目录：打开终端，并使用以下命令将服务文件移动到系统服务目录：

sudo mv streamlit.service /etc/systemd/system/
启用和启动服务：在终端中使用以下命令启用和启动服务：

sudo systemctl enable streamlit.service
sudo systemctl start streamlit.service
验证服务状态：使用以下命令验证服务是否正在运行：

sudo systemctl status streamlit.service
如果服务正在运行，你应该能够看到"active (running)"的状态。
请注意，这些步骤适用于基于systemd的Linux发行版，如Ubuntu。

准备工作

pip install streamlit-authenticator

创建 YAML 文件：config.yaml

credentials:
  usernames:
    liba001:
      email: [email protected]
      name: John Smith
      password: abc # To be replaced with hashed password
    liba002:
      email: [email protected]
      name: Rebecca Briggs
      password: def # To be replaced with hashed password
cookie:
  expiry_days: 365
  key: random_signature_key # Must be string
  name: random_cookie_name
preauthorized:
  emails:
  - [email protected]

使用 Hasher 模块将纯文本密码转换为哈希密码：

import streamlit_authenticator as stauth
hashed_passwords = stauth.Hasher(['abc', 'def']).generate()
print(hashed_passwords)

将 YAML 文件中的纯文本密码替换为生成的哈希密码

开始主程序

import yaml
from yaml.loader import SafeLoader
with open('config.yaml') as file:
    config = yaml.load(file, Loader=SafeLoader)

authenticator = Authenticate(
    config['credentials'],
    config['cookie']['name'],
    config['cookie']['key'],
    config['cookie']['expiry_days'],
    config['preauthorized']
)
name, authentication_status, username = authenticator.login('Login', 'main')

if authentication_status:
    authenticator.logout('Logout', 'main')
    st.write(f'Welcome *{name}*')
    st.title('Some content')
elif authentication_status == False:
    st.error('Username/password is incorrect')
elif authentication_status == None:
    st.warning('Please enter your username and password')

Data elements – Streamlit Docs

import time  # to simulate a real time data, time loop

import numpy as np  # np mean, np random
import pandas as pd  # read csv, df manipulation
import plotly.express as px  # interactive charts
import streamlit as st  # 🎈 data web app development

st.set_page_config(
    page_title="Real-Time Data Science Dashboard",
    page_icon="✅",
    layout="wide",
)

# read csv from a github repo
dataset_url = "https://raw.githubusercontent.com/Lexie88rus/bank-marketing-analysis/master/bank.csv"

# read csv from a URL
@st.experimental_memo
def get_data() -> pd.DataFrame:
    return pd.read_csv(dataset_url)

df = get_data()

# dashboard title
st.title("Real-Time / Live Data Science Dashboard")

# top-level filters
job_filter = st.selectbox("Select the Job", pd.unique(df["job"]))

# creating a single-element container
placeholder = st.empty()

# dataframe filter
df = df[df["job"] == job_filter]

# near real-time / live feed simulation
for seconds in range(200):

    df["age_new"] = df["age"] * np.random.choice(range(1, 5))
    df["balance_new"] = df["balance"] * np.random.choice(range(1, 5))

    # creating KPIs
    avg_age = np.mean(df["age_new"])

    count_married = int(
        df[(df["marital"] == "married")]["marital"].count()
        + np.random.choice(range(1, 30))
    )

    balance = np.mean(df["balance_new"])

    with placeholder.container():

        # create three columns
        kpi1, kpi2, kpi3 = st.columns(3)

        # fill in those three columns with respective metrics or KPIs
        kpi1.metric(
            label="Age ⏳",
            value=round(avg_age),
            delta=round(avg_age) - 10,
        )
        
        kpi2.metric(
            label="Married Count 💍",
            value=int(count_married),
            delta=-10 + count_married,
        )
        
        kpi3.metric(
            label="A/C Balance ＄",
            value=f"$ {round(balance,2)} ",
            delta=-round(balance / count_married) * 100,
        )

        # create two columns for charts
        fig_col1, fig_col2 = st.columns(2)
        with fig_col1:
            st.markdown("### First Chart")
            fig = px.density_heatmap(
                data_frame=df, y="age_new", x="marital"
            )
            st.write(fig)
            
        with fig_col2:
            st.markdown("### Second Chart")
            fig2 = px.histogram(data_frame=df, x="age_new")
            st.write(fig2)

        st.markdown("### Detailed Data View")
        st.dataframe(df)
        time.sleep(1)

Ubuntu开机启动gpt4free的streamlit网页服务_skywalk8163的博客-CSDN博客