# from pathlib import Path
# from typing import List, Dict, Tuple
# import matplotlib.colors as mpl_colors

# import pandas as pd
# import seaborn as sns
# import shinyswatch

# from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui

# sns.set_theme()

# www_dir = Path(__file__).parent.resolve() / "www"

# df = pd.read_csv(Path(__file__).parent / "penguins.csv", na_values="NA")
# numeric_cols: List[str] = df.select_dtypes(include=["float64"]).columns.tolist()
# species: List[str] = df["Species"].unique().tolist()
# species.sort()

# app_ui = ui.page_fillable(
#     shinyswatch.theme.minty(),
#     ui.layout_sidebar(
#         ui.sidebar(
#             # Artwork by @allison_horst
#             ui.input_selectize(
#                 "xvar",
#                 "X variable",
#                 numeric_cols,
#                 selected="Bill Length (mm)",
#             ),
#             ui.input_selectize(
#                 "yvar",
#                 "Y variable",
#                 numeric_cols,
#                 selected="Bill Depth (mm)",
#             ),
#             ui.input_checkbox_group(
#                 "species", "Filter by species", species, selected=species
#             ),
#             ui.hr(),
#             ui.input_switch("by_species", "Show species", value=True),
#             ui.input_switch("show_margins", "Show marginal plots", value=True),
#         ),
#         ui.output_ui("value_boxes"),
#         ui.output_plot("scatter", fill=True),
#         ui.help_text(
#             "Artwork by ",
#             ui.a("@allison_horst", href="https://twitter.com/allison_horst"),
#             class_="text-end",
#         ),
#     ),
# )


# def server(input: Inputs, output: Outputs, session: Session):
#     @reactive.Calc
#     def filtered_df() -> pd.DataFrame:
#         """Returns a Pandas data frame that includes only the desired rows"""

#         # This calculation "req"uires that at least one species is selected
#         req(len(input.species()) > 0)

#         # Filter the rows so we only include the desired species
#         return df[df["Species"].isin(input.species())]

#     @output
#     @render.plot
#     def scatter():
#         """Generates a plot for Shiny to display to the user"""

#         # The plotting function to use depends on whether margins are desired
#         plotfunc = sns.jointplot if input.show_margins() else sns.scatterplot

#         plotfunc(
#             data=filtered_df(),
#             x=input.xvar(),
#             y=input.yvar(),
#             palette=palette,
#             hue="Species" if input.by_species() else None,
#             hue_order=species,
#             legend=False,
#         )

#     @output
#     @render.ui
#     def value_boxes():
#         df = filtered_df()

#         def penguin_value_box(title: str, count: int, bgcol: str, showcase_img: str):
#             return ui.value_box(
#                 title,
#                 count,
#                 {"class_": "pt-1 pb-0"},
#                 showcase=ui.fill.as_fill_item(
#                     ui.tags.img(
#                         {"style": "object-fit:contain;"},
#                         src=showcase_img,
#                     )
#                 ),
#                 theme_color=None,
#                 style=f"background-color: {bgcol};",
#             )

#         if not input.by_species():
#             return penguin_value_box(
#                 "Penguins",
#                 len(df.index),
#                 bg_palette["default"],
#                 # Artwork by @allison_horst
#                 showcase_img="penguins.png",
#             )

#         value_boxes = [
#             penguin_value_box(
#                 name,
#                 len(df[df["Species"] == name]),
#                 bg_palette[name],
#                 # Artwork by @allison_horst
#                 showcase_img=f"{name}.png",
#             )
#             for name in species
#             # Only include boxes for _selected_ species
#             if name in input.species()
#         ]

#         return ui.layout_column_wrap(*value_boxes, width = 1 / len(value_boxes))


# # "darkorange", "purple", "cyan4"
# colors = [[255, 140, 0], [160, 32, 240], [0, 139, 139]]
# colors = [(r / 255.0, g / 255.0, b / 255.0) for r, g, b in colors]

# palette: Dict[str, Tuple[float, float, float]] = {
#     "Adelie": colors[0],
#     "Chinstrap": colors[1],
#     "Gentoo": colors[2],
#     "default": sns.color_palette()[0],  # type: ignore
# }

# bg_palette = {}
# # Use `sns.set_style("whitegrid")` to help find approx alpha value
# for name, col in palette.items():
#     # Adjusted n_colors until `axe` accessibility did not complain about color contrast
#     bg_palette[name] = mpl_colors.to_hex(sns.light_palette(col, n_colors=7)[1])  # type: ignore


# app = App(
#     app_ui,
#     server,
#     static_assets=str(www_dir),
# )

from data import get_data
get_data()