def get_layout(UPDATE_INT, config=None):
app_layout = html.Div([
html.Div([
dcc.Interval(id='interval_component',
interval=UPDATE_INT * 1000,
n_intervals=0),
dcc.Interval(
id='psutil_component', interval=2 * 1000, n_intervals=0)
],
style=dict(textAlign='center')),
html.Div([
daq.Gauge(id='virtual_memory',
min=0,
value=0,
size=150,
className="leftbox",
style=dict(textAlign="center")),
daq.Gauge(id='swap_memory',
min=0,
value=0,
size=150,
className="rightbox",
style=dict(textAlign="center")),
]),
daq.LEDDisplay(id='train-id',
value=1000,
color="#FF5E5E",
style=dict(textAlign="center")),
html.Br(),
html.Div(children=[
dcc.Tabs(
parent_className='custom-tabs',
className='custom-tabs-container',
id='view-tabs',
value='plot',
children=[
dcc.Tab(className="custom-tab",
selected_className='custom-tab--selected',
label='Stream/Load data',
value='stream-data',
children=get_stream_tab(config)),
# dcc.Tab(
# className="custom-tab",
# selected_className='custom-tab--selected',
# label='Experimental parameters',
# value='exp-param',
# children=get_exp_tab()
# ),
dcc.Tab(className="custom-tab",
selected_className='custom-tab--selected',
label='Plots',
value='plot',
children=get_plot_tab(config))
])
])
])
return app_layout
def get_my_carbon_food_print(country, age_group, *args):
newDict = {}
i = 0
for category in food_categories:
newDict[category] = {}
for item in all_keys[category]:
newDict[category][item] = args[i]
i += 1
my_carbon_food_print = helper_data.calculate_my_carbon_food_print(newDict)
country_food_print = helper_data.get_carbon_food_print_for_country(
country, age_group)
return html.Div([
html.Div([
daq.Gauge(id='my-gauge',
label='Your weekly CO2 food print',
max=100000,
value=my_carbon_food_print,
min=0)
],
className="six columns"),
html.Div([
daq.Gauge(id='country-gauge',
label=country + ' ' + age_group.lower() +
' weekly CO2 food print',
max=100000,
value=country_food_print,
min=0)
],
className="six columns")
],
className="row")
def percentfig(pipe_df, ref_df):
# Number of Evaluated Algorithms
#evalgo = len(pipe_df['ALGO_NAME'].unique())
evalgo = len(pipe_df['ALGO_NAME'][pipe_df['ALGO_NAME'].str.contains(
'_stack') == False][pipe_df['PP_FLAG'] == 'N'].unique())
# Hyperparameters
evalhyper = len(pipe_df['HYPER_NAME'].unique())
# Total Algo Space
total_algo = len(ref_df['ALGO_NAME'].unique())
print(total_algo)
# Total Hyper Space
total_hyper = ref_df['TOTAL_HYPER_COUNT'].groupby(
ref_df['ALGO_NAME']).count().sum()
pct_algos = (evalgo / total_algo) * 100
pct_hypers = (evalhyper / total_hyper) * 100
pfig1 = html.Div([
daq.Gauge(id='my-gauge',
color={
"gradient": True,
"ranges": {
"green": [60, 100],
"yellow": [40, 59],
"red": [0, 39]
}
},
label="% Algorithms",
size=150,
value=round(pct_algos, 0),
min=0,
max=100)
])
pfig2 = html.Div([
daq.Gauge(id='my-gauge-1',
color={
"gradient": True,
"ranges": {
"green": [0, 6],
"yellow": [6, 8],
"red": [8, 10]
}
},
label="% Hyperparameters",
size=150,
value=round(pct_hypers, 0),
min=0,
max=100)
])
return pfig1, pfig2
def model_perf(value):
datasets = json.loads(value)
df = pd.read_json(datasets['df_model_performance'], orient='split')
df['Value'] = round(df['Value'], 3)
return html.Div([
html.H1('Model Performance'),
dbc.Row([
dbc.Col(daq.LEDDisplay(
label='AUC', color="#FF5E5E", value=str(df['Value'][0])),
md=2),
dbc.Col(daq.LEDDisplay(
label='GINI', color="#FF5E5E", value=str(df['Value'][1])),
md=2),
