rest

vizro-core/examples/from_json/app.py

@@ -1,65 +1,14 @@
-"""Example to show dashboard configuration specified as a JSON file."""
-import json
-from pathlib import Path
-
-import pandas as pd
-
-import vizro.plotly.express as px
 from vizro import Vizro
+from vizro.integrations import kedro as kedro_integration
 from vizro.managers import data_manager
 from vizro.models import Dashboard
 
+# /Users/lingyi_zhang/vizx/os/Cortex-dev/src/packages/growth/churn/cortex_churn_pipe/src/churn_pipe/config/demo/catalog/catalog_churn.yml
+catalog = kedro_integration.catalog_from_project("/Users/lingyi_zhang/vizx/os/Cortex-dev/src/packages/growth/churn/cortex_churn_pipe/src/churn_pipe/")  # replace with your own path
+for dataset_name, dataset in kedro_integration.datasets_from_catalog(catalog).items():
+    data_manager[dataset_name] = dataset
 
-def retrieve_gapminder():
-    """This is a function that returns gapminder data."""
-    return px.data.gapminder()
-
-
-def retrieve_gapminder_year(year: int):
-    """This is a function that returns gapminder data for a year."""
-    return px.data.gapminder().query(f"year == {year}")
-
-
-def retrieve_gapminder_continent_comparison():
-    """This is a function adds aggregated continent information to gapminder data."""
-    df_gapminder = px.data.gapminder()
-    df_gapminder_agg = px.data.gapminder()
-
-    df_gapminder_agg["lifeExp"] = df_gapminder_agg.groupby(by=["continent", "year"])["lifeExp"].transform("mean")
-    df_gapminder_agg["gdpPercap"] = df_gapminder_agg.groupby(by=["continent", "year"])["gdpPercap"].transform("mean")
-    df_gapminder_agg["pop"] = df_gapminder_agg.groupby(by=["continent", "year"])["pop"].transform("sum")
-
-    df_gapminder["data"] = "Country"
-    df_gapminder_agg["data"] = "Continent"
-
-    df_gapminder_comp = pd.concat([df_gapminder_agg, df_gapminder], ignore_index=True)
-
-    return df_gapminder_comp
-
-
-def retrieve_avg_gapminder():
-    """This is a function that returns aggregated gapminder data."""
-    df = px.data.gapminder()
-    mean = (
-        df.groupby(by=["continent", "year"]).agg({"lifeExp": "mean", "pop": "mean", "gdpPercap": "mean"}).reset_index()
-    )
-    return mean
-
-
-def retrieve_avg_gapminder_year(year: int):
-    """This is a function that returns aggregated gapminder data for a specific year."""
-    return retrieve_avg_gapminder().query(f"year == {year}")
-
-
-# If you're not interested in lazy loading then you could just do data_manager["gapminder"] = px.data.gapminder()
-data_manager["gapminder"] = retrieve_gapminder
-data_manager["gapminder_2007"] = lambda: retrieve_gapminder_year(2007)
-data_manager["gapminder_avg"] = retrieve_avg_gapminder
-data_manager["gapminder_avg_2007"] = lambda: retrieve_avg_gapminder_year(2007)
-data_manager["gapminder_country_analysis"] = retrieve_gapminder_continent_comparison
-
-dashboard = json.loads(Path("dashboard.json").read_text(encoding="utf-8"))
-dashboard = Dashboard(**dashboard)
+dashboard = Dashboard.parse_file("dashboard.json")
 
 if __name__ == "__main__":
-    Vizro(assets_folder="../assets").build(dashboard).run()
+    Vizro().build(dashboard).run()