Flagging

Description

A Gradio Interface includes a ‘Flag’ button that appears underneath the output. By default, clicking on the Flag button sends the input and output data back to the machine where the gradio demo is running, and saves it to a CSV log file. But this default behavior can be changed. To set what happens when the Flag button is clicked, you pass an instance of a subclass of FlaggingCallback to the flagging_callback parameter in the Interface constructor. You can use one of the FlaggingCallback subclasses that are listed below, or you can create your own, which lets you do whatever you want with the data that is being flagged.

SimpleCSVLogger

gradio.SimpleCSVLogger(···)

Description

A simplified implementation of the FlaggingCallback abstract class provided for illustrative purposes. Each flagged sample (both the input and output data) is logged to a CSV file on the machine running the gradio app.

Example Usage

import gradio as gr
def image_classifier(inp):
    return {'cat': 0.3, 'dog': 0.7}
demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label",
                    flagging_callback=SimpleCSVLogger())

CSVLogger

gradio.CSVLogger(···)

Description

The default implementation of the FlaggingCallback abstract class in gradio>=5.0. Each flagged sample (both the input and output data) is logged to a CSV file with headers on the machine running the gradio app. Unlike ClassicCSVLogger, this implementation is concurrent-safe and it creates a new dataset file every time the headers of the CSV (derived from the labels of the components) change. It also only creates columns for "username" and "flag" if the flag_option and username are provided, respectively.

Example Usage

import gradio as gr
def image_classifier(inp):
    return {'cat': 0.3, 'dog': 0.7}
demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label",
                    flagging_callback=CSVLogger())

Initialization

simplify_file_data: bool

verbose: bool

dataset_file_name: str | None

Guides