async def serve(q: Q):
if not q.client.initialized:
q.page['meta'] = ui.meta_card(box='')
q.page['example'] = ui.form_card(box='1 1 11 10', items=[
ui.button(name='show_side_panel', label='Order donuts', primary=True)
])
q.client.initialized = True
else:
if q.args.show_side_panel:
q.page['meta'].side_panel = ui.side_panel(title='Welcome to store', items=[
ui.text('Donuts cost $1.99. Proceed?'),
ui.buttons([ui.button(name='next_step', label='Next', primary=True)])
])
elif q.args.next_step:
q.page['meta'].side_panel.items = [
ui.text('You will be charged $1.99. Proceed?'),
ui.buttons([
ui.button(name='cancel', label='Back to safety'),
ui.button(name='submit', label='Place order', primary=True),
])
]
elif q.args.submit:
q.page['example'].items = [ui.message_bar('success', 'Order placed!')]
q.page['meta'].side_panel = None
elif q.args.cancel:
q.page['example'].items = [ui.message_bar('info', 'Order canceled!')]
q.page['meta'].side_panel = None
await q.page.save()
async def render_customer_selector(q: Q):
clear_page(q)
columns = [
ui.table_column(name=column,
label=column,
sortable=True,
searchable=True)
for column in q.app.customer_df.columns
]
rows = [
ui.table_row(name=str(index), cells=row.tolist())
for index, row in q.app.customer_df.applymap(str).iterrows()
]
q.client.cards.add('customer_table')
q.page['customer_table'] = ui.form_card(
box='customer_table',
items=[
ui.message_bar(text='Click "Status" to review a customer',
type='info'),
ui.table(
name='customer_table',
columns=columns,
rows=rows,
groupable=True,
multiple=False,
)
])
async def serve(q: Q):
if q.args.start:
q.page['form'] = ui.form_card(box='1 1 12 10',
items=[ui.progress('Running...')])
await q.page.save()
seconds = random.randint(1, 6)
# DON'T DO THIS!
# This will make your app unresponsive for some time:
# message = blocking_function(seconds)
# Do this instead:
with concurrent.futures.ThreadPoolExecutor() as pool:
message = await q.exec(pool, blocking_function, seconds)
# You can also pass a ProcessPoolExecutor, like this:
# with concurrent.futures.ProcessPoolExecutor() as pool:
# message = await q.exec(pool, blocking_function, seconds)
q.page['form'] = ui.form_card(box='1 1 12 10',
items=[ui.message_bar('info', message)])
await q.page.save()
else:
q.page['form'] = ui.form_card(
box='1 1 12 10', items=[ui.button(name='start', label='Start')])
await q.page.save()
def get_contrast(color1: str, color2: str, q: Q, min_contrast=4.5):
rgb1 = hex_to_rgb(q.client[color1].lstrip('#'))
rgb2 = hex_to_rgb(q.client[color2].lstrip('#'))
lum1 = get_luminance(rgb1[0], rgb1[1], rgb1[2])
lum2 = get_luminance(rgb2[0], rgb2[1], rgb2[2])
brightest = max(lum1, lum2)
darkest = min(lum1, lum2)
contrast = (brightest + 0.05) / (darkest + 0.05)
if contrast < min_contrast:
return ui.message_bar(
type='error',
text=f'Improve contrast between **{color1}** and **{color2}**.')
else:
return ui.message_bar(
type='success',
text=f'Contrast between **{color1}** and **{color2}** is great!')
async def serve(q: Q):
if q.args.train:
# train WaveML Model using H2O-3 AutoML
copy_expando(q.args, q.client)
q.client.wave_model = build_model(
train_df=q.client.train_df,
target_column='target',
model_type=ModelType.H2O3,
_h2o3_max_runtime_secs=30,
_h2o3_nfolds=2,
_h2o3_include_algos=[q.client.algo]
)
model_id = q.client.wave_model.model.model_id
accuracy = round(100 - q.client.wave_model.model.mean_per_class_error() * 100, 2)
# show training details and prediction option
q.page['example'].items[1].choice_group.value = q.client.algo
q.page['example'].items[2].buttons.items[1].button.disabled = False
q.page['example'].items[3].message_bar.type = 'success'
q.page['example'].items[3].message_bar.text = 'Training successfully completed!'
q.page['example'].items[4].text.content = f'''**H2O AutoML model id:** {model_id} <br />
**Accuracy:** {accuracy}%'''
q.page['example'].items[5].text.content = ''
elif q.args.predict:
# predict on test data
preds = q.client.wave_model.predict(test_df=q.client.test_df)
# show predictions
q.page['example'].items[3].message_bar.text = 'Prediction successfully completed!'
