html.Br(),
# Sub-title Left
html.Div([dcc.Markdown(children=markdown_text1)],
style={
'width': '80%',
'display': 'inline-block',
"font-size": "20px"
}),
# Space between text and dropdown
html.H1(id='space1', children=' '),
# Dropdown
html.Div([
dcc.Textarea(
id="question-input",
value=
"I just want to create a simple C++ struct that has an int index and an int gray value . The function is given the vector with the gray values. When I try to compile it I get a segmentation fault, does ...",
style={
'width': '100%',
'height': 300
})
],
style={
'width': '40%',
'display': 'inline-block'
}),
# # Space between dropdown AND POST
# html.H1(id='space2', children=' '),
# html.Div(id='post-text', children= "Fetching question from "+default_site+"... (Please wait 5-10 seconds) :)",
# style={'whiteSpace': 'pre-line', "font-style": "italic"}),
# Space between text and CATEGORY PREDICTED
html.H1(id='space3', children=' '),
html.H1(id='space4', children=' '),
html.P('Import data from:'),
dcc.Input(id='in-path-input',
type='text',
value=in_path,
style={'min-width': '35em'}),
html.Button('Load',
id='in-path-button',
n_clicks_timestamp=0,
style={
'margin-top': '10pt',
'display': 'block'
}),
dcc.Textarea(id='loaded-files-area',
readOnly=True,
value='\n'.join(loaded_files),
style={
'min-width': '35em',
'display': 'block',
'margin-top': '10pt'
}),
],
style={
'padding': '10pt',
'display': 'inline-block',
'vertical-align': 'top'
}),
html.Div(
[
html.P('Export current view to:'),
dcc.Input(id='export-input',
type='text',
value=in_path,
def get_dashboard_app(server=None):
session = boto3.session.Session(
aws_access_key_id=AWS_ACCESS_KEY_ID,
aws_secret_access_key=AWS_SECRET_ACCESS_KEY
)
if not LOCAL:
logger.info('Attempting to download DB from S3..')
try:
if not os.path.exists(f"{DB_DIR}"):
download_from_s3(local_path=f"{DB_DIR}", s3_path='db.sqlite', bucket=S3_BUCKET_NAME)
except Exception as e:
raise Exception(f'DB cannot be found in S3, or the access credentials are incorrect. Error: {e}')
# Download training data from S3
logger.info('Downloading training data from S3')
files = wr.s3.list_objects(f's3://{S3_BUCKET_NAME}/training_data/', boto3_session=session)
for file in files:
filename = file.split(f's3://{S3_BUCKET_NAME}/training_data/')[1]
download_from_s3(local_path=f'{training_data_dir}/{filename}',
s3_path=f'training_data/{filename}', bucket=S3_BUCKET_NAME)
# Download models from S3
logger.info('Downloading models from S3')
files = wr.s3.list_objects(f's3://{S3_BUCKET_NAME}/models/', boto3_session=session)
files = [f for f in files if 'in_production' not in f]
for file in files:
filename = file.split(f's3://{S3_BUCKET_NAME}/models/')[1]
download_from_s3(local_path=f'{model_dir}/{filename}',
s3_path=f'models/{filename}', bucket=S3_BUCKET_NAME)
# Get the latest predictions
latest_preds = run_query(query='select * from latest_predictions')
# Get the names of models saved in the models directory
model_names = os.listdir(model_dir)
model_names = [m for m in model_names if '.joblib' in m]
if len(model_names) < 1:
raise Exception('No models could be found to add to the dashboard')
predictions = run_query(query='select * from historic_predictions')
logger.info(f'Retrieved {len(predictions)} rows from the historic_predictions table')
# Get historic features
historic_df_all, all_model_ids = get_historic_features(predictions, model_names)
logger.info(f"Found {len(all_model_ids)} models in the DB")
# Drop Duplicates
historic_df_all = historic_df_all.drop_duplicates()
logger.info(f'Retrieved historic features. Rows: {len(historic_df_all)}')
# Load the in-production model
production_model, production_model_id, production_model_dir = load_production_model()
# Get the current season
current_season = run_query(query='select max(season) from main_fixtures').iloc[0, 0]
# Get the historic predictions for the production model
historic_df = historic_df_all[historic_df_all['model_id'] == production_model_id]
# Save latest_preds and all_model_ids to the /tmp file. We will use these to
# determine whether the dashboard has been updated
joblib.dump(latest_preds, f"{tmp_dir}/latest_preds.joblib")
joblib.dump(model_names, f"{tmp_dir}/model_names.joblib")
# Get the performance of the model for each team, at home and away
combined_team_perf = get_team_home_away_performance(historic_df=historic_df, current_season=current_season)
logger.info(f'Getting team performance at home and away, returned {len(combined_team_perf)} rows')
# Get the performance of all models, grouped by season
time_perf = get_performance_by_season(
historic_df_all=historic_df_all, production_model_id=production_model_id)
# Get the cumulative profit gained form each model over time
profit_perf = get_cumulative_profit_view(
historic_df_all=historic_df_all, all_model_ids=all_model_ids, production_model_id=production_model_id)
# Get the cumulative profit from betting on the favourite
profit_perf_bof = get_cumulative_profit_from_bof(historic_df=historic_df)
logger.info(f'Getting cumulative profit from betting on the favourite, returned {len(profit_perf_bof)} rows')
# Get the performance of each model, grouped by the difference in form between teams
form_diff_acc = get_form_dif_view(historic_df_all=historic_df_all, production_model_id=production_model_id)
logger.info(f'Getting performance of each model grouped by difference in form, returned {len(form_diff_acc)} rows')
if server is not None:
logger.info(f'Server passed of type {str(type(server))}. It will only be used if its of type Flask.')
# Create the dashboard
app = dash.Dash(__name__,
# Use the server if its a Flask object, else create our own
server=server if isinstance(server, Flask) else True,
external_stylesheets=[dbc.themes.BOOTSTRAP],
url_base_pathname='/dashboard/')
app.config.suppress_callback_exceptions = True
# Show the latest predictions (if any exist)
if len(latest_preds) > 0:
upcoming_fixture_table = dash_table.DataTable(
id='next_fixtures',
columns=[{"name": i, "id": i} for i in latest_preds.columns],
data=latest_preds.to_dict('records'))
else:
upcoming_fixture_table = html.P('No upcoming fixtures available. Was there a pandemic recently?')
# Define the app layout
app.layout = html.Div(
children=[
dbc.Row([
dbc.Col([
html.H2("Upcoming Fixtures"),
upcoming_fixture_table,
])
]),
dbc.Row([
dbc.Col([
html.H4("Model Performance Plots"),
html.P("The following sections show the performance of the model, split into different views. "
"These views allow us to see where the model is underperforming, so that we can "
"design new features to help mitigate its shortcomings.")