dbc.Col(daq.LEDDisplay(label='Kolmogorov-Smirnov',
color="#FF5E5E",
value=str(df['Value'][2])),
md=2),
dbc.Col(daq.Gauge(color={
"gradient": True,
"ranges": {
"red": [0, 40],
"yellow": [40, 70],
"green": [70, 100]
}
},
value=df['Value'][0] * 100,
label='Overall Model Quality',
max=100,
min=0),
md=4),
],
align="center"),
]),
def predict(Platform, Genre):
row = pd.DataFrame(
columns=['Platform', 'Genre'],
data=[[Platform, Genre]]
)
y_pred = round(model.predict(row)[0] * 100, 2)
label = f'Sales prediction chances are likely {y_pred}%'
output = daq.Gauge(
id='Prediction-Sales',
label=label,
color="3346FF",
showCurrentValue=True,
value=y_pred,
min=0,
max=200,
units="%")
print(f'{label}')
return output
def make_card(name, avg):
link = '/' + name
return dbc.Card(
[
dbc.CardBody([
html.Div([
html.H4(name, className="card-title"),
daq.Gauge(id=name + '_gauge',
label="Mood in past week",
value=avg,
max=10,
min=0,
showCurrentValue=True,
color={
"gradient": True,
"ranges": {
"red": [0, 4],
"yellow": [4, 7],
"green": [7, 10]
}
}),
dbc.Button("Details", href=link, color="primary"),
],
className="container text-center")
]),
],
style={"width": "18rem"},
)
def progress_gauge(current,total):
logging.debug("progress gauge: {} of {}".format(current,total))
try:
per_cent = round(100*(current/total))
except ZeroDivisionError:
per_cent = 0
# some "total" numbers are estimates, so could go over 100%
if per_cent > 100:
per_cent = 100
elements = [
daq.Gauge( value = per_cent,
max = 100,
min = 0,
size = 130,
color = {"gradient" : True,
"ranges" : {"red" : [0, 25],
"yellow" : [25, 75],
"green" : [75, 100],
},
},
label = {'label' : '%',
'style' : {'font-size': 'larger'}
},
labelPosition = 'bottom',
),
]
return elements
def create_quick_overview_tab(id_gauges, id_graphs, n_clicks, data):
if n_clicks % 2 == 1:
button = 'The live graphs are currently being updated.'
graphs = []
for id_graph in id_graphs:
sensor_graph = find_sensor_given_id(id_graph)
graph = create_graph(sensor_graph, data)
if sensor_graph:
graphs.append(graph)
else:
button = 'The live graphs are currently paused.'
graphs = no_update
gauges = []
for id_gauge in id_gauges:
sensor_gauge = find_sensor_given_id(id_gauge)
if sensor_gauge:
maxVal = sensor_gauge['max_value']
minVal = sensor_gauge['min_value']
gauge_unit = sensor_gauge['units']
gauge_label = sensor_gauge['label']
if sensor_gauge['id'] == 'AVG_VSS':
gauge_value = (data['FL_VSS'][len(data) - 1] + data['FR_VSS'][len(data) - 1] +
data['BL_VSS'][len(data) - 1] + data['BR_VSS'][len(data) - 1]) / 4
else:
gauge_value = data[sensor_gauge['id']][len(data) - 1]
gauges.append(daq.Gauge(id=sensor_gauge['id'] + "_qo_gauge", max=maxVal, min=minVal, units=gauge_unit,
label=gauge_label, value=gauge_value, showCurrentValue=True,
style={'display': 'inline-block',
'width': '20%'}))
return gauges, graphs, button, no_update, no_update, no_update, no_update, no_update, no_update
def predict(walk_distance, time_survived, duration, game_mode, platform, ride_distance, map_name, weapons_acquired, boosts):
# Create row
row = pd.DataFrame(
columns=['walkDistance', 'timeSurvived', 'duration', 'gameMode',
'platform', 'rideDistance', 'mapName', 'weaponsAcquired', 'boosts'],
data=[[walk_distance, time_survived, duration, game_mode,
platform, ride_distance, map_name, weapons_acquired, boosts]]
)
y_pred = round(model.predict(row)[0] * 100, 2)
label = f'Your chances of winning are {y_pred}%'
output = daq.Gauge(