q.page['example'].items[5].text.content = f'''**Example predictions:** <br />
{preds[0]} <br /> {preds[1]} <br /> {preds[2]}'''
else:
# prepare sample train and test dataframes
data = load_wine(as_frame=True)['frame']
q.client.train_df, q.client.test_df = train_test_split(data, train_size=0.8)
# algos
algo_choices = [ui.choice(x, x) for x in ['DRF', 'GLM', 'XGBoost', 'GBM', 'DeepLearning']]
# display ui
q.page['example'] = ui.form_card(
box='1 1 -1 -1',
items=[
ui.text(content='''The sample dataset used is the
<a href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_wine.html" target="_blank">wine dataset</a>.'''),
ui.choice_group(name='algo', label='Select Algo', choices=algo_choices, value='DRF'),
ui.buttons(items=[
ui.button(name='train', label='Train', primary=True),
ui.button(name='predict', label='Predict', primary=True, disabled=True),
]),
ui.message_bar(type='warning', text='Training will take a few seconds'),
ui.text(content=''),
ui.text(content='')
]
)
await q.page.save()
def render_plot_view(q: Q):
"""Sets up the view a file as ui.table card"""
items = [ui.separator(label='Step 3: Visualize the Dataset')]
if q.client.working_file_path is None:
items.append(ui.message_bar(type='warning', text='Please upload a dataset!'))
else:
items.append(make_ui_plot(file_path=q.client.working_file_path))
q.page['plot'] = ui.form_card(box='4 6 8 -1', items=items)
def render_table_view(q: Q):
"""Sets up the view a file as ui.table card"""
items = [ui.separator(label='Step 2: View the Dataset')]
if q.client.working_file_path is None:
items.append(ui.message_bar(type='warning', text='Please upload a dataset!'))
else:
items.append(ui.text_xl(os.path.basename(q.client.working_file_path)))
items.append(make_ui_table(file_path=q.client.working_file_path, n_rows=10, name='head_of_table'))
q.page['table'] = ui.form_card(box='4 2 8 4', items=items)
def render_upload_view(q: Q):
"""Sets up the upload-dataset card"""
q.page['upload'] = ui.form_card(
box='1 2 3 -1',
items=[
ui.separator(label='Step 1: Choose a Dataset'),
ui.message_bar(
type='info',
text='This application requires a .csv file with any type of data within it',
),
ui.file_upload(name='file_upload', label='Upload Data', multiple=False, file_extensions=['csv']),
]
)
async def list_tweets_for_hashtag(q):
values, text = search_tweets(q)
for tweet_count, tweet in enumerate(text):
popularity_score = values[tweet]
q.page[f'twitter_card_{tweet_count}'].items = [
ui.message_bar(type=f"{derive_sentiment_message_type(popularity_score['compound'])}",
text=f"Sentiment - {derive_sentiment_status(popularity_score['compound'])}"),
ui.text(content=tweet[:150].strip()),
ui.frame(content=convert_plot_to_html(
generate_figure_pie_of_target_percent(map_popularity_score_keys(popularity_score)), "cdn", False),
width='300px', height='250px')
]
def form_training_completed(q: Q):
# display when model training is completed
return [
ui.text(content=DATASET_TEXT),
ui.dropdown(name='dai_instance_id', label='Select Driverless AI instance', value=q.client.dai_instance_id,
choices=q.client.choices_dai_instances, required=True),
ui.buttons(items=[
ui.button(name='train', label='Train', primary=True),
ui.button(name='predict', label='Predict', primary=True)
]),
ui.message_bar(type='success', text='Training successfully completed!'),
ui.text(content=q.client.model_details)
]
def form_prediction_completed(q: Q):
# display when model prediction is completed
return [
ui.text(content=DATASET_TEXT),
ui.dropdown(name='dai_instance_id', label='Select Driverless AI instance', value=q.client.dai_instance_id,
choices=q.client.choices_dai_instances, required=True),
ui.dropdown(name='categorical_columns', label='Select categorical columns',
choices=q.client.column_choices, values=q.client.categorical_columns),
ui.buttons(items=[
ui.button(name='train', label='Train', primary=True),
ui.button(name='predict', label='Predict', primary=True)
]),
ui.message_bar(type='success', text='Prediction successfully completed!'),
ui.text(content=q.client.model_details),
ui.text(content=f'''**Example predictions:** <br />
{q.client.preds[0]} <br /> {q.client.preds[1]} <br /> {q.client.preds[2]}''')
]
def form_prediction_completed(q: Q):
# display when model prediction is completed
return [
ui.text(content=DATASET_TEXT),
ui.dropdown(name='dai_instance_id', label='Select Driverless AI instance', value=q.client.dai_instance_id,
choices=q.client.choices_dai_instances, required=True),
ui.slider(name='dai_interpretability', label='Interpretability', min=1, max=10, step=1,
value=q.client.dai_interpretability),
ui.toggle(name='dai_reproducible', label='Reproducible', value=q.client.dai_reproducible),
ui.buttons(items=[
ui.button(name='train', label='Train', primary=True),
ui.button(name='predict', label='Predict', primary=True)
]),
ui.message_bar(type='success', text='Prediction successfully completed!'),
ui.text(content=q.client.model_details),
ui.text(content=f'''**Example predictions:** <br />
{q.client.preds[0]} <br /> {q.client.preds[1]} <br /> {q.client.preds[2]}''')
]
def render_compare_word_cloud(q: Q):
df = filter_data_frame(config.dataset, q.client.filters)
if len(df):
image = plot_word_cloud(merge_to_single_text(df[q.client.review]))
q.page['compare'] = ui.image_card(
box=config.boxes['right_panel'],
title='Word Cloud based on the selected filters',
type='png',
image=image,
)
else:
q.page['compare'] = ui.form_card(
box=config.boxes['right_panel'],
items=[
ui.message_bar(type='warning',
text='No reviews matching filter criteria!')
