tags: [gradio-custom-component, DateTime] title: gradio_datetimerange short_description: Component to create time ranges. colorFrom: blue colorTo: yellow sdk: gradio pinned: false app_file: space.py

gradio_datetimerange

Component to create time ranges.

Installation

pip install gradio_datetimerange

Usage

import gradio as gr
from gradio_datetimerange import DateTimeRange
import pandas as pd
from random import randint

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,
    }
)

with gr.Blocks() as demo:
    date = DateTimeRange(["2021-01-01 00:00:00", "2021-01-07 00:00:00"])
    merged_temp_plot = gr.LinePlot(
        temp_sensor_data,
        x="time",
        y="temperature",
    )
    split_temp_plot = gr.LinePlot(
        temp_sensor_data,
        x="time",
        y="temperature",
        color="location",
    )
    with gr.Row():
        humidity_bar_plot = gr.BarPlot(
            temp_sensor_data,
            x="time",
            y="humidity",
            color="location",
            x_bin="1h",
        )
        humidity_scatter_plot = gr.ScatterPlot(
            temp_sensor_data,
            x="time",
            y="humidity",
            color="location",
        )
    
    date.bind([merged_temp_plot, split_temp_plot, humidity_bar_plot, humidity_scatter_plot])


if __name__ == "__main__":
    demo.launch()

DateTimeRange

Initialization

tuple[float | str | datetime, float | str | datetime] | None

bool

Literal["timestamp", "datetime", "string"]

str | None

list[str] | None

str | None

bool | None

str | None

float | None

int | None

int

bool

str | None

list[str] | str | None

bool

int | str | None

Events

User function

The impact on the users predict function varies depending on whether the component is used as an input or output for an event (or both).

• When used as an Input, the component only impacts the input signature of the user function.
• When used as an output, the component only impacts the return signature of the user function.

The code snippet below is accurate in cases where the component is used as both an input and an output.

• As output: Is passed, passes text value as a {str} into the function.
• As input: Should return, expects a tuple pair of datetimes.

def predict(
    value: tuple[str | float | datetime, str | float | datetime] | None
) -> tuple[float | datetime | str, float | datetime | str] | None:
    return value