])
]),
dbc.Row([
dbc.Col([
html.H4("Profit Over Time (Cumulative Sum)"),
html.P("This section shows the cumulative profit by date of the different "
"models. We use 10-fold cross-validation to train the model, saving the "
"predictions in each fold. Because of this, we have predictions for 100% "
"of the data which is very useful for analysing model performance"
"when you don't have much data."),
dcc.Graph(
id='profit_by_date',
figure={
'data': [
go.Scatter(
x=profit_perf[profit_perf['Model ID'] == model_id]['Date'],
y=profit_perf[profit_perf['Model ID'] == model_id]['Profit'],
mode="lines+markers",
name='_'.join(model_id.split('_')[:-1]),
) for model_id in all_model_ids] +
[go.Scatter(
x=profit_perf_bof['Date'],
y=profit_perf_bof['Profit'],
mode="lines+markers",
name='Betting on Favourite'
)],
'layout':
go.Layout(
title="Profit Over Time (Cumulative Sum)",
clickmode='event+select',
height=600,
)
})],
width=12)
]),
dbc.Row([
dbc.Col([
html.H4("Model Accuracy for PL Teams - Home and Away"),
html.P("Below you can see the accuracy of the model at predicting "
"for each team, split by whether the team was at home or away. "
"Future strategies may involve only betting on teams that the "
"model has a good accuracy with. The accuracy of the model is "
"mainly down to the consistency of results for the team."),
dcc.Graph(
id='accuracy_home_away',
figure={
'data': [
{'x': combined_team_perf['Team Name'],
'y': combined_team_perf['Accuracy when Home'],
'type': 'bar', 'name': 'Home'},
{'x': combined_team_perf['Team Name'],
'y': combined_team_perf['Accuracy when Away'],
'type': 'bar', 'name': 'Away'}
],
'layout': {
'title': 'Model Accuracy for PL Teams - Home and Away'
}
}
)
], width=12)
]),
dbc.Row(
dbc.Col([
html.H4("Model Accuracy Over Time"),
html.P("Below you can see the accuracy of each model over time. Notice how "
"the model under-performs in certain seasons. The outlier is the year "
"Leicester won the league."),
dcc.Graph(
id='accuracy_over_time',
figure={
'data': [
go.Scatter(
x=time_perf[time_perf['Model ID'] ==
model_id].sort_values('Date')['Date'],
y=time_perf[time_perf['Model ID'] ==
model_id].sort_values('Date')['Accuracy'],
mode="lines+markers",
name='_'.join(model_id.split('_')[:-1])
) for model_id in all_model_ids],
'layout': go.Layout(
title="Model Accuracy Over Time",
clickmode='event+select',
height=600,
)
})],
width=12)
),
dbc.Row(
dbc.Col([
html.H4("Accuracy vs Form Difference"),
html.P("This figure shows the performance of the model, split by the "
"average goal difference of the opponents in the last 8 games. "
"The more difficult matchups are when the teams have similar performance. "
"We can also see the effect of the home advantage here."),
dcc.Graph(
id='form_diff_acc',
figure={
'data': [
go.Scatter(
x=form_diff_acc[form_diff_acc['Model ID'] ==
model_id].sort_values('Form Dif')['Form Dif'],
y=form_diff_acc[form_diff_acc['Model ID'] ==
model_id].sort_values('Form Dif')['Correct'],
mode="lines+markers",
name='_'.join(model_id.split('_')[:-1])
) for model_id in all_model_ids],
'layout': go.Layout(
title="Accuracy vs Form Difference (home_goal_dif_last_7 - "
"away_goal_dif_last_7)",
clickmode='event+select',
height=600,
)
})],
width=12)
),
dbc.Row(
dbc.Col([
dcc.Interval(
id='interval-component',
interval=1 * 1000 * 60, # in milliseconds, run every minute
n_intervals=0
),
dcc.Textarea(id='update')]
)
)
]
)
@app.callback(Output('update', 'value'), [Input('interval-component', 'n_intervals')])
def check_for_updates(interval):
# ToDo: Why isnt this updating properly
# Get the names of models saved in the models directory
model_names = os.listdir(model_dir)
model_names = [m for m in model_names if '.joblib' in m]
latest_preds = run_query(query='select * from latest_predictions')
model_names_local = joblib.load(f"{tmp_dir}/model_names.joblib")
latest_preds_local = joblib.load(f"{tmp_dir}/latest_preds.joblib")
# If there are any updates, save a file for each active graph, which will tell the dashboard to update
if not model_names.sort() == model_names_local.sort() or not latest_preds.equals(latest_preds_local):
logger.info('Updates to the tables have been detected, updating graphs...')
for graph in active_graphs:
joblib.dump(1, f"{tmp_dir}/{graph}.joblib")
# Save the new values
joblib.dump(latest_preds, f"{tmp_dir}/latest_preds.joblib")
joblib.dump(model_names, f"{tmp_dir}/model_names.joblib")
@app.callback(Output('profit-by-date', 'figure'), [Input('interval-component', 'n_intervals')])
def update_pbd(interval):
if os.path.exists(f"{tmp_dir}/profit-by-date.joblib"):
logger.info('Updating profit_by_date graph.')
# Get the names of models saved in the models directory
model_names = os.listdir(model_dir)
predictions = run_query(query='select * from historic_predictions')
# Get historic features
historic_df_all, all_model_ids = get_historic_features(predictions, model_names)
# Load the in-production model
production_model_dir = os.path.join(model_dir, 'in_production')
production_model = os.listdir(production_model_dir)
if len(production_model) > 0:
logger.warning('There are two models in the in_production folder.. Picking the first one.')
production_model_dir = os.path.join(production_model_dir, production_model[0])
logger.info(f'Loading production model from {production_model_dir}')
with open(production_model_dir, 'rb') as f_in:
production_model = joblib.load(f_in)
# Get the ID of the production model
production_model_id = production_model.model_id
# Get the historic predictions for the production model
historic_df = historic_df_all[historic_df_all['model_id'] == production_model_id]
# Get the cumulative profit gained form each model over time
profit_perf = get_cumulative_profit_view(historic_df_all=historic_df_all, all_model_ids=all_model_ids)
# Get the cumulative profit from betting on the favourite
profit_perf_bof = get_cumulative_profit_from_bof(historic_df=historic_df)
os.remove(f"{tmp_dir}/profit-by-date.joblib")
return {
'data': [
go.Scatter(
x=profit_perf[profit_perf['Model ID'] == model_id]['Date'],
y=profit_perf[profit_perf['Model ID'] == model_id]['Profit'],
mode="lines+markers",
name='_'.join(model_id.split('_')[:-1]),
) for model_id in all_model_ids] +
[go.Scatter(
x=profit_perf_bof['Date'],
y=profit_perf_bof['Profit'],
mode="lines+markers",
name='Betting on Favourite'
)],
'layout':
go.Layout(
title="Profit Over Time (Cumulative Sum)",
clickmode='event+select',
height=600,
)
}
@app.callback(Output('accuracy_home_away', 'figure'), [Input('interval-component', 'n_intervals')])
def update_aha(interval):
if os.path.exists(f"{tmp_dir}/accuracy_home_away.joblib"):
logger.info('Updating accuracy_home_away graph.')
# Get the names of models saved in the models directory
model_names = os.listdir(model_dir)
predictions = run_query(query='select * from historic_predictions')
# Get historic features
historic_df_all, all_model_ids = get_historic_features(predictions, model_names)
# Load the in-production model
production_model_dir = os.path.join(model_dir, 'in_production')
production_model = os.listdir(production_model_dir)
if len(production_model) > 0:
logger.warning('There are two models in the in_production folder.. Picking the first one.')
production_model_dir = os.path.join(production_model_dir, production_model[0])
logger.info(f'Loading production model from {production_model_dir}')
with open(production_model_dir, 'rb') as f_in:
production_model = joblib.load(f_in)
# Get the ID of the production model
production_model_id = production_model.model_id
# Get the production model features
historic_df = historic_df_all[historic_df_all['model_id'] == production_model_id]
# Get the performance of the model for each team, at home and away
combined_team_perf = get_team_home_away_performance(historic_df=historic_df, current_season=current_season)
os.remove(f"{tmp_dir}/accuracy_home_away.joblib")
return {
'data': [
{'x': combined_team_perf['Team Name'],
'y': combined_team_perf['Accuracy when Home'],
'type': 'bar', 'name': 'Home'},
{'x': combined_team_perf['Team Name'],
'y': combined_team_perf['Accuracy when Away'],
'type': 'bar', 'name': 'Away'}
],
'layout': {
'title': 'Model Accuracy for PL Teams - Home and Away'
}
}
@app.callback(Output('accuracy_over_time', 'figure'), [Input('interval-component', 'n_intervals')])
def update_aot(interval):
if os.path.exists(f"{tmp_dir}/accuracy_over_time.joblib"):
logger.info('Updating accuracy_over_time graph.')