])
async def serve(q: Q):
if q.args.start:
q.page['form'] = ui.form_card(box='1 1 -1 -1', items=[ui.progress('Running...')])
await q.page.save()
seconds = random.randint(1, 6)
# DON'T DO THIS!
# This will make your app unresponsive for some time:
# message = blocking_function(seconds)
# Do this instead:
message = await q.run(blocking_function, seconds)
q.page['form'] = ui.form_card(box='1 1 -1 -1', items=[ui.message_bar('info', message)])
await q.page.save()
else:
q.page['form'] = ui.form_card(box='1 1 -1 -1', items=[ui.button(name='start', label='Start')])
await q.page.save()
def form_training_completed(q: Q):
# display when model training is completed
return [
ui.text(content=DATASET_TEXT),
ui.dropdown(name='dai_instance_id',
label='Select Driverless AI instance',
value=q.client.dai_instance_id,
choices=q.client.choices_dai_instances,
required=True),
ui.button(name='train',
label='Train',
primary=True,
disabled=q.client.disable_training),
ui.message_bar(type='success',
text='Training successfully completed!'),
ui.text(content=q.client.model_details),
ui.text(content=
f'**Download:** <a href="{q.client.path_autodoc}">AutoDoc</a>')
]
def render_diff_word_cloud(q: Q):
df = config.dataset
for key, value in q.client.filters.items():
df = df[df[key] == value]
if len(df):
q.page['diff'] = ui.image_card(box='content',
title='Diff',
type='png',
image=plot_word_cloud(
df[q.client.review], q))
else:
# TODO: Move into sidebar when https://github.com/h2oai/wave/pull/507 merged.
q.page['diff'] = ui.form_card(
box='content',
items=[
ui.message_bar(type='warning',
text='No reviews matching filter criteria!')
])
def render_home(q: Q):
init(q)
df = predictor.get_testing_data_as_pd_frame()
predicted_df = predictor.get_predict_data_as_pd_frame()
drop_column_from_df(df, 'default.payment.next.month')
add_column_to_df(df, predicted_df, 'Default Prediction Rate', 'predict')
df = round_df_column(df, 'Default Prediction Rate', 4)
q.page["risk_table"] = ui.form_card(
box='risk_table',
items=[
ui.message_bar(text='Double click to review a customer',
type='info'),
ui.table(name='risk_table',
columns=get_column_headers_for_df(df, True),
rows=get_rows(q, df),
groupable=True,
multiple=False,
height="800px")
])
async def serve(q: Q):
if q.args.start: # The button named 'start' was clicked
seconds = 5
# Grab a reference to the form
form = q.page['form']
# Start incrementing the progress bar in the background
future = asyncio.ensure_future(display_progress_bar(q, form, seconds))
# Execute our long-running function in the background
with concurrent.futures.ThreadPoolExecutor() as pool:
message = await q.exec(pool, blocking_function, seconds)
