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import pandas as pd
from random import randint, random
import gradio as gr
temp_sensor_data = pd.DataFrame(
{
"time": pd.date_range("2021-01-01", end="2021-01-05", periods=200),
"temperature": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],
"humidity": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],
"location": ["indoor", "outdoor"] * 100,
}
)
food_rating_data = pd.DataFrame(
{
"cuisine": [["Italian", "Mexican", "Chinese"][i % 3] for i in range(100)],
"rating": [random() * 4 + 0.5 * (i % 3) for i in range(100)],
"price": [randint(10, 50) + 4 * (i % 3) for i in range(100)],
"wait": [random() for i in range(100)],
}
)
with gr.Blocks() as scatter_plots:
with gr.Row():
start = gr.DateTime("2021-01-01 00:00:00", label="Start")
end = gr.DateTime("2021-01-05 00:00:00", label="End")
apply_btn = gr.Button("Apply", scale=0)
with gr.Row():
group_by = gr.Radio(["None", "30m", "1h", "4h", "1d"], value="None", label="Group by")
aggregate = gr.Radio(["sum", "mean", "median", "min", "max"], value="sum", label="Aggregation")
temp_by_time = gr.ScatterPlot(
temp_sensor_data,
x="time",
y="temperature",
)
temp_by_time_location = gr.ScatterPlot(
temp_sensor_data,
x="time",
y="temperature",
color="location",
)
time_graphs = [temp_by_time, temp_by_time_location]
group_by.change(
lambda group: [gr.ScatterPlot(x_bin=None if group == "None" else group)] * len(time_graphs),
group_by,
time_graphs
)
aggregate.change(
lambda aggregate: [gr.ScatterPlot(y_aggregate=aggregate)] * len(time_graphs),
aggregate,
time_graphs
)
price_by_cuisine = gr.ScatterPlot(
food_rating_data,
x="cuisine",
y="price",
)
with gr.Row():
price_by_rating = gr.ScatterPlot(
food_rating_data,
x="rating",
y="price",
color="wait",
show_actions_button=True,
)
price_by_rating_color = gr.ScatterPlot(
food_rating_data,
x="rating",
y="price",
color="cuisine",
)
if __name__ == "__main__":
scatter_plots.launch()
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