# Get the names of models saved in the models directory
model_names = os.listdir(model_dir)
predictions = run_query(query='select * from historic_predictions')
# Get historic features
historic_df_all, all_model_ids = get_historic_features(predictions, model_names)
# Load the in-production model
production_model_dir = os.path.join(model_dir, 'in_production')
production_model = os.listdir(production_model_dir)
if len(production_model) > 0:
logger.warning('There are two models in the in_production folder.. Picking the first one.')
production_model_dir = os.path.join(production_model_dir, production_model[0])
logger.info(f'Loading production model from {production_model_dir}')
with open(production_model_dir, 'rb') as f_in:
production_model = joblib.load(f_in)
# Get the ID of the production model
production_model_id = production_model.model_id
# Get the performance of all models, grouped by season
time_perf = get_performance_by_season(
historic_df_all=historic_df_all, production_model_id=production_model_id)
os.remove(f"{tmp_dir}/accuracy_over_time.joblib")
return {
'data': [
go.Scatter(
x=time_perf[time_perf['Model ID'] ==
model_id].sort_values('Date')['Date'],
y=time_perf[time_perf['Model ID'] ==
model_id].sort_values('Date')['Accuracy'],
mode="lines+markers",
name='_'.join(model_id.split('_')[:-1])
) for model_id in all_model_ids],
'layout': go.Layout(
title="Model Accuracy Over Time",
clickmode='event+select',
height=600,
)
}
@app.callback(Output('form_dif_acc', 'figure'), [Input('interval-component', 'n_intervals')])
def update_fda(interval):
if os.path.exists(f"{tmp_dir}/form_dif_acc.joblib"):
logger.info('Updating form_dif_acc graph.')
# Get the names of models saved in the models directory
model_names = os.listdir(model_dir)
predictions = run_query(query='select * from historic_predictions')
# Get historic features
historic_df_all, all_model_ids = get_historic_features(predictions, model_names)
# Load the in-production model
production_model_dir = os.path.join(model_dir, 'in_production')
production_model = os.listdir(production_model_dir)
if len(production_model) > 0:
logger.warning('There are two models in the in_production folder.. Picking the first one.')
production_model_dir = os.path.join(production_model_dir, production_model[0])
logger.info(f'Loading production model from {production_model_dir}')
with open(production_model_dir, 'rb') as f_in:
production_model = joblib.load(f_in)
# Get the ID of the production model
production_model_id = production_model.model_id
# Get the performance of each model, grouped by the difference in form between teams
form_diff_acc = get_form_dif_view(
historic_df_all=historic_df_all, production_model_id=production_model_id)
os.remove(f"{tmp_dir}/form_dif_acc.joblib")
return {
'data': [
go.Scatter(
x=form_diff_acc[form_diff_acc['Model ID'] ==
model_id].sort_values('Form Dif')['Form Dif'],
y=form_diff_acc[form_diff_acc['Model ID'] ==
model_id].sort_values('Form Dif')['Correct'],
mode="lines+markers",
name='_'.join(model_id.split('_')[:-1])
) for model_id in all_model_ids],
'layout': go.Layout(
title="Accuracy vs Form Difference (home_goal_dif_last_7 - "
"away_goal_dif_last_7)",
clickmode='event+select',
height=600,
)
}
# Return the ft_api
return app
ComponentBlock('''import dash_core_components as dcc
dcc.Input(
placeholder='Enter a value...',
type='text',
value=''
)''',
style=styles.code_container),
html.Br(),
dcc.Link('More Input Examples and Reference',
href=tools.relpath("/dash-core-components/input")),
html.Hr(),
html.H3(
dcc.Link('Textarea',
href=tools.relpath('/dash-core-components/textarea'))),
ComponentBlock('''import dash_core_components as dcc
dcc.Textarea(
placeholder='Enter a value...',
value='This is a TextArea component',
style={'width': '100%'}
)''',
style=styles.code_container),
html.Br(),
html.Br(),
dcc.Link('Textarea Reference',
href=tools.relpath("/dash-core-components/textarea")),
html.Hr(),
html.H3(
dcc.Link('Checkboxes',
href=tools.relpath('/dash-core-components/checklist'))),
style={
"height": "350px",
"width": "400px",
"position": "absolute",
"border": "1px solid #2a3f5f",
"border-radius": "5px",
},
),
html.Div([
dcc.Textarea(
id="object-detection",
placeholder="",
value=" ",
style={
"width": "93%",
"height": "291px",
"border": "1px solid #2a3f5f",
"border-radius": "5px",
"position": "absolute",
"top": "363px",
},
)
]),
],
className="five columns",
style={"position": "relative"},
),
],
className="row",
style={"marginTop": "4%"},
),
server = app.server
navbar = dbc.NavbarSimple(
children=[
dbc.NavItem(dbc.NavLink("Source", href="https://github.com/neshitov/response")),
],
brand="Disaster Response Project",
brand_href="#",
sticky="top",
)
app.layout = html.Div(children=[
navbar,
dcc.Textarea(placeholder='Enter a message to classify',
style={'width': '100%'},
value='', id='input-field'),
html.Button('Submit', id='button'),
html.Div( id = 'list',
children = []
),
html.H3(children='Dataset visualization:', style={'text-align':'center'}),
dbc.Container([
dbc.Row([
dbc.Col(html.Img(src=app.get_asset_url('white_cloud.png'))),
dbc.Col(
dcc.Graph(
id='example-graph',
import dash_core_components as dcc
import dash_html_components as html
import plotly.express as px
from dash.dependencies import Input, Output, State
import json
from app import app
gapminder = px.data.gapminder()
gap_jp = gapminder[gapminder["country"] == "Japan"]
handson1 = html.Div([dcc.Textarea(id="textarea"),
html.H1(id="text_output")],
style={"margin": "5%"})
@app.callback(Output("text_output", "children"), [Input("textarea", "value")])
def update_text1(textarea_value):
return textarea_value
handson2 = html.Div(
[
dcc.Textarea(id="textarea2", style={
"height": 300,
"width": 800
}),
html.Button(id="my_button2", n_clicks=0, children="おす"),
html.H1(id="text_output2"),
],
style={"margin": "5%"},
import dash_html_components as html
import webbrowser
app = dash.Dash(__name__)
port = 5000 # or simply open on the default `8050` port
# app = dash.Dash()
server = flask.Flask(__name__)
# app = dash.Dash(__name__,server=server)
app.layout = html.Div(children=[
html.H1(children='Test cases', className="banner"),
html.Iframe(id="storyIFrame",
# src='static/Inferno.html',
className="webpageFrame"),
html.Button("press here", id="button"),
dcc.Textarea(id="textDiv"),
html.Div(children=[html.A('display new page', href='/static/Inferno.html', target='_blank')])
])
@app.callback([Output("storyIFrame", "src"),
Output('textDiv', 'value')],
[Input("button", "n_clicks")]
)
def on_Button_Click(n_clicks):
if n_clicks is None:
return '',''
print('=== button clicked')
# pdb.set_trace()
open_tab("file:///Users/David/OpenSource/github/slexil/Dash_Tests/static/Inferno.html")
webbrowser.open("static/Inferno.html")
html.Br(),
html.Span("Copy text ",
className="uppercase bold"),
html.Span("directly into the text area box."),
])
],
className="app__header",
),
# Textarea box for entering url for text extraction
html.Div(
[
dcc.Textarea(
id='textarea-url',
value='https://en.wikipedia.org/wiki/Kubernetes',
style={
'width': '50%',
'height': 100
},
),
#html.Div(id='textarea-url-output', style={'whiteSpace': 'pre-line'})
],
className="app__url_enter_box",
),
html.Div(
[
html.Button('Extract',
id='textarea-url-button',
n_clicks=0,
style={
'width': '12%',
db.session.commit()
########### Set up the layout
# generates HTML code
app.layout = html.Div(children=[
html.H1('Medicinal Cultivar Recommendations'),
# multi line single-Div
html.Div([
# sections have similar code but unique slider id
# header
html.
H6('Enter a description of your preferences and requested medicinal effects below, and get recommendations from Over 2,300 cultivars.'
