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# Create app for demo-drift-detection root.py
"""
The rootpage of the application.
Page content is imported from the root.md file.
Please refer to https://docs.taipy.io/en/latest/manuals/gui/pages for more details.
"""
from taipy.gui import Markdown
root_page = Markdown("pages/root.md")
|
# Create app for demo-drift-detection Drift.py
"""
A page of the application.
Page content is imported from the Drift.md file.
Please refer to https://docs.taipy.io/en/latest/manuals/gui/pages for more details.
"""
import taipy as tp
from taipy.gui import Markdown
import pandas as pd
from taipy.gui import notify
fro... |
# Create app for demo-drift-detection Drift.md
<|layout|columns=1 1|
<|part|class_name=card|
### Select Reference Data<br/>
<|{ref_selected}|selector|lov=data_ref;data_noisy;data_female;data_big|dropdown|on_change=on_ref_change|>
|>
<|part|class_name=card|
### Select Comparison Data<br/>
<|{compare_selected}|selector|... |
# Create app for demo-covid-dashboard main.py
from taipy.gui import Gui
import taipy as tp
from pages.country.country import country_md
from pages.world.world import world_md
from pages.map.map import map_md
from pages.predictions.predictions import predictions_md, selected_scenario
from pages.root import root, select... |
# Create app for demo-covid-dashboard config.py
from taipy.config import Config, Scope
import datetime as dt
from algos.algos import add_features, create_train_data, preprocess,\
train_arima, train_linear_regression,\
forecast, forecast_linear_regression,\
... |
# Create app for demo-covid-dashboard algos.py
import pandas as pd
from sklearn.linear_model import LinearRegression
import datetime as dt
import numpy as np
from pmdarima import auto_arima
def add_features(data):
dates = pd.to_datetime(data["Date"])
data["Months"] = dates.dt.month
data["Days"] = dates.d... |
# Create app for demo-covid-dashboard data.py
import pandas as pd
path_to_data = "data/covid-19-all.csv"
data = pd.read_csv(path_to_data, low_memory=False) |
# Create app for demo-covid-dashboard root.md
<|toggle|theme|>
<center>
<|navbar|>
</center>
|
# Create app for demo-covid-dashboard root.py
from taipy.gui import Markdown
import numpy as np
from data.data import data
selector_country = list(np.sort(data['Country/Region'].astype(str).unique()))
selected_country = 'France'
root = Markdown("pages/root.md") |
# Create app for demo-covid-dashboard world.py
from taipy.gui import Markdown
import numpy as np
import json
from data.data import data
type_selector = ['Absolute', 'Relative']
selected_type = type_selector[0]
def initialize_world(data):
data_world = data.groupby(["Country/Region",
... |
# Create app for demo-covid-dashboard world.md
# **World**{: .color-primary} Statistics
<br/>
<|layout|columns=1 1 1 1|gap=50px|
<|card|
**Deaths**{: .color-primary}
<|{'{:,}'.format(int(np.sum(data_world_pie_absolute['Deaths']))).replace(',', ' ')}|text|class_name=h2|>
|>
<|card|
**Recovered**{: .color-primary}
<|{'... |
# Create app for demo-covid-dashboard map.md
# **Map**{: .color-primary} Statistics
<|{data_province_displayed}|chart|type=scattermapbox|lat=Latitude|lon=Longitude|marker={marker_map}|layout={layout_map}|text=Text|mode=markers|height=800px|options={options}|>
|
# Create app for demo-covid-dashboard map.py
import numpy as np
from taipy.gui import Markdown
from data.data import data
marker_map = {"color":"Deaths", "size": "Size", "showscale":True, "colorscale":"Viridis"}
layout_map = {
"dragmode": "zoom",
"mapbox": { "style": "open-street-map", "cente... |
# Create app for demo-covid-dashboard country.md
# **Country**{: .color-primary} Statistics
<|layout|columns=1 1 1|
<|{selected_country}|selector|lov={selector_country}|on_change=on_change_country|dropdown|label=Country|>
<|{selected_representation}|toggle|lov={representation_selector}|on_change=convert_density|>
|>
