import altair as alt import gradio as gr import numpy as np import pandas as pd from vega_datasets import data def make_plot(plot_type): if plot_type == "scatter_plot": cars = data.cars() return alt.Chart(cars).mark_point().encode( x='Horsepower', y='Miles_per_Gallon', color='Origin', ) elif plot_type == "heatmap": # Compute x^2 + y^2 across a 2D grid x, y = np.meshgrid(range(-5, 5), range(-5, 5)) z = x ** 2 + y ** 2 # Convert this grid to columnar data expected by Altair source = pd.DataFrame({'x': x.ravel(), 'y': y.ravel(), 'z': z.ravel()}) return alt.Chart(source).mark_rect().encode( x='x:O', y='y:O', color='z:Q' ) elif plot_type == "us_map": states = alt.topo_feature(data.us_10m.url, 'states') source = data.income.url return alt.Chart(source).mark_geoshape().encode( shape='geo:G', color='pct:Q', tooltip=['name:N', 'pct:Q'], facet=alt.Facet('group:N', columns=2), ).transform_lookup( lookup='id', from_=alt.LookupData(data=states, key='id'), as_='geo' ).properties( width=300, height=175, ).project( type='albersUsa' ) elif plot_type == "interactive_barplot": source = data.movies.url pts = alt.selection(type="single", encodings=['x']) rect = alt.Chart(data.movies.url).mark_rect().encode( alt.X('IMDB_Rating:Q', bin=True), alt.Y('Rotten_Tomatoes_Rating:Q', bin=True), alt.Color('count()', scale=alt.Scale(scheme='greenblue'), legend=alt.Legend(title='Total Records') ) ) circ = rect.mark_point().encode( alt.ColorValue('grey'), alt.Size('count()', legend=alt.Legend(title='Records in Selection') ) ).transform_filter( pts ) bar = alt.Chart(source).mark_bar().encode( x='Major_Genre:N', y='count()', color=alt.condition(pts, alt.ColorValue("steelblue"), alt.ColorValue("grey")) ).properties( width=550, height=200 ).add_selection(pts) plot = alt.vconcat( rect + circ, bar ).resolve_legend( color="independent", size="independent" ) return plot elif plot_type == "radial": source = pd.DataFrame({"values": [12, 23, 47, 6, 52, 19]}) base = alt.Chart(source).encode( theta=alt.Theta("values:Q", stack=True), radius=alt.Radius("values", scale=alt.Scale(type="sqrt", zero=True, rangeMin=20)), color="values:N", ) c1 = base.mark_arc(innerRadius=20, stroke="#fff") c2 = base.mark_text(radiusOffset=10).encode(text="values:Q") return c1 + c2 elif plot_type == "multiline": source = data.stocks() highlight = alt.selection(type='single', on='mouseover', fields=['symbol'], nearest=True) base = alt.Chart(source).encode( x='date:T', y='price:Q', color='symbol:N' ) points = base.mark_circle().encode( opacity=alt.value(0) ).add_selection( highlight ).properties( width=600 ) lines = base.mark_line().encode( size=alt.condition(~highlight, alt.value(1), alt.value(3)) ) return points + lines with gr.Blocks() as demo: button = gr.Radio(label="Plot type", choices=['scatter_plot', 'heatmap', 'us_map', 'interactive_barplot', "radial", "multiline"], value='scatter_plot') plot = gr.Plot(label="Plot") button.change(make_plot, inputs=button, outputs=[plot]) demo.load(make_plot, inputs=[button], outputs=[plot]) if __name__ == "__main__": demo.launch()

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