id='win-place-pred-gauge',
label=label,
color="#F2A900",
showCurrentValue=True,
value=y_pred,
max=100,
min=0,
units="%")
print(f'[DEBUG] {label}')
return output
def make_sentiment_gauge(label, value):
return daq.Gauge(
label=label,
min=0,
max=1,
value=value,
color={"gradient": True, "ranges": {
"green": [0, 0.6], "yellow": [0.6, 0.8], "red": [0.8, 1]}},
size=120)
def explore_model_make_gauges(*values):
if values[-5] == None:
raise PreventUpdate
feature_values = json.loads(values[-5])
data_target = values[-4]
model_length = json.loads(values[-3])
model_weights = json.loads(values[-2])
model_weights = [np.array(i) for i in model_weights]
N_values = json.loads(values[-1])
N = NormalizationTensor.from_stored_value(
np.array(N_values['feature_mean']), np.array(N_values['feature_std']),
np.array(N_values['target_mean']), np.array(N_values['target_std']))
model1 = model1_prep(model_length, add_dropout=False)
model1.set_weights(model_weights)
prediction = N.n2t(model1.predict(N.f2n(feature_values)))
df_target = pd.read_json(data_target)
column_names = list(df_target.columns)
min_targets = df_target.min(axis=0).values
max_targets = df_target.max(axis=0).values
gauges = []
for i, (name, min_value,
max_value) in enumerate(zip(column_names, min_targets,
max_targets)):
gap = (max_value - min_value) / 3
green = [min_value, min_value + gap]
yellow = [min_value + gap, min_value + 2 * gap]
red = [min_value + 2 * gap, min_value + 3 * gap]
gauge = daq.Gauge(id={
'type': 'gaugeModelExploration',
'index': name
},
label=name,
min=min_value,
max=max_value,
value=prediction[0, i],
showCurrentValue=True,
color={
"gradient": True,
"ranges": {
"green": green,
"yellow": yellow,
"red": red
}
},
size=100,
scale={
'start': min_value,
'interval': (max_value - min_value) / 8
})
gauges.append(html.Div(gauge, className='col s1'))
return [html.Div(gauges, className='row')]
def build_quick_stats_panel(self):
"""Quick stats on the left of the view
:return: html.Div object
"""
current_giesszellenbedarf = \
self.dm.giesszellenbedarf_over_time().iloc[0, :]
return html.Div(
id="quick-stats",
className="row",
children=[
html.Div(
id="card-1",
children=[
html.P("Operator ID"),
daq.LEDDisplay(
id="operator-led",
value="042",
color="#92e0d3",
backgroundColor="#1e2130",
size=20,
),
],
),
html.Div(
id="card-2",
children=[
html.P("Durchschnittl. Formverschleiß"),
daq.GraduatedBar(id="form-life-bar",
value=self.dm.avg_attrition),
],
),
html.Div(
id="card-3",
children=[
html.P(
"Prozentuale Gießzellenauslastung diesen Monat"),
daq.Gauge(id="attrition-gauge",
min=0,
max=100,
showCurrentValue=True,
value=100 *
current_giesszellenbedarf.mean() /
current_giesszellenbedarf.max())
],
),
html.Div( # todo: Implement this feature
id="card-4",
children=daq.BooleanSwitch(id="AI-powerbutton",
on=False,
label='AI',
color="#92e0d3"),
),
],
)
def realTimeStatusFigs():
algo_status_fig = html.Div([
daq.Gauge(id='algo-status-fig',
label="% Algorithms",
size=130,
min=0,
max=100)
],
className='col-4')
hyper_status_fig = html.Div([
daq.Gauge(id='hyper-status-fig',
label="% Hyperparameters",
size=130,
min=0,
max=100)
],
className='col-4')
return algo_status_fig, hyper_status_fig
def second_layer(self):
return html.Div([create_paragraph(PVCONNECTION_TEXTS['second_layer']['heading'], \
PVCONNECTION_TEXTS['second_layer']['detail']),
html.Div([
html.Div([daq.Gauge(id='nvri',
color="#9B51E0",
value=0,
label='Increase in NVRI',