),
dcc.Textarea(
id='textarea',
placeholder=
'Please describe your treatment needs here & wait for new results...',
value='',
style={'width': '100%'}),
#added linebreak so no overlap on screen
html.Br(),
# header
# where output data will go
html.H6(id='output-message', children='output will go here')
]),
html.Br(),
html.Br(),
html.A('Inspect and Get The Open Source Code On Github',
href='https://github.com/lineality/medflask1v1'),
html.H6('''For best results, please use terms like the following:
(Effect Terms)
Aroused
data.columns = ['X', 'Y', 'Cluster', 'paper']
#data = pd.DataFrame(np.random.randn(20, 2), columns = ['X', 'Y'])
#print([*['real']*15, *['fake']*5])
#data['paper'] = [*['real']*15, *['fake']*5]
app.layout = html.Div([
html.Div(
[
#Header
html.H3('Fake paper predictor'),
#Input boxes
html.Label('Input paper title'),
dcc.Textarea(
id='input_title',
placeholder='Enter a paper title',
#type='text',
value='hello',
style={'width': '100%'}),
html.Label('Input paper abstract'),
dcc.Textarea(
id='input_abstract',
placeholder='Enter a the paper abstract',
#type='text',
value='hello',
style={'width': '100%'}),
#Slider to
html.Label('N similar papers'),
dcc.Slider(id='npaper',
min=1,
max=10,
'value': i,
'label': i
} for i in df.index],
multi=False,
),
html.Div(id='most-similar'),
html.Br(),
html.Hr(),
html.H5('What do you want to know?'),
html.Div(
'Returns suggestions from the disclosure log. Based on cosine similarity of vectors of submitted text vs requests. These sentence/document vectors are a TF-IDF weighted average of the vectors of the constituent words.'
),
html.Br(),
dcc.Textarea(
id='search-textarea',
placeholder='Your request...',
rows=50,
style={'width': '50%'},
),
html.Br(),
html.Button('Submit', id='search_log_button'),
html.Br(),
html.Div(id='results-list'),
html.Br(),
html.Br(),
html.Hr(),
]
app.layout = html.Div(children=layout_children)
@app.callback(
from dash.dependencies import Input, Output, State
from sklearn.externals import joblib
model = joblib.load('modelv1.pkl')
app = dash.Dash()
server = app.server
app.layout = html.Div([
html.H2(children=
"Hello :D! Let's see if you are saying something nice or mean..."),
html.Div([
html.H3(children="Enter your thoughts:"),
dcc.Textarea(id='input-text-area',
placeholder="I feel...",
style={
'width': '50%',
'height': '100%'
}),
html.Br(),
html.Div([
html.Button(id='submit-button',
n_clicks=0,
children='Press me and let me guess',
style={
'font-size': '14px',
'marginLeft': 10
}),
html.A(children=html.Button(children='Source in GitHub',
style={
'font-size': '14px',
'marginLeft': 10
def corner_modeling():
with open("./assets/templates/sp_lib", mode="r") as f:
buffer_lib = f.read()
return html.Div([
html.H5("Selected Modeling"),
dbc.Row([
dbc.Col(
dcc.Textarea(
id="buffer-textarea",
value=buffer_lib,
style={
"width": "100%",
"height": 500
},
)),
]),
dbc.Row(
[
dbc.Col(dbc.Label("VTH"), width=1),
dbc.Col([
dbc.InputGroup([
dbc.InputGroupAddon("TT", addon_type="prepend"),
dbc.Input(id="target-tt-vth", value=0.27),
], ),
dbc.InputGroup([
dbc.InputGroupAddon("SS", addon_type="prepend"),
dbc.Input(id="target-ss-vth"),
], ),
dbc.InputGroup([
dbc.InputGroupAddon("FF", addon_type="prepend"),
dbc.Input(id="target-ff-vth"),
], ),
]),
dbc.Col(dbc.Label("Ion"), width=1),
dbc.Col([
dbc.InputGroup([
dbc.InputGroupAddon("TT", addon_type="prepend"),
dbc.Input(id="target-tt-ion", value=8.36),
], ),
dbc.InputGroup([
dbc.InputGroupAddon("SS", addon_type="prepend"),
dbc.Input(id="target-ss-ion"),
], ),
dbc.InputGroup([
dbc.InputGroupAddon("FF", addon_type="prepend"),
dbc.Input(id="target-ff-ion"),
], ),
]),
dbc.Col([
dbc.Button("Fit Corner", id="fit-button",
className="mr-2"),
]),
],
className="m-1",
),
dbc.Row([
dbc.Col([
dcc.Textarea(id="corner-output",
value="library",
style={
"width": "100%",
"height": 100
},
loading_state={'is_loading': True}),
]),
]),
dbc.Button("Apply to Library",
color="primary",
id="library-bnt",
block=True)
])
'value': 'linear'
}, {
'label': 'logarithmic',
'value': 'log'
}],
value='linear',
inputStyle={
'margin-left': '5px',
'margin-right': '5px'
})
]),
html.Hr(),
html.P('Stock index tickers:'),
dcc.Textarea(id='txtr-index-tickers',
value='\n'.join(default_index_tickers),
style={
'height': '12rem',
'width': '12rem'
}),
html.P('Commodity tickers:'),
dcc.Textarea(
id='txtr-commodity-tickers',
value='\n'.join(default_commodity_tickers),
style={
'height': '12rem',
'width': '12rem'
})
],
style={'padding': '1rem'})
]),
# body
dbc.Col(
def _sub_layouts(self):
self.favorite_button = Button(
[Icon(kind="heart", fill="r"),
html.Span("Favorite")],
kind="white",
id=self.id("favorite-button"),
)
self.favorited_button = Button(
[Icon(kind="heart", fill="s"),
html.Span("Favorited")],
kind="danger",
id=self.id("favorite-button"),
)
favorite_button_container = html.Div(
[self.favorite_button],
id=self.id("favorite-button-container"),
style={"display": "inline-block"},
)
favorite_materials = html.Div(
[
Reveal(
[self._make_links([])],
title=H6(
"Favorited Materials",
style={
"display": "inline-block",
"vertical-align": "middle"
},
),
id=self.id("favorite-materials"),
open=True,
)
],
id=self.id("favorite-materials-container"),
style={"display": "none"},
)
# TODO: add when Dash supports text areas!
notes_layout = Reveal(
[
Field([
Control(
dcc.Textarea(
id=self.id("favorite-notes"),
className="textarea",
rows=6,
style={
"height": "100%",
"width": "100%"
},
placeholder=
"Enter your notes on the current material here",
)),
html.P(
[
dcc.Markdown(
"Favorites and notes are saved in your web browser "
"and not associated with your Materials Project account or stored on our servers. "
"If you want a permanent copy, [click here to download all of your notes]()."
)
],
className="help",
),
])
],
title="Notes",
)
return {
"button": favorite_button_container,
"favorite_materials": favorite_materials,
"notes": notes_layout,
}
def get_page_apartments():
df = dss.apartment_summary.df
fields = data.get_apartment_key_fields()
fields += [
'growth_rate', 'min_count', 'max_count', 'initial_condition', 'n'
]
fields += df.filter(regex='^tz_count_.*').columns.tolist()
# TODO: Apply filters on first_count and elapsed_hours_min
return [
html.Div(id='table-info-apartments', style={'float': 'right'}),
html.Details([
html.Summary('Apartment Data', style={'font-size': '18'}),
html.Div([
dcc.Markdown(
'Each row of this table corresponds to an individual apartment (for {} total apartments '
'across all experimental conditions) and includes the estimated growth rate of cells within'
'that apartment as well as a few other statistics useful for sorting/filtering.'
