gradio/demo/outbreak_forecast/run.py

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from math import sqrt
import matplotlib
matplotlib.use('Agg')
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import matplotlib.pyplot as plt
import numpy as np
import plotly.express as px
import pandas as pd
import bokeh.plotting as bk
from bokeh.models import ColumnDataSource
from bokeh.embed import json_item
import gradio as gr
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def outbreak(plot_type, r, month, countries, social_distancing):
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months = ["January", "February", "March", "April", "May"]
m = months.index(month)
start_day = 30 * m
final_day = 30 * (m + 1)
x = np.arange(start_day, final_day + 1)
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pop_count = {"USA": 350, "Canada": 40, "Mexico": 300, "UK": 120}
if social_distancing:
r = sqrt(r)
df = pd.DataFrame({'day': x})
for country in countries:
df[country] = ( x ** (r) * (pop_count[country] + 1))
if plot_type == "Matplotlib":
fig = plt.figure()
plt.plot(df['day'], df[countries].to_numpy())
plt.title("Outbreak in " + month)
plt.ylabel("Cases")
plt.xlabel("Days since Day 0")
plt.legend(countries)
return fig
elif plot_type == "Plotly":
fig = px.line(df, x='day', y=countries)
fig.update_layout(title="Outbreak in " + month,
xaxis_title="Cases",
yaxis_title="Days Since Day 0")
return fig
else:
source = ColumnDataSource(df)
p = bk.figure(title="Outbreak in " + month, x_axis_label="Cases", y_axis_label="Days Since Day 0")
for country in countries:
p.line(x='day', y=country, line_width=2, source=source)
item_text = json_item(p, "plotDiv")
return item_text
inputs = [
gr.Dropdown(["Matplotlib", "Plotly", "Bokeh"], label="Plot Type"),
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gr.Slider(1, 4, 3.2, label="R"),
gr.Dropdown(["January", "February", "March", "April", "May"], label="Month"),
gr.CheckboxGroup(["USA", "Canada", "Mexico", "UK"], label="Countries",
value=["USA", "Canada"]),
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gr.Checkbox(label="Social Distancing?"),
]
outputs = gr.Plot()
demo = gr.Interface(fn=outbreak, inputs=inputs, outputs=outputs, examples=[
["Matplotlib", 2, "March", ["Mexico", "UK"], True],
["Plotly", 3.6, "February", ["Canada", "Mexico", "UK"], False],
["Bokeh", 1.2, "May", ["UK"], True]
], cache_examples=True)
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if __name__ == "__main__":
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demo.launch()