# Stop the progress bar (optional unless we used a infinite while loop in display_progress_bar()).
future.cancel()
# At this point, we're done with the long-running function; so display a completion message
form.items = [ui.message_bar('info', message)]
await q.page.save()
else:
q.page['form'] = ui.form_card(
box='1 1 -1 -1', items=[ui.button(name='start', label='Start')])
await q.page.save()
async def showProducts(q: Q):
global filter_product
global filter_manufacturer
global filter_supplier
global unit_measure
global gqty
items = [ui.tabs(name='menu', value=q.args.menu, items=tabs)]
print('---------------------------')
print('== PRODUCT TAB == ')
print('q.args.show_tables: ' + str(q.args.show_tables) +
', q.args.show_inputs: ' + str(q.args.show_inputs))
## the user have clicked 'Add' button but haven't clicked 'Submit' button yet
if (q.args.show_tables == None or q.args.show_tables == False) and (
q.args.show_inputs or q.args.product or q.args.manufacturer
or q.args.supplier or q.args.qty):
## need to get manufacturer data first (later used for the dropdown when adding new supplier)
df_m = pd.DataFrame(l_manufacturers,
columns=['manufacturer', 'product'])
df_s = pd.DataFrame(l_suppliers,
columns=['supplier', 'manufacturer', 'product'])
## if there is no manufacturer data
if len(df_m) == 0:
items.extend([
ui.message_bar(type='info',
text='Try adding manufacturer first'),
ui.button(name='goto_manufacturer',
label='Close',
primary=True)
])
## if there is no supplier data
elif len(df_s) == 0:
items.extend([
ui.message_bar(type='info', text='Try adding supplier first'),
ui.button(name='goto_supplier', label='Close', primary=True)
])
## if there is data on manufacturer and supplier, when the user click 'Add Product' button
else:
items.append(ui.text_xl(content='Add Product'))
print('q.args.product: ' + str(q.args.product) +
', q.args.manufacturer: ' + str(q.args.manufacturer) +
', q.args.supplier: ' + str(q.args.supplier))
if q.args.product and (filter_product == None
or q.args.product != filter_product):
filter_product = q.args.product
filter_manufacturer = None
filter_supplier = None
unit_measure = None
# selling_price = None
gqty = None
elif q.args.manufacturer and (
filter_manufacturer == None
or q.args.manufacturer != filter_manufacturer):
filter_manufacturer = q.args.manufacturer
filter_supplier = None
unit_measure = None
# selling_price = None
gqty = None
elif q.args.supplier and (filter_supplier == None
or q.args.supplier != filter_supplier):
filter_supplier = q.args.supplier
unit_measure = None
# selling_price = None
gqty = None
if q.args.qty:
gqty = q.args.qty
print(filter_product, filter_manufacturer, filter_supplier)
## PRODUCT
if filter_product != None:
items.append(
ui.dropdown(name='product',
label='Product',
choices=[
ui.choice(name=x, label=x)
for x in df_m['product'].unique()
],
trigger=True,
value=filter_product))
tmp_m = df_m[df_m['product'] ==
q.args.product]['manufacturer'].unique()
else:
items.append(
ui.dropdown(name='product',
label='Product',
choices=[
ui.choice(name=x, label=x)
for x in df_m['product'].unique()
],
trigger=True))
tmp_m = df_m['manufacturer'].unique()
## MANUFACTURER
if filter_manufacturer != None:
items.append(
ui.dropdown(
name='manufacturer',
label='Manufacturer',
choices=[ui.choice(name=x, label=x) for x in tmp_m],
trigger=True,
value=filter_manufacturer))
tmp_s = df_s[(df_s['product'] == filter_product)
& (df_s['manufacturer'] == filter_manufacturer
)]['supplier'].unique()
else:
items.append(
ui.dropdown(
name='manufacturer',
label='Manufacturer',
choices=[ui.choice(name=x, label=x) for x in tmp_m],
trigger=True))
tmp_s = df_s['supplier'].unique()
## SUPPLIER
if filter_supplier != None:
items.append(
ui.dropdown(
name='supplier',
label='Supplier',
choices=[ui.choice(name=x, label=x) for x in tmp_s],
trigger=True,
value=filter_supplier))
# selling_price = df_s[(df_s['product']==filter_product)&(df_s['manufacturer']==filter_manufacturer)
# &(df_s['supplier']==filter_supplier)]['selling_price'].values[0]
# unit_measure = df_s[(df_s['product']==filter_product)&(df_s['manufacturer']==filter_manufacturer)
# &(df_s['supplier']==filter_supplier)]['uom'].values[0]
else:
items.append(
ui.dropdown(
name='supplier',
label='Supplier',
choices=[ui.choice(name=x, label=x) for x in tmp_s],
trigger=True))