'\n\n**Field Definitions**\n'
'- {}: Experimental conditions\n'
'- **growth_rate**: 24hr growth rate estimate (log 2)\n'
'- **min_count**: Minimum cell count across all images taken'
' (cell type/component set in analysis configuration)\n'
'- **max_count**: Maximum cell count across all images taken'
' (cell type/component set in analysis configuration)\n'
'- **initial_condition**: String name associated with classification of time zero'
' conditions within apartment (e.g. single_cell, no_cell, double_cell, many_cells)\n'
'- **tz_count_\*_\***: Time zero counts for each cell type and component\n'
'- **n**: Number of time points (i.e. hours) for which measurements exist for apartment'
.format(
len(df), ', '.join([
'**{}**'.format(f)
for f in cfg.experimental_condition_fields
]))),
html.Div([
dcc.Markdown(
'-----\n'
'**Custom data operations**\n\n'
'Enter any custom apartment data processing code in the field below, and it will be\n'
'applied to the data shown in the following table (after pressing the "Apply" button).\n'
'Examples:\n'
'```\n'
'df = df[df[\'apt_num\'] == \'01\']\n'
'df.to_csv(\'/tmp/apartment_data.csv\')\n'
'print(\'Data filtered and saved to csv at "/tmp/apartment_data.csv"\')\n'
'```\n\n'),
dcc.Textarea(placeholder='',
value='',
style={
'width': '100%',
'height': '125px',
'margin-top': '10px'
},
id='code-apartments'),
html.Button(
'Apply',
id='code-apartments-apply',
),
])
])
]),
html.Div([get_datatable('table-apartments-data', df[fields])]),
html.Div([
html.Div(dcc.Graph(id='graph-growth-data'),
className='seven columns',
style={'margin-top': '10px'}),
html.Div([
html.Div(dcc.Dropdown(
id='apartment-dropdown',
placeholder='Apartment (must be selected in table first)'),
style={
'display': 'inline-block',
'width': '40%'
}),
html.Div(dcc.Textarea(
placeholder='Filter ("hr in [0,24] and hr != 72")',
style={
'width': '100%',
'margin': '0px',
'min-height': '35px',
'height': '35px'
},
id='apartment-time-filter'),
style={
'display': 'inline-block',
'width': '45%'
}),
html.Div(dcc.Checklist(options=[{
'label': 'Centroids',
'value': 'enabled'
}],
values=['enabled'],
id='enable-cell-marker'),
style={
'display': 'inline-block',
'width': '15%',
'vertical-align': 'top',
'padding-top': '4px'
}),
html.Div(id='apartment-animation',
style={
'overflow-x': 'scroll',
'white-space': 'nowrap'
}),
html.Div([
html.Div(dcc.Dropdown(
id='export-apartment-type',
options=[{
'label': 'Single TIF',
'value': 'tif'
}, {
'label': 'Single TIF (+centroids)',
'value': 'tif_markers'
}, {
'label': 'Multi PNG',
'value': 'png'
}, {
'label': 'Multi PNG (+centroids)',
'value': 'png_markers'
}, {
'label': 'RectLabel Annotations',
'value': 'rectlabel_annotations'
}],
placeholder='Export Type',
clearable=False),
style={
'display': 'inline-block',
'width': '40%',
'margin-top': '1px'
}),
html.Div(html.Button('Export',
id='export-apartment-images',
style={
'width': '100%',
'height': '35px',
'line-height': '18px'
}),
style={
'display': 'inline-block',
'width': '60%',
'vertical-align': 'top',
'padding-top': '1px'
})
])
],
className='five columns',
style={'margin-top': '0'})
],
className='row'),
html.Div(id='null1', style={'display': 'none'})
]
html.Div(id='laborFreqOut',className='four columns'),
html.Div(id='laborSpectroOut', className='four columns'),
],className='row'),
html.Div([
html.Div([
html.H4(
children = 'Additional Symptoms/Comments',
style = {
'color':'#FF2A9C'
}
),
dcc.Textarea(
placeholder='Additional Symptoms/Comments',
value='',
style={'width': '100%'}
),
html.Br(),
html.Br(),
html.Br(),
],className='four columns'),
html.Div([
html.Div(id='pieOut'),
],className='five columns'),
html.Div([
],className='three columns')
dcc.Dropdown(
id = 'table-dropdown',
options=[{'label': k, 'value': k} for k in schema.keys()],
),
html.H5("Select:"),
dcc.Dropdown(
id = 'table-columns-dropdown',
multi=True
),
html.Hr(),
]))),
dbc.Row(dbc.Col(html.Div([
dcc.Textarea(
id='query_input',
style={'width':'100%', 'height':'200'},
value="SELECT {columns} FROM {table}"
),
html.Button('Ask your OS.', id='query_submit'),#add a link to schema on far right column?
html.Div(id='query_table_container')]
))),
])
layout = html.Div([nav,body])
@app.callback(
Output('table-columns-dropdown', 'options'),
(Input('table-dropdown', 'value')))
def set_columns_dropdown(selected_table):
if selected_table is None:
return dash.no_update
def uploadPage():
if 'user_id' in session:
user = [x for x in users if x.id == session['user_id']][0]
g.user = user
user_name = user.username
else:
user_name = " "
## Step 1. Questions for Data Source Information
uploadDataInfo = dbc.Container(
[
html.
H5('Step 1. Fill out the Data Source questionnaire below with appropriate information and links as needed.',
id="Instructions"),
#### Identifying - Name, Institution, Database Name
dbc.Row([
html.Form([
dcc.Input(placeholder='Name',
id='uploader-Name',
value=str(user_name),
type='text'),
dcc.Input(placeholder='Institution',
id='user-inst',
type='text'),
dcc.Input(placeholder='Database Name',
id='db-name',
type='text'),
]),
]),
#### Things with URL inputs - Peer Reviewed, Field Method, Lab Method, QA, Full QA
dbc.Row([
HalsNamedInlineRadioItems(
name="Is the data peer reviewed or published?",
id="is-data-reviewed",
options=data_Review_options,
),
dcc.Input(placeholder='URL Link',
type='text',
id='publication-url',
style={'display': 'none'}),
]),
dbc.Row([
HalsNamedInlineRadioItems(
name="Is the field method reported?",
id="is-field-method-reported",
options=data_Review_options,
),
dcc.Input(placeholder='URL Link',
type='text',
id='field-method-report-url',
style={'display': 'none'}),
]),
dbc.Row([
HalsNamedInlineRadioItems(
name="Is the lab method reported?",
id="is-lab-method bui-reported",
options=data_Review_options,
),
dcc.Input(placeholder='URL Link',
type='text',
id='lab-method-report-url',
style={'display': 'none'}),
]),
dbc.Row([
HalsNamedInlineRadioItems(
name='Is the QA/QC data available?',
id="is-qaqc-available",
options=data_Review_options,
),
dcc.Input(placeholder='URL Link',
type='text',
id='qaqc-url',
style={'display': 'none'}),
]),
dbc.Row([
HalsNamedInlineRadioItems(
name='Is the full QA/QC data available upon request?',
id="is-full-qaqc-available",
options=data_Review_options,
),
dcc.Input(placeholder='URL Link',
type='text',
id='full-qaqc-url',
style={'display': 'none'}),
]),
# Cell Count
dbc.Row([
dbc.Row(html.P('Cell count method?')),
dbc.Row(
dcc.Input(placeholder='URL Link',
type='text',
id='cell-count-url',
style={
'display': 'inline-block',
'margin-left': '.5rem',
'text-align': 'center'
}), ),
]),
# Ancillary
dbc.Row([
dbc.Row(html.P('Ancillary data available?')),
dbc.Row(
dcc.Textarea(
placeholder='Description of parameters or URL link',
id='ancillary-data',
style={
'display': 'inline-block',
'margin-left': '.5rem',
'text-align': 'center'
}), ),
]),
], )
## Step 2. Questions for Methodology Information
uploadMethodologies = dbc.Container(
[
html.