max=100,
min=-100,
showCurrentValue=True)
],className="col"),
html.Div([daq.Gauge(id='cri',
color="#9B51E0",
value=0,
label='Increase in CRI',
max=100, min=-100,
showCurrentValue=True )
], className="col"),
],className="row textwhite")
])
def generate_gauge(div_id, label_name, max_value=10):
return daq.Gauge(
id=div_id,
value=0,
label={
'label': label_name,
'style': {
'color': '#C4CDD5'
}
},
max=max_value,
min=0,
)
def update_output(w):
l_items = t.get()
cpu_usage = l_items[0]
total_ram = l_items[1]
used_ram = l_items[2]
disk_usage = l_items[3]
return daq.Gauge(showCurrentValue=True,
id='my-gauge',
label="CPU",
units="%",
max=100,
min=0,
value=cpu_usage), daq.Gauge(
showCurrentValue=True,
id='my-gauge2',
label="Disk",
units="%",
max=100,
min=0,
value=disk_usage), html.P(
'Total RAM :{}'.format(total_ram)), html.P(
'Used RAM :{}'.format(used_ram))
def build_quick_stats_panel():
return html.Div(
id="quick-stats",
className='row',
children=
[
html.Div(
id="card-1",
className='four columns',
children=[
html.H5("Operator ID"),
daq.LEDDisplay(
value='1704',
color=theme['secondary'],
size=50,
theme=theme
)
]
),
html.Div(
id='card-2',
className='four columns',
children=[
html.H5("Time to completion"),
daq.Gauge(
id='progress-gauge',
value=0,
size=150,
max=max_length * 2,
min=0,
color=theme['secondary']
)
]
),
html.Div(
id='utility-card',
className='four columns',
children=[
daq.StopButton(id='stop-button', size=160, buttonText='start')
]
)
]
)
def sentiment(df):
value = df.sentiment[df.sentiment != 0].mean()
value = round(value, 1)
value = float("%.1f" % value)
return daq.Gauge(min=-1,
max=1,
value=value,
showCurrentValue=True,
scale={
'start': -1,
'interval': 0.5,
'labelInterval': 0.5
},
color='#00a0d6',
label=' ',
size=200,
id='sentiment')
def Main():
return html.Div([
dcc.Location(id='url', refresh=False),
html.Div([
html.Div(
html.Img(src="/assets/jolt.png"),
className=
"w3-container w3-cell w3-margin w3-padding w3-center jolt-box"
),
MetricCard("In Queue", "metric_tasks_queued"),
MetricCard("In Progress", "metric_tasks_running"),
MetricCard("Completed (1h)", "metric_tasks_completed"),
MetricCard("Failed (1h)", "metric_tasks_failed"),
],
className="w3-padding w3-border-bottom"),
html.Div(Tabs([("Overview", [
html.Div([
daq.Gauge(id='metric_cluster_load',
label="Cluster Load",
value=0,
max=0,
scale={
'start': 0,
'interval': 1,
'labelInterval': '5'
}),
],
className="w3-container w3-padding"),
]),
("Tasks",
SmallTabs([
("Live",
TaskList(id="tasklist_live",
columns=[
"worker", "name", "identity",
"queued", "started", "status"
])),
("Last Hour", TaskList(id="tasklist")),
],
id="tabs-tasks")),
("Workers", WorkerContent(id="workercontent"))]),
className="w3-cell-row w3-white"),
],
className="w3-light-gray")
def update_graph_interactive_images(buttons,text):
if text=="":
return ''
numericValue=mod.runs(str(text))*10
print(numericValue)
gauge=daq.Gauge(
showCurrentValue=True,
color='#FFC107',
value=numericValue,
label='True-Meter',
max=10,
min=0,
)
return gauge
def predict(clicked, text):
if clicked:
text = [text]
text = wrangle2(text)
y_pred = model.predict(text)
if y_pred == 0:
y_pred = 'hateful'
if y_pred == 1:
y_pred = 'offensive'
if y_pred == 2:
y_pred = 'normal'
df = pd.DataFrame(model.predict_proba(text),
columns=["Hateful", "Offensive", "Normal"])
df_percent = df.mul(100).round(2).astype(str) + '%'
output1 = f'This tweet is labeled as {y_pred}.'