# selling_price = None
# items.append(ui.date_picker(name='purchase_date', label='Purchase Date'))
# items.append(ui.textbox(name='qty', label='Qty'))
# ## SELLING PRICE
# if selling_price!=None:
# items.append(ui.textbox(name='selling_price', label='Selling Price', value = str(selling_price)))
# else:
# items.append(ui.textbox(name='selling_price', label='Selling Price'))
# ## UOM
# if unit_measure!=None:
# items.append(ui.textbox(name='uom', label='Unit of Measurement', value = unit_measure, readonly = True))
# else:
# items.append(ui.dropdown(name='uom', label='Unit of Measurement', choices=[
# ui.choice(name=x, label=x) for x in uom
# ]))
# items.extend([
# # ui.date_picker(name='mfg_date', label='Mfg Date'),
# # ui.date_picker(name='exp_date', label='Exp Date'),
# ui.textbox(name='selling_price', label='Selling Price'),
# # ui.textbox(name='operational_cost', label='Operational Cost'),
# # ui.textbox(name='lvl', label='Order Point by level(%)'),
# # ui.textbox(name='schedule', label='Order Point Schedule (days)'),
# # ui.textbox(name='schedule_qty', label='Scheduled Qty'),
# # ui.textbox(name='rate', label='Avg. Consumption'),
# # ui.dropdown(name='freq', label='Consumption Frequency', choices=[
# # ui.choice(name=x, label=x, value='daily', readonly=True) for x in consumption
# # ]),
# ui.button(name='show_tables', label='Submit', primary=True)])
items.extend([
# ui.textbox(name='selling_price', label='Selling Price'),
ui.button(name='show_tables', label='Submit', primary=True)
])
else: ## first iteration goes here
filter_product = None
filter_manufacturer = None
filter_supplier = None
unit_measure = None
selling_price = None
gqty = None
## if q.args.show_tables == True
if q.args.show_tables:
# l_products.append([q.args.product, q.args.manufacturer, q.args.supplier, q.args.purchase_date, q.args.qty,
# q.args.uom, q.args.selling_price, q.args.mfg_date, q.args.exp_date, q.args.selling_price,
# q.args.operational_cost, q.args.lvl, q.args.schedule, q.args.schedule_qty, q.args.rate])
# p = Products(q.args.product, q.args.manufacturer, q.args.supplier, q.args.purchase_date, q.args.qty,
# q.args.uom, q.args.selling_price, q.args.mfg_date, q.args.exp_date, q.args.selling_price,
# q.args.operational_cost, q.args.lvl, q.args.schedule, q.args.schedule_qty, q.args.rate)
# l_products.append([q.args.product, q.args.manufacturer, q.args.supplier,
# q.args.uom])
l_products.append(
[q.args.product, q.args.manufacturer, q.args.supplier])
# p = Products(q.args.product, q.args.manufacturer, q.args.supplier,
# q.args.uom)
p = Products(q.args.product, q.args.manufacturer, q.args.supplier)
products.append(p)
items.append(
ui.message_bar(type='success',
text='You have successfully added a product'))
## if there is a product data
if len(products) > 0:
items.append(
ui.table(
name='products',
columns=column_product_table,
rows=[
ui.table_row(
name=product.id,
# cells=[product.product, product.manufacturer, product.supplier, product.selling_price,
# product.uom]
cells=[
product.product, product.manufacturer,
product.supplier
]) for product in products
],
groupable=True,
downloadable=True,
resettable=True,
height='500px'))
else: ## if there is no product data
items.append(ui.text_l(content='No Product found'))
# items.append(ui.button(name='show_inputs', label='Add Product', primary=True))
items.append(
ui.buttons([
ui.button(name='show_inputs',
label='Add Product',
primary=True),
ui.button(name='delete_button', label='Delete Product'),
ui.button(name='edit_button', label='Edit Product'),
]))
q.page['example'] = ui.form_card(box='1 2 -1 -1', items=items)
await q.page.save()
async def showManufacturers(q: Q):
items = [ui.tabs(name='menu', value=q.args.menu, items=tabs)]
print('---------------------------')
print('== MANUFACTURER TAB == ')
## when user is adding new manufacturer
if q.args.show_inputs:
items.extend([
ui.text_xl(content='Add Manufacturer'),
ui.textbox(name='manufacturer', label='Manufacturer'),
ui.textbox(name='product', label='Product'),
# ui.dropdown(name='uom', label='Unit of Measurement', choices=[
# ui.choice(name=x, label=x) for x in uom
# ]),
ui.button(name='show_tables', label='Submit', primary=True)
])