H5('Step 2. Fill out the Methodology questionnaire below with appropriate information and links as needed.',
id="Instructions"),
# Substrate
dbc.Row([
HalsNamedInlineRadioItems(
name="Substrate",
id="substrate-option",
options=Substrate_Status_options,
)
],
className="uploadOption"),
# Sample Type
dbc.Row([
HalsNamedInlineRadioItems(
name="Sample Types",
id="sample-type-option",
options=Sample_Types_options,
),
]),
# Field Method
dbc.Row([
HalsNamedInlineRadioItems(name="Field Method",
id="field-method-option",
options=Field_Methods_options),
dcc.Input(placeholder='Depth Integrated (m)',
type='text',
id='vertically-depth-integrated',
style={'display': 'none'}),
dcc.Input(placeholder='Depth Sampled (m)',
type='text',
id='discrete-depth-sampled',
style={'display': 'none'}),
dcc.Input(placeholder='Depth of Sample (m)',
type='text',
id='spatially-integrated-depth',
style={'display': 'none'}),
dcc.Input(placeholder='# of samples integrated',
type='text',
id='num-spatially-integrated-samples',
style={'display': 'none'}),
]),
#### Microcystin Method and filtering
dbc.Row([
HalsNamedInlineRadioItems(
name="Microcystin Method",
id="microcystin-method",
options=Microcystin_Method_options,
),
]),
dbc.Row([
HalsNamedInlineRadioItems(
name="Was Sample Filtered?",
id="sample-filtered",
options=data_Review_options,
),
dcc.Input(placeholder='Filter Size (μm)',
type='text',
id='filter-size',
style={'display': 'none'}),
]),
], )
## Step 3. Links to example csv
exampleBar = dbc.Container([
html.
H5('Step 3. Download the outline file below and copy the appropriate data into the csv file.',
id="Instructions"),
dbc.Row(html.A("Download Datasheet Outline File",
href=dfexampleSheet,
target='blank',
download='GLEON_GMA_OUTLINE.csv',
className="mr-1",
style={
'textAlign': 'center',
"padding": "2rem .5rem 2rem .5rem"
}),
justify="center",
form=True),
])
## Step 4. Spot to upload files
dragUpload = dbc.Container([
html.
H5('Step 4. Select or drag and drop the filled out csv file containing your data using the provided outline.',
id="Instructions"),
dcc.Upload(
id="upload-data",
children=[
"Drag and Drop or ",
html.A(children="Select a File"),
],
style={
'width': '100%',
'height': '60px',
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px',
'textAlign': 'center',
'margin': '10px'
},
accept=".csv, .xls, .xlsx",
),
dbc.Row([
HalsNamedInlineRadioItems(
name=
'Would you like to password protect this file? If so, users will not be able to download it from the data page.',
id="pw-protect",
options=data_Review_options,
),
dcc.Input(placeholder='Password',
type='password',
id='pw-protect-txt',
style={'display': 'none'}),
]),
html.Div(id='upload-output'),
], )
## Step 5. Upload button and warning about using outline
uploadButton = dbc.Container([
html.H5(
'Step 5. Click \'Upload\' to '
'upload your data and information to the project.',
id="Instructions"),
html.P(
'**Please note, the data must be in the same format as the outline file provided in step 3 above. Failure to use this '
'outline will likely return an error.**',
style={'font-size': '1.5rem'}),
html.Button(id='upload-button',
n_clicks=0,
children='Upload',
style={
'margin': '1rem .5rem .6rem .5rem',
'justify': 'center'
}),
dbc.Row(html.P(id='upload-msg')),
], )
## Combines all the questions/forms from above into one section, this is what's actually returned to the page
uploadBar = html.Div([
dbc.Col([
dbc.Row(html.H5(
"Thank you for your interest in contributing to our data collection! Please follow the 5 steps below to upload your file to our database.",
style={'textAlign': 'center'}),
justify="center",
form=True),
dbc.Row(html.A(
"Contact us with any questions, problems, or concerns!",
href="/PageContact",
className="mr-1",
style={
'textAlign': 'center',
"padding": "2rem .5rem 2rem .5rem"
}),
justify="center",
form=True),
],
className="twelve columns"),
dbc.Row(uploadDataInfo,
className='pretty_container ten columns offset-by-one'),
dbc.Row(uploadMethodologies,
className='pretty_container ten columns offset-by-one'),
dbc.Row([
dbc.Col(exampleBar, className="pretty_container five columns"),
dbc.Col(dragUpload, className="pretty_container five columns"),
],
className="ten columns offset-by-one"),
dbc.Row(uploadButton,
className='pretty_container ten columns offset-by-one')
],
className="twelve columns")
layout = uploadBar
return layout
def get_acl_content():
options = [{'label': 'Cisco IOS', 'value': 'cisco'},
{'label': 'Cisco NX-OS', 'value': 'cisco-nx'},
{'label': 'Cisco XR', 'value': 'cisco-xr'},
{'label': 'Juniper', 'value': 'juniper'}]
return html.Div(
id="acl_content_container",
children=[
html.Div(
id="acl_content",
children=[
html.Div(children=[
html.Fieldset(
id="acl_original_fieldset",
children=[html.Legend("Original ACL"),
dbc.InputGroup(
id="acl_original_input_group",
children=[
html.Div([dbc.Button(
"Get Configuration",
id="acl_get_config_button")]),
html.Div(
children=[
dbc.InputGroup([
dbc.InputGroupAddon(
"Choose Platform",
addon_type="prepend"),
dbc.Select(
id="acl_original_choose_platform",
options=options,
value='cisco',
),
]),
]),
]),
html.Div(
id="acl_original_text_area_container",
children=[
dcc.Textarea(
id='acl_original_textarea',
value='',
placeholder="Please Copy and Paste an ACL",
required=True,
),
]),
]),
]),
html.Div([dbc.Button(
"Analyze",
id="acl_analyze_button")]),
html.Div(children=[
html.Fieldset(
id="acl_refactored_fieldset",
children=[html.Legend("Refactored ACL"),
html.Div(
id="acl_refactored_choose_platform_container",
children=[
dbc.InputGroup([
dbc.InputGroupAddon(
"Choose Platform",
addon_type="prepend"),
dbc.Select(
id="acl_refactored_choose_platform",
options=options,
value='',
),
]),
]),
html.Div(
id="acl_refactored_text_area_container",
children=[
dcc.Textarea(
id='acl_refactored_textarea',
value='',
placeholder="Please Copy and Paste an ACL",
required=True,
),
]),
]),
]),
]
),
html.Div(
id="acl_result_fieldset_container",
children=[
html.Fieldset(
id="acl_result_fieldset",
children=[html.Legend("Results"),
html.Div(
id="acl_result_table",
),
]),
]),
]
)
import dash
import dash_html_components as html
import dash_bootstrap_components as dbc
import dash_core_components as dcc
from dash.dependencies import Input, Output
app = dash.Dash(__name__)
app.layout = html.Div(
dbc.Container([
html.Br(),
dcc.Textarea(style={
'width': '100%',
'height': '300px'
},
id='input',
value='',
placeholder='请输入文字内容！'),
html.P(id='output')
]))
@app.callback(Output('output', 'children'), Input('input', 'value'))
def mask_dirty_talk(value):
return value.replace('他妈', '**')
if __name__ == "__main__":
app.run_server(debug=True)
def test_gallery(self):
app = dash.Dash(__name__)
app.layout = html.Div([
html.Div(id='waitfor'),
html.Label('Upload'),
dcc.Upload(),
html.Label('Horizontal Tabs'),
dcc.Tabs(
id="tabs",
children=[
dcc.Tab(label='Tab one',
className='test',
style={'border': '1px solid magenta'},
children=[html.Div(['Test'])]),
dcc.Tab(label='Tab two',
children=[
html.Div([
html.H1("This is the content in tab 2"),
html.P("A graph here would be nice!")
])
],
id='tab-one'),
dcc.Tab(label='Tab three',
children=[
html.Div([
html.H1("This is the content in tab 3"),
])
]),
],
style={'fontFamily': 'system-ui'},
content_style={
'border': '1px solid #d6d6d6',
'padding': '44px'
},
parent_style={
'maxWidth': '1000px',
'margin': '0 auto'
}),
html.Label('Vertical Tabs'),
dcc.Tabs(
id="tabs1",
vertical=True,
children=[
dcc.Tab(label='Tab one', children=[html.Div(['Test'])]),
dcc.Tab(label='Tab two',
children=[
html.Div([
html.H1("This is the content in tab 2"),
html.P("A graph here would be nice!")