... |
# Create app for demo-covid-dashboard country.py
import numpy as np
import pandas as pd
from taipy.gui import Markdown
from data.data import data
selected_country = 'France'
data_country_date = None
representation_selector = ['Cumulative', 'Density']
selected_representation = representation_selector[0]
layout = {'... |
# Create app for demo-covid-dashboard predictions.py
from taipy.gui import Markdown, notify
import datetime as dt
selected_data_node = None
selected_scenario = None
selected_date = None
default_result = {"Date": [dt.datetime(2020,10,1)], "Deaths": [0], "ARIMA": [0], "Linear Regression": [0]}
def on_submission_chang... |
# Create app for demo-covid-dashboard predictions.md
<|layout|columns=2 9|gap=50px|
<sidebar|sidebar|
**Scenario** Creation
<|{selected_scenario}|scenario_selector|>
|sidebar>
<scenario|part|render={selected_scenario}|
# **Prediction**{: .color-primary} page
<|1 1|layout|
<date|
#### First **day**{: .color-pri... |
# Create app for demo-yearly-prediction main.py
from config.config import configure
from pages import scenario_page
from pages.root import root, selected_scenario, selected_data_node, content
import taipy as tp
from taipy import Core, Gui, Config
def on_init(state):
...
def on_change(state, var, val):
if v... |
# Create app for demo-yearly-prediction config.py
from taipy import Config
from taipy.config import Frequency, Scope
from algos import clean_data, filter_data, predict
def configure():
historical_data_cfg = Config.configure_data_node(
"historical_data",
storage_type="csv",
default_path="hi... |
# Create app for demo-yearly-prediction __init__.py
|
# Create app for demo-yearly-prediction algos.py
import pandas as pd
import statsmodels.api as sm
from sklearn.linear_model import LinearRegression
def clean_data(historical_data: pd.DataFrame) -> pd.DataFrame:
"""
Transforms sales data into total sales per month
Args:
historical_data: historical... |
# Create app for demo-yearly-prediction __init__.py
from .algos import clean_data, filter_data, predict
|
# Create app for demo-yearly-prediction root.md
<|layout|columns=1 5|
<|sidebar|
<|{selected_scenario}|scenario_selector|>
<|part|render={selected_scenario}|
<|{selected_data_node}|data_node_selector|not display_cycles|>
|>
|>
<|part|class_name=main|render={selected_scenario}|
<|content|>
|>
|>
|
# Create app for demo-yearly-prediction __init__.py
from .scenario_page import scenario_page
|
# Create app for demo-yearly-prediction root.py
from taipy.gui import Markdown
selected_scenario = None
selected_data_node = None
content = ""
root = Markdown("pages/root.md")
|
# Create app for demo-yearly-prediction scenario_page.py
from taipy.gui import Markdown
from .data_node_management import manage_partial
def manage_data_node_partial(state):
manage_partial(state)
scenario_page = Markdown("pages/scenario_page/scenario_page.md")
|
# Create app for demo-yearly-prediction __init__.py
from .scenario_page import scenario_page
|
# Create app for demo-yearly-prediction data_node_management.py
# build partial content for a specific data node
def build_dn_partial(dn, dn_label):
partial_content = "<|part|render={selected_scenario}|\n\n"
# #####################################################################################################... |
# Create app for demo-yearly-prediction scenario_page.md
<|layout|columns=1 1|
<|part|render={selected_scenario}|
<|{selected_scenario}|scenario|not expandable|expanded|>
<|{selected_scenario}|scenario_dag|>
|>
<|part|partial={data_node_partial}|render={selected_data_node}|>
|>
|
# Create app for demo-image-classification-part-1 demo-image_classifcation-taipy-cloud.py
import tensorflow as tf
from tensorflow.keras import layers, models
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.utils import to_categorical
import pandas as pd
import matplotl... |
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