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import gradio as gr
from vega_datasets import data
stocks = data.stocks()
gapminder = data.gapminder()
gapminder = gapminder.loc[
gapminder.country.isin(["Argentina", "Australia", "Afghanistan"])
]
climate = data.climate()
seattle_weather = data.seattle_weather()
## Or generate your own fake data, here's an example for stocks:
#
# import pandas as pd
# import random
#
# stocks = pd.DataFrame(
# {
# "symbol": [
# random.choice(
# [
# "MSFT",
# "AAPL",
# "AMZN",
# "IBM",
# "GOOG",
# ]
# )
# for _ in range(120)
# ],
# "date": [
# pd.Timestamp(year=2000 + i, month=j, day=1)
# for i in range(10)
# for j in range(1, 13)
# ],
# "price": [random.randint(10, 200) for _ in range(120)],
# }
# )
def line_plot_fn(dataset):
if dataset == "stocks":
return gr.LinePlot(
stocks,
x="date",
y="price",
color="symbol",
color_legend_position="bottom",
title="Stock Prices",
tooltip=["date", "price", "symbol"],
height=300,
width=500,
)
elif dataset == "climate":
return gr.LinePlot(
climate,
x="DATE",
y="HLY-TEMP-NORMAL",
y_lim=[250, 500],
title="Climate",
tooltip=["DATE", "HLY-TEMP-NORMAL"],
height=300,
width=500,
)
elif dataset == "seattle_weather":
return gr.LinePlot(
seattle_weather,
x="date",
y="temp_min",
tooltip=["weather", "date"],
overlay_point=True,
title="Seattle Weather",
height=300,
width=500,
)
elif dataset == "gapminder":
return gr.LinePlot(
gapminder,
x="year",
y="life_expect",
color="country",
title="Life expectancy for countries",
stroke_dash="cluster",
x_lim=[1950, 2010],
tooltip=["country", "life_expect"],
stroke_dash_legend_title="Country Cluster",
height=300,
width=500,
)
with gr.Blocks() as line_plot:
with gr.Row():
with gr.Column():
dataset = gr.Dropdown(
choices=["stocks", "climate", "seattle_weather", "gapminder"],
value="stocks",
)
with gr.Column():
plot = gr.LinePlot()
dataset.change(line_plot_fn, inputs=dataset, outputs=plot)
line_plot.load(fn=line_plot_fn, inputs=dataset, outputs=plot)
if __name__ == "__main__":
line_plot.launch()
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