])
]),
dcc.Tab(label='Tab three',
children=[
html.Div([
html.H1("This is the content in tab 3"),
])
]),
]),
html.Label('Dropdown'),
dcc.Dropdown(options=[{
'label': 'New York City',
'value': 'NYC'
}, {
'label': u'Montréal',
'value': 'MTL'
}, {
'label': 'San Francisco',
'value': 'SF'
}, {
'label': u'北京',
'value': u'北京'
}],
value='MTL',
id='dropdown'),
html.Label('Multi-Select Dropdown'),
dcc.Dropdown(options=[{
'label': 'New York City',
'value': 'NYC'
}, {
'label': u'Montréal',
'value': 'MTL'
}, {
'label': 'San Francisco',
'value': 'SF'
}, {
'label': u'北京',
'value': u'北京'
}],
value=['MTL', 'SF'],
multi=True),
html.Label('Radio Items'),
dcc.RadioItems(options=[{
'label': 'New York City',
'value': 'NYC'
}, {
'label': u'Montréal',
'value': 'MTL'
}, {
'label': 'San Francisco',
'value': 'SF'
}, {
'label': u'北京',
'value': u'北京'
}],
value='MTL'),
html.Label('Checkboxes'),
dcc.Checklist(options=[{
'label': 'New York City',
'value': 'NYC'
}, {
'label': u'Montréal',
'value': 'MTL'
}, {
'label': 'San Francisco',
'value': 'SF'
}, {
'label': u'北京',
'value': u'北京'
}],
values=['MTL', 'SF']),
html.Label('Text Input'),
dcc.Input(value='',
placeholder='type here',
type='text',
id='textinput'),
html.Label('Disabled Text Input'),
dcc.Input(value='disabled',
type='text',
id='disabled-textinput',
disabled=True),
html.Label('Slider'),
dcc.Slider(
min=0,
max=9,
marks={
i: 'Label {}'.format(i) if i == 1 else str(i)
for i in range(1, 6)
},
value=5,
),
html.Label('Graph'),
dcc.Graph(id='graph',
figure={
'data': [{
'x': [1, 2, 3],
'y': [4, 1, 4]
}],
'layout': {
'title': u'北京'
}
}),
html.Label('DatePickerSingle'),
dcc.DatePickerSingle(id='date-picker-single',
date=datetime(1997, 5, 10)),
html.Label('DatePickerRange'),
dcc.DatePickerRange(id='date-picker-range',
start_date=datetime(1997, 5, 3),
end_date_placeholder_text='Select a date!'),
html.Label('TextArea'),
dcc.Textarea(placeholder='Enter a value... 北京',
style={'width': '100%'}),
html.Label('Markdown'),
dcc.Markdown('''
#### Dash and Markdown
Dash supports [Markdown](http://commonmark.org/help).
Markdown is a simple way to write and format text.
It includes a syntax for things like **bold text** and *italics*,
[links](http://commonmark.org/help), inline `code` snippets, lists,
quotes, and more.
北京
'''.replace(' ', '')),
dcc.Markdown(['# Line one', '## Line two']),
dcc.Markdown(),
dcc.SyntaxHighlighter(dedent('''import python
print(3)'''),
language='python'),
dcc.SyntaxHighlighter(['import python', 'print(3)'],
language='python'),
dcc.SyntaxHighlighter()
])
self.startServer(app)
self.wait_for_element_by_css_selector('#waitfor')
self.snapshot('gallery')
self.driver.find_element_by_css_selector(
'#dropdown .Select-input input').send_keys(u'北')
self.snapshot('gallery - chinese character')
text_input = self.driver.find_element_by_id('textinput')
disabled_text_input = self.driver.find_element_by_id(
'disabled-textinput')
text_input.send_keys('HODOR')
# It seems selenium errors when send(ing)_keys on a disabled element.
# In case this changes we try anyway and catch the particular
# exception. In any case Percy will snapshot the disabled input style
# so we are not totally dependent on the send_keys behaviour for
# testing disabled state.
try:
disabled_text_input.send_keys('RODOH')
except InvalidElementStateException:
pass
self.snapshot('gallery - text input')
def modal():
contacts["Name"] = (
contacts["Salutation"]
+ " "
+ contacts["FirstName"]
+ " "
+ contacts["LastName"]
)
return html.Div(
html.Div(
[
html.Div(
[
# modal header
html.Div(
[
html.Span(
"New Case",
style={
"color": "#506784",
"fontWeight": "bold",
"fontSize": "20",
},
),
html.Span(
"×",
id="cases_modal_close",
n_clicks=0,
style={
"float": "right",
"cursor": "pointer",
"marginTop": "0",
"marginBottom": "17",
},
),
],
className="row",
style={"borderBottom": "1px solid #C8D4E3"},
),
# modal form
html.Div(
[
# left Div
html.Div(
[
html.P(
"Account name",
style={
"textAlign": "left",
"marginBottom": "2",
"marginTop": "4",
},
),
html.Div(
dcc.Dropdown(
id="new_case_account",
options=[
{
"label": row["Name"],
"value": row["Id"],
}
for index, row in accounts.iterrows()
],
clearable=False,
value=accounts.iloc[0].Id,
)
),
html.P(
"Priority",
style={
"textAlign": "left",
"marginBottom": "2",
"marginTop": "4",
},
),
dcc.Dropdown(
id="new_case_priority",
options=[
{"label": "High", "value": "High"},
{"label": "Medium", "value": "Medium"},
{"label": "Low", "value": "Low"},
],
value="Medium",
clearable=False,
),
html.P(
"Origin",
style={
"textAlign": "left",
"marginBottom": "2",
"marginTop": "4",
},
),
dcc.Dropdown(
id="new_case_origin",
options=[
{"label": "Phone", "value": "Phone"},
{"label": "Web", "value": "Web"},
{"label": "Email", "value": "Email"},
],
value="Phone",
clearable=False,
),
html.P(
"Reason",
style={
"textAlign": "left",
"marginBottom": "2",
"marginTop": "4",
},
),
dcc.Dropdown(
id="new_case_reason",
options=[
{
"label": "Installation",
"value": "Installation",
},
{
"label": "Equipment Complexity",
"value": "Equipment Complexity",
},
{
"label": "Performance",
"value": "Performance",
},
{
"label": "Breakdown",
"value": "Breakdown",
},
{
"label": "Equipment Design",
"value": "Equipment Design",
},
{
"label": "Feedback",
"value": "Feedback",
},
{"label": "Other", "value": "Other"},
],
value="Installation",
clearable=False,
),
html.P(
"Subject",
style={
"float": "left",
"marginTop": "4",
"marginBottom": "2",
},
className="row",
),
dcc.Input(
id="new_case_subject",
placeholder="The Subject of the case",
type="text",
value="",
style={"width": "100%"},
),
],
className="six columns",
style={"paddingRight": "15"},
),
# right Div
html.Div(
[
html.P(
"Contact name",
style={
"textAlign": "left",
"marginBottom": "2",
"marginTop": "4",
},
),
html.Div(
dcc.Dropdown(
id="new_case_contact",
options=[
{
"label": row["Name"],
"value": row["Id"],
}
for index, row in contacts.iterrows()
],
clearable=False,
value=contacts.iloc[0].Id,
)
),
html.P(
"Type",
style={
"textAlign": "left",
"marginBottom": "2",
"marginTop": "4",
},
),
dcc.Dropdown(
id="new_case_type",
options=[
{
"label": "Electrical",
"value": "Electrical",
},
{
"label": "Mechanical",
"value": "Mechanical",
},
{
"label": "Electronic",
"value": "Electronic",
},
{
"label": "Structural",
"value": "Structural",
},
{"label": "Other", "value": "Other"},
],
value="Electrical",
),
html.P(
"Status",
style={
"textAlign": "left",
"marginBottom": "2",
"marginTop": "4",
},
),
dcc.Dropdown(
id="new_case_status",
options=[
{"label": "New", "value": "New"},
{
"label": "Working",
"value": "Working",
},
{
"label": "Escalated",
"value": "Escalated",
},
{"label": "Closed", "value": "Closed"},
],
value="New",
),
html.P(
"Supplied Email",
style={
"textAlign": "left",
"marginBottom": "2",
"marginTop": "4",
},
),
dcc.Input(
id="new_case_email",
placeholder="email",
type="email",
value="",
style={"width": "100%"},
),
html.P(
"Description",
style={
"float": "left",
"marginTop": "4",