# ## when user is editing existing manufacturer
# elif q.args.edit_button:
# items.extend([ui.text_xl(content='Edit Manufacturer'),
# ui.textbox(name='manufacturer', label='Manufacturer'),
# # ui.textbox(name='product', label='Product'),
# # ui.dropdown(name='uom', label='Unit of Measurement', choices=[
# # ui.choice(name=x, label=x) for x in uom
# # ]),
# ui.button(name='show_tables', label='Submit', primary=True)])
## default goes here
else:
## user have click has clicked 'Submit' button after adding supplier
if q.args.show_tables:
l_manufacturers.append([q.args.manufacturer, q.args.product])
m = Manufacturers(q.args.manufacturer, q.args.product)
manufacturers.append(m)
items.append(
ui.message_bar(type='success',
text='You have successfully added a product'))
## GET ALL MANUFACTURERS DATA
## default view (if there's a manufacturer data)
if len(manufacturers) > 0:
items.append(
ui.table(name='manufacturers',
columns=column_manufacturer_table,
rows=[
ui.table_row(name=manufacturer.id,
cells=[
manufacturer.manufacturer,
manufacturer.product
]) for manufacturer in manufacturers
],
groupable=True,
downloadable=True,
resettable=True,
height='500px'))
## GET ALL MANUFACTURERS DATA
## default view (if there's no manufacturer data)
else:
items.append(ui.text_l(content='No Manufacturer found'))
# items.append(ui.button(name='show_inputs', label='Add Manufacturer', primary=True))
items.append(
ui.buttons([
ui.button(name='show_inputs',
label='Add Manufacturer',
primary=True),
ui.button(name='delete_button', label='Delete Manufacturer'),
ui.button(name='edit_button', label='Edit Manufacturer'),
]))
q.page['example'] = ui.form_card(box='1 2 -1 -1', items=items)
await q.page.save()
async def showSuppliers(q: Q):
items = [ui.tabs(name='menu', value=q.args.menu, items=tabs)]
print('items in supplier: ' + str(items))
## if user clicks 'Add Supplier'
if q.args.show_inputs:
## need to get manufacturer data first (later used for the dropdown when addiing new supplier)
df_m = pd.DataFrame(l_manufacturers,
columns=['manufacturer', 'product'])
## if there is no Manufacter data, then the user should add manufacturer data first
if len(df_m) == 0:
items.extend([
ui.message_bar(type='info',
text='Try adding manufacturer first'),
ui.button(name='goto_manufacturer',
label='Close',
primary=True)
])
## there's a manufacturer data, then go here
else:
items.extend([
ui.text_xl(content='Add Supplier'),
ui.textbox(name='supplier', label='Supplier'),
ui.dropdown(
name='manufacturer',
label='Manufacturer',
choices=[ ## dropdown existing manufacturers
ui.choice(name=x, label=x)
for x in df_m['manufacturer'].unique()
]),
ui.dropdown(
name='product',
label='Product',
choices=[ ## dropdown existing items
ui.choice(name=x, label=x)
for x in df_m['product'].unique()
]), ## product should based on the what manufacturer have
# ui.dropdown(name='uom', label='Unit of Measurement', choices=[ ## dropdown unit of measurement based on UOM variable above
# ui.choice(name=x, label=x) for x in uom
# ]),
# ui.textbox(name='selling_price', label='Price'),
# ui.textbox(name='lead_time', label='Lead Time(days)'),
# ui.textbox(name='delivery_time', label='Delivery Time(days)'),
ui.button(name='show_tables', label='Submit',
primary=True) ## submit button
])
## user hasn't clicked 'Add suppliers' button
else:
## user have click has clicked 'Submit' button after adding supplier
if q.args.show_tables:
l_suppliers.append(
[q.args.supplier, q.args.manufacturer, q.args.product]) ## !!!
s = Suppliers(q.args.supplier, q.args.manufacturer, q.args.product)
suppliers.append(s)
items.append(
ui.message_bar(type='success',
text='You have successfully added a supplier'))
## default view (if there's suppliers data)
if len(suppliers) > 0:
items.append(
ui.table(name='suppliers',
columns=column_supplier_table,
rows=[
ui.table_row(name=supplier.id,
cells=[
supplier.supplier,
supplier.manufacturer,
supplier.product
]) for supplier in suppliers
],
groupable=True,
downloadable=True,
resettable=True,
height='500px'))
## default view (if there's a supplier data)
else:
items.append(ui.text_l(content='No Supplier found'))
# items.append(ui.button(name='show_inputs', label='Add Supplier', primary=True))
items.append(
ui.buttons([
ui.button(name='show_inputs',
label='Add Supplier',
primary=True),
ui.button(name='delete_button', label='Delete Supplier'),
ui.button(name='edit_button', label='Edit Supplier'),
]))
