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app.py
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from typing import List, Dict, Tuple
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import matplotlib.colors as mpl_colors
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import seaborn as sns
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import shinyswatch
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from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui
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sns.set_theme()
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www_dir = Path(__file__).parent.resolve() / "www"
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df = pd.read_csv(Path(__file__).parent / "penguins.csv", na_values="NA")
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numeric_cols: List[str] = df.select_dtypes(include=["float64"]).columns.tolist()
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species: List[str] = df["Species"].unique().tolist()
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species.sort()
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app_ui = ui.page_fillable(
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shinyswatch.theme.minty(),
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ui.layout_sidebar(
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ui.sidebar(
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# Artwork by @allison_horst
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ui.input_selectize(
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"xvar",
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"X variable",
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numeric_cols,
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selected="Bill Length (mm)",
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),
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ui.input_selectize(
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"yvar",
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"Y variable",
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numeric_cols,
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selected="Bill Depth (mm)",
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),
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ui.input_checkbox_group(
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"species", "Filter by species", species, selected=species
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),
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ui.hr(),
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ui.input_switch("by_species", "Show species", value=True),
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ui.input_switch("show_margins", "Show marginal plots", value=True),
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),
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ui.output_ui("value_boxes"),
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ui.output_plot("scatter", fill=True),
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ui.help_text(
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"Artwork by ",
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ui.a("@allison_horst", href="https://twitter.com/allison_horst"),
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class_="text-end",
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),
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),
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)
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def server(input: Inputs, output: Outputs, session: Session):
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@reactive.Calc
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def filtered_df() -> pd.DataFrame:
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"""Returns a Pandas data frame that includes only the desired rows"""
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# This calculation "req"uires that at least one species is selected
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req(len(input.species()) > 0)
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# Filter the rows so we only include the desired species
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return df[df["Species"].isin(input.species())]
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@output
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@render.plot
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def scatter():
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"""Generates a plot for Shiny to display to the user"""
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# The plotting function to use depends on whether margins are desired
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plotfunc = sns.jointplot if input.show_margins() else sns.scatterplot
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plotfunc(
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data=filtered_df(),
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x=input.xvar(),
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y=input.yvar(),
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palette=palette,
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hue="Species" if input.by_species() else None,
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hue_order=species,
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legend=False,
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)
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@output
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@render.ui
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def value_boxes():
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df = filtered_df()
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def penguin_value_box(title: str, count: int, bgcol: str, showcase_img: str):
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return ui.value_box(
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title,
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count,
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{"class_": "pt-1 pb-0"},
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showcase=ui.fill.as_fill_item(
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ui.tags.img(
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{"style": "object-fit:contain;"},
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src=showcase_img,
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)
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),
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theme_color=None,
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style=f"background-color: {bgcol};",
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)
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if not input.by_species():
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return penguin_value_box(
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"Penguins",
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len(df.index),
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bg_palette["default"],
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# Artwork by @allison_horst
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showcase_img="penguins.png",
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)
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value_boxes = [
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penguin_value_box(
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name,
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len(df[df["Species"] == name]),
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bg_palette[name],
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# Artwork by @allison_horst
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showcase_img=f"{name}.png",
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)
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for name in species
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# Only include boxes for _selected_ species
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if name in input.species()
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]
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return ui.layout_column_wrap(*value_boxes, width = 1 / len(value_boxes))
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# "darkorange", "purple", "cyan4"
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colors = [[255, 140, 0], [160, 32, 240], [0, 139, 139]]
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colors = [(r / 255.0, g / 255.0, b / 255.0) for r, g, b in colors]
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palette: Dict[str, Tuple[float, float, float]] = {
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"Adelie": colors[0],
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"Chinstrap": colors[1],
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"Gentoo": colors[2],
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"default": sns.color_palette()[0], # type: ignore
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}
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bg_palette = {}
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# Use `sns.set_style("whitegrid")` to help find approx alpha value
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for name, col in palette.items():
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# Adjusted n_colors until `axe` accessibility did not complain about color contrast
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bg_palette[name] = mpl_colors.to_hex(sns.light_palette(col, n_colors=7)[1]) # type: ignore
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app = App(
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app_ui,
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server,
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static_assets=str(www_dir),
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)
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import gradio as gr
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gr.load("models/togethercomputer/Llama-2-7B-32K-Instruct").launch()
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