Plotly-Dash:如何从 Dash WebApp 中删除状态图标?
Plotly-Dash: How to remove the status icon from Dash WebApp?
我正在使用 Dash plotly 为 IPL 构建 Web 仪表板。那里有一个显示网络服务器状态的状态图标。
如何从网页中删除它?我无法解释太多所以添加下面用黄色标记的图片
只需在此处设置 Debug=False
即可:
app.run_server(debug=False)
我会用一个来说明。您将在下面找到完整的代码片段。
1 - app.run_server(debug=True)
2 - app.run_server(debug=False)
完整代码
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from jupyter_dash import JupyterDash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import make_pipeline
from IPython.core.debugger import set_trace
# Load Data
df = px.data.tips()
# Build App
app = JupyterDash(__name__)
app.layout = html.Div([
html.H1("ScikitLearn: Polynomial features"),
dcc.Graph(id='graph'),
html.Label([
"Set number of features",
dcc.Slider(id='PolyFeat',
min=1,
max=6,
marks={i: '{}'.format(i) for i in range(10)},
value=1,
)
]),
])
# Define callback to update graph
@app.callback(
Output('graph', 'figure'),
[Input("PolyFeat", "value")]
)
def update_figure(nFeatures):
global model
# data
df = px.data.tips()
x=df['total_bill']
y=df['tip']
# model
model = make_pipeline(PolynomialFeatures(nFeatures), LinearRegression())
model.fit(np.array(x).reshape(-1, 1), y)
x_reg = x.values
y_reg = model.predict(x_reg.reshape(-1, 1))
df['model']=y_reg
# figure setup and trace for observations
fig = go.Figure()
fig.add_traces(go.Scatter(x=df['total_bill'], y=df['tip'], mode='markers', name = 'observations'))
# trace for polynomial model
df=df.sort_values(by=['model'])
fig.add_traces(go.Scatter(x=df['total_bill'], y=df['model'], mode='lines', name = 'model'))
# figure layout adjustments
fig.update_layout(yaxis=dict(range=[0,12]))
fig.update_layout(xaxis=dict(range=[0,60]))
#print(df['model'].tail())
fig.update_layout(template = 'plotly_dark')
return(fig)
# Run app and display result inline in the notebook
app.enable_dev_tools(dev_tools_hot_reload =True)
app.run_server(mode='inline', port = 8040, dev_tools_ui=True, debug=False,
dev_tools_hot_reload =True, threaded=True)
我正在使用 Dash plotly 为 IPL 构建 Web 仪表板。那里有一个显示网络服务器状态的状态图标。
如何从网页中删除它?我无法解释太多所以添加下面用黄色标记的图片
只需在此处设置 Debug=False
即可:
app.run_server(debug=False)
我会用一个
1 - app.run_server(debug=True)
2 - app.run_server(debug=False)
完整代码
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from jupyter_dash import JupyterDash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import make_pipeline
from IPython.core.debugger import set_trace
# Load Data
df = px.data.tips()
# Build App
app = JupyterDash(__name__)
app.layout = html.Div([
html.H1("ScikitLearn: Polynomial features"),
dcc.Graph(id='graph'),
html.Label([
"Set number of features",
dcc.Slider(id='PolyFeat',
min=1,
max=6,
marks={i: '{}'.format(i) for i in range(10)},
value=1,
)
]),
])
# Define callback to update graph
@app.callback(
Output('graph', 'figure'),
[Input("PolyFeat", "value")]
)
def update_figure(nFeatures):
global model
# data
df = px.data.tips()
x=df['total_bill']
y=df['tip']
# model
model = make_pipeline(PolynomialFeatures(nFeatures), LinearRegression())
model.fit(np.array(x).reshape(-1, 1), y)
x_reg = x.values
y_reg = model.predict(x_reg.reshape(-1, 1))
df['model']=y_reg
# figure setup and trace for observations
fig = go.Figure()
fig.add_traces(go.Scatter(x=df['total_bill'], y=df['tip'], mode='markers', name = 'observations'))
# trace for polynomial model
df=df.sort_values(by=['model'])
fig.add_traces(go.Scatter(x=df['total_bill'], y=df['model'], mode='lines', name = 'model'))
# figure layout adjustments
fig.update_layout(yaxis=dict(range=[0,12]))
fig.update_layout(xaxis=dict(range=[0,60]))
#print(df['model'].tail())
fig.update_layout(template = 'plotly_dark')
return(fig)
# Run app and display result inline in the notebook
app.enable_dev_tools(dev_tools_hot_reload =True)
app.run_server(mode='inline', port = 8040, dev_tools_ui=True, debug=False,
dev_tools_hot_reload =True, threaded=True)