output2 = dash_table.DataTable(data=df_percent.to_dict('records'),
style_cell={
'textAlign': 'left',
'padding': '15px'
},
columns=[{
"name": i,
"id": i
} for i in df_percent.columns],
style_header={
'backgroundColor':
'rgb(230, 230, 230)',
'fontSize': '15px',
'fontWeight': 'bold'
})
output3 = daq.Gauge(
showCurrentValue=True,
units="percentage points",
value=(((df[["Hateful", "Offensive", "Normal"]].max()).max()) *
100).round(2),
label=f'Percent probability this tweet is {y_pred}',
size=380,
labelPosition='bottom',
max=100,
min=0,
)
return output1, output2, output3
def build_quick_stats_panel():
return html.Div(
id="quick-stats",
className="row",
children=[
html.Div(
id="card-1",
className="w3-text-white",
children=[
html.P("Operator ID"),
daq.LEDDisplay(
id="operator-led",
value="1704",
color="#92e0d3",
backgroundColor="#1e2130",
size=50,
),
],
),
html.Div(
id="card-2",
className="w3-text-white",
children=[
html.P("Time to completion"),
daq.Gauge(
id="progress-gauge",
max=max_length * 2,
min=0,
showCurrentValue=True, # default size 200 pixel
),
],
),
html.Div(
id="utility-card",
children=[
daq.StopButton(id="stop-button", size=160, n_clicks=0)
],
),
],
)
def get_layout(UPDATE_INT, config=None):
app_layout = html.Div(
[
html.Div(
[
dcc.Interval(
id="interval_component",
interval=UPDATE_INT * 1000,
n_intervals=0,
),
dcc.Interval(
id="psutil_component", interval=2 * 1000, n_intervals=0
),
],
style=dict(textAlign="center"),
),
html.Div(
[
daq.Gauge(
id="virtual_memory",
min=0,
value=0,
size=150,
className="leftbox",
style=dict(textAlign="center"),
),
daq.Gauge(
id="swap_memory",
min=0,
value=0,
size=150,
className="rightbox",
style=dict(textAlign="center"),
),
]
),
daq.LEDDisplay(
id="timestamp",
value="1000",
color="#FF5E5E",
style=dict(textAlign="center"),
),
html.Br(),
html.Div(
children=[
html.Div(
[
html.Div(
[
html.Label("Proposal"),
dcc.Input(
id="proposal",
placeholder="Enter the proposal",
type="text",
value=config["proposal"],
),
html.Br(),
html.Label("Run Type:", className="leftbox"),
dcc.Dropdown(
id="run-type",
options=[
{"label": i, "value": i} for i in _run_types
],
value=_run_types[0],
className="rightbox",
),
html.Hr(),
daq.BooleanSwitch(id="start", on=False),
html.Br(),
html.Div(id="stream-info"),
html.Hr(),
html.Div(
[
dcc.Dropdown(
id="problem-runs",
placeholder="Problematic runs",
options=[],
className="leftbox",
),
dcc.Textarea(
id="problems-info",
placeholder="Validation Message",
draggable="false",
readOnly=True,
disabled=True,
style={"width": "100%", "height": 200},
),
]
),
],
className="pretty_container one-third column",
),
html.Div(
[
dcc.Dropdown(
id="format-type",
options=[
{"label": i, "value": i} for i in _units
],
value=_units[0],
className="leftbox",
),
dcc.Graph(id="histogram"),
],
className="two-thirds column",
),
],
className="row",
)
]
),
html.Br(),
]
)
return app_layout
html.Label(['Fatigue levels'])
],
style = {
'font-size' : '24px',
'text-align' : 'center',
'marginTop':10,
'marginBottom':5,
}
),
html.Div([
daq.Gauge(
id = 'output-b',
color={"gradient":True,"ranges":{"red":[0,40],"white":[40,60],"blue":[60,100]}},