q.page['example'] = ui.form_card(box='1 2 -1 -1', items=items)
await q.page.save()
async def serve(q: Q):
q.page['example'] = ui.form_card(box='1 1 4 10', items=[
ui.text_xl(content='Extra-large text, for headings.'),
ui.text_l(content='Large text, for sub-headings.'),
ui.text_m(content='Body text, for paragraphs and other content.'),
ui.text_s(content='Small text, for small print.'),
ui.text_xs(content='Extra-small text, for really small print.'),
ui.separator(label='A separator sections forms'),
ui.progress(label='A progress bar'),
ui.progress(label='A progress bar'),
ui.message_bar(type='success', text='Message bar'),
ui.textbox(name='textbox', label='Textbox'),
ui.label(label='Checkboxes'),
ui.checkbox(name='checkbox1', label='A checkbox'),
ui.checkbox(name='checkbox1', label='Another checkbox'),
ui.checkbox(name='checkbox1', label='Yet another checkbox'),
ui.toggle(name='toggle', label='Toggle'),
ui.choice_group(name='choice_group', label='Choice group', choices=[
ui.choice(name=x, label=x) for x in ['Egg', 'Bacon', 'Spam']
]),
ui.checklist(name='checklist', label='Checklist', choices=[
ui.choice(name=x, label=x) for x in ['Egg', 'Bacon', 'Spam']
]),
ui.dropdown(name='dropdown', label='Dropdown', choices=[
ui.choice(name=x, label=x) for x in ['Egg', 'Bacon', 'Spam']
]),
ui.dropdown(name='dropdown', label='Multi-valued Dropdown', values=[], choices=[
ui.choice(name=x, label=x) for x in ['Egg', 'Bacon', 'Spam']
]),
ui.combobox(name='combobox', label='Combobox', choices=['Choice 1', 'Choice 2', 'Choice 3']),
ui.slider(name='slider', label='Slider'),
ui.range_slider(name='range_slider', label='Range slider', max=99),
ui.spinbox(name='spinbox', label='Spinbox'),
ui.date_picker(name='date_picker', label='Date picker'),
ui.color_picker(name='color_picker', label='Color picker'),
ui.buttons([
ui.button(name='primary_button', label='Primary', primary=True),
ui.button(name='standard_button', label='Standard'),
ui.button(name='standard_disabled_button', label='Standard', disabled=True),
]),
ui.file_upload(name='file_upload', label='File upload'),
ui.table(name='table', columns=[
ui.table_column(name='col1', label='Column 1'),
ui.table_column(name='col2', label='Column 2'),
], rows=[
ui.table_row(name='row1', cells=['Text A', 'Text B']),
ui.table_row(name='row2', cells=['Text C', 'Text D']),
ui.table_row(name='row3', cells=['Text E', 'Text F']),
]),
ui.link(label='Link'),
ui.tabs(name='tabs', items=[
ui.tab(name='email', label='Mail', icon='Mail'),
ui.tab(name='events', label='Events', icon='Calendar'),
ui.tab(name='spam', label='Spam'),
]),
ui.expander(name='expander', label='Expander'),
ui.frame(path='https://example.com'),
ui.markup(content=html),
ui.template(
content=menu,
data=pack(dict(dishes=[
dict(name='Spam', price='$2.00'),
dict(name='Ham', price='$3.45'),
dict(name='Eggs', price='$1.75'),
]))
),
ui.picker(name='picker', label='Picker', choices=[
ui.choice('choice1', label='Choice 1'),
ui.choice('choice2', label='Choice 2'),
ui.choice('choice3', label='Choice 3'),
]),
ui.stepper(name='stepper', items=[
ui.step(label='Step 1', icon='MailLowImportance'),
ui.step(label='Step 2', icon='TaskManagerMirrored'),
ui.step(label='Step 3', icon='Cafe'),
]),
ui.visualization(
plot=ui.plot([ui.mark(type='interval', x='=product', y='=price', y_min=0)]),
data=data(fields='product price', rows=[(c, x) for c, x, _ in [f.next() for _ in range(n)]], pack=True),
),
ui.vega_visualization(
specification=spec,
data=data(fields=["a", "b"], rows=[
["A", rnd()], ["B", rnd()], ["C", rnd()],
["D", rnd()], ["E", rnd()], ["F", rnd()],
["G", rnd()], ["H", rnd()], ["I", rnd()]
], pack=True),
),
ui.button(name='show_inputs', label='Submit', primary=True),
])
await q.page.save()
# Form / Message Bar
# Use message bars to indicate relevant status information.
# #form #message_bar
# ---
from h2o_wave import site, ui
page = site['/demo']
page['example'] = ui.form_card(
box='1 1 4 10',
items=[
ui.message_bar(type='blocked', text='This action is blocked.'),
ui.message_bar(type='error', text='This is an error message'),
ui.message_bar(type='warning', text='This is a warning message.'),
ui.message_bar(type='info', text='This is an information message.'),
ui.message_bar(type='success', text='This is an success message.'),
ui.message_bar(type='danger', text='This is a danger message.'),
])
page.save()
