Progress indicators

Gradio supports the ability to create custom progress bars so that you have customizability and control over the progress update that you show to the user.

Basic usage

To enable progress bars, add an argument to your function that has a default value of a gr.Progress instance. Then you can update the progress levels by calling this instance directly with a float between 0 and 1.

import gradio as gr
import time

def slowly_reverse(word, progress=gr.Progress()):
    progress(0, desc="Starting")
    time.sleep(1)
    progress(0.05)
    new_string = ""
    for letter in progress.tqdm(word, desc="Reversing"):
        time.sleep(0.25)
        new_string = letter + new_string
    return new_string

demo = gr.Interface(slowly_reverse, gr.Text(), gr.Text())
demo.launch()

In this example, the progress bar will:

Start at 0% with the description “Starting”
Update to 5% after a brief pause
Show incremental progress with the description “Reversing” as it processes each letter

Using the tqdm method

The Progress instance provides a tqdm() method that makes it easy to track progress over an iterable. This method works similarly to the popular tqdm library:

import gradio as gr
import time

def process_items(items, progress=gr.Progress()):
    results = []
    for item in progress.tqdm(items, desc="Processing items"):
        time.sleep(0.1)
        results.append(item * 2)
    return results

demo = gr.Interface(
    process_items,
    gr.Textbox(lines=5, placeholder="Enter items, one per line"),
    gr.Textbox()
)
demo.launch()

Automatic tqdm integration

If you use the tqdm library, you can even report progress updates automatically from any tqdm.tqdm that already exists within your function by setting the default argument as gr.Progress(track_tqdm=True):

import gradio as gr
import time
from tqdm import tqdm

def process_with_tqdm(n, progress=gr.Progress(track_tqdm=True)):
    results = []
    for i in tqdm(range(n)):
        time.sleep(0.1)
        results.append(i ** 2)
    return sum(results)

demo = gr.Interface(process_with_tqdm, gr.Slider(1, 100, step=1), gr.Number())
demo.launch()

With track_tqdm=True, you don’t need to manually update the progress - Gradio automatically tracks any tqdm progress bars in your function.

Progress bar parameters

When calling the Progress instance, you can provide:

Float value (0-1): The progress level as a decimal between 0 and 1
desc parameter: A description string to display with the progress bar

def my_function(data, progress=gr.Progress()):
    progress(0.25, desc="Loading data")
    # Load data...
    progress(0.50, desc="Processing data")
    # Process data...
    progress(0.75, desc="Generating results")
    # Generate results...
    progress(1.0, desc="Complete")
    return results

Multi-step progress tracking

For complex operations with multiple stages, you can combine direct progress updates with the tqdm() method:

import gradio as gr
import time

def multi_step_process(items, progress=gr.Progress()):
    # Step 1: Validation
    progress(0, desc="Validating input")
    time.sleep(1)
    progress(0.1)
    
    # Step 2: Processing
    results = []
    for item in progress.tqdm(items[:len(items)//2], desc="Phase 1"):
        time.sleep(0.1)
        results.append(item)
    
    # Step 3: Final processing
    for item in progress.tqdm(items[len(items)//2:], desc="Phase 2"):
        time.sleep(0.1)
        results.append(item * 2)
    
    progress(1.0, desc="Complete")
    return results

demo = gr.Interface(multi_step_process, gr.Textbox(), gr.Textbox())
demo.launch()

Progress bars provide valuable feedback to users during long-running operations, improving the overall user experience of your Gradio app.

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Progress indicators

Basic usage

Using the tqdm method

Automatic tqdm integration

Progress bar parameters

Multi-step progress tracking

​Basic usage

​Using the tqdm method

​Automatic tqdm integration

​Progress bar parameters

​Multi-step progress tracking

Basic usage

Using the tqdm method

Automatic tqdm integration

Progress bar parameters

Multi-step progress tracking