"marginBottom": "2",
},
className="row",
),
dcc.Textarea(
id="new_case_description",
placeholder="Description of the case",
value="",
style={"width": "100%"},
),
],
className="six columns",
style={"paddingLeft": "15"},
),
],
style={"marginTop": "10", "textAlign": "center"},
className="row",
),
# submit button
html.Span(
"Submit",
id="submit_new_case",
n_clicks=0,
className="button button--primary add"
),
],
className="modal-content",
style={"textAlign": "center", "border": "1px solid #C8D4E3"},
)
],
className="modal",
),
id="cases_modal",
style={"display": "none"},
)
],
value = intervals[0],
placeholder="Filter by interval",
clearable=True
)
],
width={'size':3}),
# Filter 3: Forecast period (days)
dbc.Col([
html.H5("Forecast Period (days):",style={'color': 'white','text-align': 'left'}),
dcc.Textarea(
id = 'forecast_period',
placeholder=' days',
value = '5',
# value='Textarea content initialized\nwith multiple lines of text',
style={'width': '100%'}
)
,\
],
width={'size':3}),
dbc.Col([
html.Br(),
html.Br(),
dbc.Button(children='Apply', color="dark",size="lg", id="submit_btn",n_clicks=0, className="mr-1"),
],
width={'size':3}),
],style={'backgroundColor': "#003B70"}),
def create_app_ui():
global dfff
dfff = pd.read_csv('ETSY.csv')
# Create the UI of the Webpage here
main_layout = html.Div([
html.H1(id='Main_title',
children='Sentiment Analysis with Insights',
style={
"text-align": "center",
"font-size": "300%"
}),
#piechart section
html.H1(children='Pie Chart',
id='Main_title2',
style={
'textAlign': 'center',
'color': 'blue'
}),
html.Label("Pie Chart of reviews"),
dcc.Graph(id="piechart", figure=None),
#review_check section
html.H1(children='Review Check Section',
id='Main_title3',
style={
'textAlign': 'center',
'color': 'blue'
}),
#dropdown
dcc.Dropdown(
id='drop_down',
options=[{
'label': i,
'value': i
} for i in dfff['Reviews_df'].sample(50)],
optionHeight=100,
searchable=True,
),
html.Button(children='Find Review of the selected data',
id='button_click',
n_clicks=0,
style={
'color': 'Red',
"border": "5px solid black"
}),
html.H1(children=None,
id='result',
style={
'textAlign': 'center',
'color': 'red'
}),
#text_review
dcc.Textarea(
id="textarea_review",
placeholder="Enter the Review here.....",
style={
"width": "100%",
"height": 100,
"background-color": "orange",
"font-size": "160%",
"font-family": "verdana",
"color": "white",
"border": "5px solid black"
},
),
html.Button(children='Find Review of the entered data',
id='button_click1',
n_clicks=0,
style={
'color': 'Red',
"border": "5px solid black"
}),
html.H1(children=None,
id='result1',
style={
'textAlign': 'center',
'color': 'red'
}),
])
return main_layout
import dash_labs as dl
import dash
import numpy as np
import dash_core_components as dcc
import plotly.express as px
app = dash.Dash(__name__, plugins=[dl.plugins.FlexibleCallbacks()])
tpl = dl.templates.FlatDiv()
dropdown = dcc.Dropdown(options=[{
"label": i,
"value": i
} for i in ["sin", "cos", "exp"]],
value="sin")
textbox = dcc.Textarea("Initial Title")
slider = dcc.Slider(min=1,
max=10,
value=2,
tooltip={
"placement": "bottom",
"always_visible": True
})
slider2 = dcc.Slider(min=1,
max=10,
value=6,
tooltip={
"placement": "bottom",
"always_visible": True
})
html.H3('STEP 2', style = {'margin-top':'0'}),
html.Div(
[
html.Div(
[
html.Div(
[
html.P([
'Insert crRNA sequence(s), one per line.',
html.P('Sequences must have the same length and be provided without the PAM sequence') ,
#html.Abbr('\uD83D\uDEC8', style = {'text-decoration':'none'} ,title = 'One sequence per line. All sequences must have the same lenght and PAM characters are not required')
],
style = {'word-wrap': 'break-word'}),
dcc.Textarea(id = 'text-guides', placeholder = 'GAGTCCGAGCAGAAGAAGAA\nCCATCGGTGGCCGTTTGCCC', style = {'width':'450px', 'height':'160px'}),
html.P('or', style = {'position': 'relative', 'left':'50%'}),
html.Div(
[
html.Div(
[
dcc.Upload('Upload file with crRNA sequences', id = 'upload-guides')
],
style={
'width': '100%',
'height': '60px',
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px',
'textAlign': 'center',
'textAlign': 'center',
'color': 'black'
}),
html.Br(),
html.Br(),
])
input_2 = html.Div(
style={'backgroundColor': 'cornsilk'},
children=[
html.Div(
dcc.Textarea(
id="input2",
placeholder="please insert case text... the more the better",
style={
'display': 'block',
'textAlign': 'center',
'width': '50%',
'margin-left': 'auto',
'margin-right': 'auto'
})),
html.Button('Submit',
id='button',
type='submit',
style={
'display': 'block',
'textAlign': 'center',
'width': '50%',
'margin-left': 'auto',
'margin-right': 'auto'
}),
html.Br(),
def layout(code):
return html.Div([
html.Div([
html.Div([
dcc.Dropdown(
id='goo_ba',
options=[{'label': r, 'value': v} for r, v in zip(["Good", "Great", "Bad", "Severe"],
["good", "great", "bad", "severe"])],
value="good",
clearable=False,
className="dropper"
)
], style={'background-color': "white", 'color': 'rgb(217, 224, 236)', 'float': 'left',
'padding-right': '1cm', 'width': '19%'}),
html.Div([
dcc.Dropdown(
id='time',
options=[{'label': r, 'value': v} for r, v in zip(["All Observations","Recent vs Past","Last Six Month", "Last Year","Last Three Years", "Last Five Years","First Five Years"], ["alltime","rvp","six month","year","three_years", "five_years","five_years_ago"])],
value="alltime",
clearable=False,
className="dropper"
)
], style={'background-color': 'white', 'color': 'rgb(217, 224, 236)', 'float': 'left', 'padding-right': '1cm',
'width': '19%'}),
html.Div([
dcc.Dropdown(
id='many',
options=[{'label': r, 'value': v} for r, v in zip(["One Noun","Two Words","Phrase"],["1","2","3"])],
value="3",
clearable=False,
className="dropper"
)
], style={'background-color': 'white', 'padding-right': '1cm', 'color': 'rgb(217, 224, 236)', 'float': 'left',
'width': '19%'}),
html.Div([
dcc.Dropdown(
id='norm',
options=[{'label': r, 'value': v} for r, v in
zip(["Relative", "Count"], ["False","True"])],
value="True",
clearable=False,
className="dropper"
)
], style={'background-color': 'white', 'padding-right': '1cm', 'color': 'rgb(217, 224, 236)', 'float': 'left',
'width': '19%'}),
html.Div([
dcc.Dropdown(
id='bencher',
options=[{'label': r, 'value': v} for r, v in
zip(input_fields["short_name"], input_fields["code_or_ticker"])],
value="BJRI",
clearable=False,
className="dropper"
)
], style={'background-color': 'white', 'padding-right': '1cm', 'color': 'rgb(217, 224, 236)', 'float': 'left',
'width': '19%'}),
# , 'float': 'right', 'display': 'inline-block'
], style={'background-color': 'white', 'padding-left': '1.8cm', 'clear': 'both', 'padding-top': '0.3cm'},
className="double_drop"),
html.Div([dcc.Graph(id='graphed',config={'displayModeBar': False})], style={'clear':'both'}),
html.Div([dcc.Textarea(id='text_sum', placeholder='Summary', value='Component', style={'width': '100%', 'height':'140px'}
)], style={'padding-top':'25px','clear':'both'})
])