# Form / Message Bar
# Use message bars to indicate relevant status information.
# #form #message_bar
# ---
from h2o_wave import site, ui
page = site['/demo']
page['example'] = ui.form_card(
box='1 1 4 10',
items=[
ui.message_bar(type='blocked', text='This action is blocked.'),
ui.message_bar(type='error', text='This is an error message'),
ui.message_bar(type='warning', text='This is a warning message.'),
ui.message_bar(type='info', text='This is an information message.'),
ui.message_bar(type='success', text='This is an success message.'),
ui.message_bar(type='danger', text='This is a danger message.'),
ui.message_bar(type='success',
text='This is a **MARKDOWN** _message_.'),
ui.message_bar(type='success',
text='This is an <b>HTML</b> <i>message</i>.'),
])
page.save()
async def serve(q: Q):
if q.args.train:
# train WaveML Model using H2O-3 AutoML
q.client.wave_model = build_model(
train_df=q.client.train_df,
target_column='target',
model_type=ModelType.H2O3,
_h2o3_max_runtime_secs=5,
_h2o3_nfolds=2,
_h2o3_include_algos=['DRF', 'XGBoost', 'GBM'])
model_id = q.client.wave_model.model.model_id
accuracy = round(q.client.wave_model.model.accuracy()[0][1] * 100, 2)
# show training details and prediction option
q.page['example'].items[1].buttons.items[1].button.disabled = False
q.page['example'].items[2].message_bar.type = 'success'
q.page['example'].items[
2].message_bar.text = 'Training successfully completed!'
q.page['example'].items[
3].text.content = f'''**H2O AutoML model id:** {model_id} <br />
**Accuracy:** {accuracy}%'''
q.page['example'].items[4].text.content = ''
q.page['example'].items[5].text.content = ''
elif q.args.predict:
# predict on test data
preds = q.client.wave_model.predict(test_df=q.client.test_df)
shaps = q.client.wave_model.model.predict_contributions(
H2OFrame(q.client.test_df)).as_data_frame()
# show predictions
q.page['example'].items[
2].message_bar.text = 'Prediction successfully completed!'
q.page['example'].items[
4].text.content = f'''**Example predictions:** <br />
{preds[0]} <br /> {preds[1]} <br /> {preds[2]}'''
q.page['example'].items[
5].text.content = f'''**Example SHAP contributions:** <br />
{shaps.head(3).to_html()}'''
else:
# prepare sample train and test dataframes
data = load_breast_cancer(as_frame=True)['frame']
q.client.train_df, q.client.test_df = train_test_split(data,
train_size=0.8)
# display ui
q.page['example'] = ui.form_card(
box='1 1 -1 -1',
items=[
ui.text(content='''The sample dataset used is the
<a href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html#sklearn.datasets.load_breast_cancer" target="_blank">breast cancer dataset</a>.'''
),
ui.buttons(items=[
ui.button(name='train', label='Train', primary=True),
ui.button(name='predict',
label='Predict',
primary=True,
disabled=True),
]),
ui.message_bar(type='warning',
text='Training will take a few seconds'),
ui.text(content=''),
ui.text(content=''),
ui.text(content='')
])
await q.page.save()
async def serve(q: Q):
if q.args.train:
# train WaveML Model using H2O-3 AutoML
wave_model = build_model(train_df=q.client.train_df,
target_column='target',
model_type=ModelType.H2O3,
_h2o3_max_runtime_secs=5,
_h2o3_nfolds=2)
model_id = wave_model.model.model_id
accuracy = round(100 - wave_model.model.mean_per_class_error() * 100,
2)
# save model to local folder
q.client.path_model = save_model(model=wave_model,
output_dir_path='./mymodelfolder')
# show training details and prediction option
q.page['example'].items[1].buttons.items[1].button.disabled = False
q.page['example'].items[2].message_bar.type = 'success'
q.page['example'].items[
2].message_bar.text = 'Training successfully completed!'
q.page['example'].items[
3].text.content = f'''**H2O AutoML model id:** {model_id} <br />
**Accuracy:** {accuracy}%'''
q.page['example'].items[4].text.content = ''
elif q.args.predict:
# load model from local path
wave_model = load_model(file_path=q.client.path_model)
# predict on test data
preds = wave_model.predict(test_df=q.client.test_df)
# show predictions
q.page['example'].items[
2].message_bar.text = 'Prediction successfully completed!'
q.page['example'].items[
4].text.content = f'''**Example predictions:** <br />
{preds[0]} <br /> {preds[1]} <br /> {preds[2]}'''
else:
# prepare sample train and test dataframes
data = load_wine(as_frame=True)['frame']
q.client.train_df, q.client.test_df = train_test_split(data,
train_size=0.8)
# display ui
q.page['example'] = ui.form_card(
box='1 1 -1 -1',
items=[
ui.text(content='''The sample dataset used is the
<a href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_wine.html" target="_blank">wine dataset</a>.'''
),
ui.buttons(items=[
ui.button(name='train', label='Train', primary=True),
ui.button(name='predict',
label='Predict',
primary=True,
disabled=True),
]),
ui.message_bar(type='warning',
text='Training will take a few seconds'),
ui.text(content=''),
ui.text(content='')
])
await q.page.save()
def add_error_message(q, type, message):
q.page['error_message'] = ui.form_card(box='1 5 2 2',
items=[
ui.message_bar(type=type,
text=message),
])