value= 50,
max=100,
min=0,
)
],
style = {
'text-align' : 'center',
}
),
html.Div([
html.Label(['0 = no fatigue, 100 = maximum fatigue'])
],
style = {
'font-size' : '14px',
"height": "10%",
"marginLeft": "34%",
"marginBottom": "6%",
},
),
html.Div(
[
daq.Gauge(
id="speed-gauge",
showCurrentValue=True,
units="Revolutions/Second",
min=0,
max=3,
value=0,
size=150,
color="#FF5E5E",
label="Revolutions Per Second (Max 3 RPS)",
className="twelve columns",
style={
"marginTop": "5%",
"marginBottom": "-10%",
"color": "#222",
},
)
],
className="row",
style={
"border-radius": "1px",
"border-width": "5px",
"border-top": "1px solid rgb(216, 216, 216)",
},
color='red',
label='Off/Do Not Run =')
])
]),
html.Br(),
html.Div(className='indicator-box',
id='pgpress-container',
children=[
html.H4('Blue Pressure Gauge'),
daq.Gauge(
id='pgpress-status',
min=0,
max=1,
showCurrentValue=True,
color={
"gradient": True,
"ranges": {
"green": [0, 0.3],
"yellow": [0.3, 0.7],
"red": [0.7, 1]
}
},
),
html.P('Order of 1000')
]),
html.Div(className='indicator-box',
id='time-container',
children=[
html.H4('Time to completion (hours)'),
daq.Gauge(id='time-to-completion',
min=0,
max=10,
id='darktheme-daq-booleanswitch',
className='dark-theme-control'
), html.Br(),
daq.ToggleSwitch(
id='darktheme-daq-toggleswitch',
className='dark-theme-control'
), html.Br(),
daq.ColorPicker(
value=17,
id='darktheme-daq-colorpicker',
className='dark-theme-control'
), html.Br(),
daq.Gauge(
min=0,
max=10,
value=6,
color=theme['primary'],
id='darktheme-daq-gauge',
className='dark-theme-control'
), html.Br(),
daq.GraduatedBar(
value=4,
color=theme['primary'],
id='darktheme-daq-graduatedbar',
className='dark-theme-control'
), html.Br(),
daq.Indicator(
value=True,
color=theme['primary'],
id='darktheme-daq-indicator',
className='dark-theme-control'
), html.Br(),
25: {'label': '25'},
50: {'label': '50'},
75: {'label': '75'},
100: {'label': '100'},
}),
],className="pretty_container four columns"),
html.Div([
daq.Gauge(
id='my-gauge',
showCurrentValue=True,
color={"gradient":True,"ranges":{"red":[0,30],"yellow":[30,60],"green":[60,100]}},
label="Test Score",
max=100,
min=0,
value=1
),
])
])
@app.callback(
Output('my-gauge', 'value'),
[Input('Paper1-slider', 'value'),
Input('Paper2-slider', 'value'),
Input('Paper3-slider', 'value'),
Input('Paper4-slider', 'value'),
Input('Paper5-slider', 'value'),
40: {'label': '40'},
60: {'label': '60'},
80: {'label': '80'},
100: {'label': '100'},
}),
],className="pretty_container four columns"),
html.Div([
daq.Gauge(
id='my-gauge',
showCurrentValue=True,
color={"gradient":True,"ranges":{"red":[0,40],"yellow":[40,70],"green":[70,100]}},
label="Probability",
max=100,
min=0,
value=50
),
])
])
@app.callback(
Output('my-gauge', 'value'),
[Input('gre-slider', 'value'),
Input('toefl-slider', 'value'),
Input('rating-slider', 'value'),
Input('sop-slider', 'value'),
Input('lor-slider', 'value'),
html.Div([
daq.Gauge(
id='FinalGrade-gauge',
showCurrentValue=True,
color={"gradient":True,"ranges":{"red":[0,5],"yellow":[5,15],"green":[15,20]}},
label="Final Grade",
max=20,
min=0,
value=10
),
])
])