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Demos

Here are some examples of what you can build with Gradio in just a few lines of Python. Once you’re ready to learn, head over to the ⚡ Quickstart.

Check out more demos on Spaces.

🖊️ Text & Natural Language Processing

import gradio as gr

def greet(name):
    return "Hello " + name + "!"

demo = gr.Interface(fn=greet, inputs="text", outputs="text")
    
if __name__ == "__main__":
    demo.launch()

🖼️ Images & Computer Vision

import gradio as gr
import torch
import requests
from torchvision import transforms

model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")

def predict(inp):
  inp = transforms.ToTensor()(inp).unsqueeze(0)
  with torch.no_grad():
    prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
    confidences = {labels[i]: float(prediction[i]) for i in range(1000)}    
  return confidences

demo = gr.Interface(fn=predict, 
             inputs=gr.inputs.Image(type="pil"),
             outputs=gr.outputs.Label(num_top_classes=3),
             examples=[["cheetah.jpg"]],
             )
             
demo.launch()

📈 Tabular Data & Plots

import gradio as gr
import pandas as pd
import plotly.express as px
from helpers import *


LIBRARIES = ["accelerate", "datasets", "diffusers", "evaluate", "gradio", "hub_docs",
             "huggingface_hub", "optimum", "pytorch_image_models", "tokenizers", "transformers"]


def create_pip_plot(libraries, pip_choices):
    if "Pip" not in pip_choices:
        return gr.update(visible=False)
    output = retrieve_pip_installs(libraries, "Cumulated" in pip_choices)
    df = pd.DataFrame(output).melt(id_vars="day")
    plot = px.line(df, x="day", y="value", color="variable",
                   title="Pip installs")
    plot.update_layout(legend=dict(x=0.5, y=0.99),  title_x=0.5, legend_title_text="")
    return gr.update(value=plot, visible=True)


def create_star_plot(libraries, star_choices):
    if "Stars" not in star_choices:
        return gr.update(visible=False)
    output = retrieve_stars(libraries, "Week over Week" in star_choices)
    df = pd.DataFrame(output).melt(id_vars="day")
    plot = px.line(df, x="day", y="value", color="variable",
                   title="Number of stargazers")
    plot.update_layout(legend=dict(x=0.5, y=0.99),  title_x=0.5, legend_title_text="")
    return gr.update(value=plot, visible=True)


def create_issue_plot(libraries, issue_choices):
    if "Issue" not in issue_choices:
        return gr.update(visible=False)
    output = retrieve_issues(libraries,
                             exclude_org_members="Exclude org members" in issue_choices,
                             week_over_week="Week over Week" in issue_choices)
    df = pd.DataFrame(output).melt(id_vars="day")
    plot = px.line(df, x="day", y="value", color="variable",
                   title="Cumulated number of issues, PRs, and comments",
                   )
    plot.update_layout(legend=dict(x=0.5, y=0.99),  title_x=0.5, legend_title_text="")
    return gr.update(value=plot, visible=True)


with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            with gr.Box():
                gr.Markdown("## Select libraries to display")
                libraries = gr.CheckboxGroup(choices=LIBRARIES, label="")
        with gr.Column():
            with gr.Box():
                gr.Markdown("## Select graphs to display")
                pip = gr.CheckboxGroup(choices=["Pip", "Cumulated"], label="")
                stars = gr.CheckboxGroup(choices=["Stars", "Week over Week"], label="")
                issues = gr.CheckboxGroup(choices=["Issue", "Exclude org members", "week over week"], label="")
    with gr.Row():
        fetch = gr.Button(value="Fetch")
    with gr.Row():
        with gr.Column():
            pip_plot = gr.Plot(visible=False)
            star_plot = gr.Plot(visible=False)
            issue_plot = gr.Plot(visible=False)

    fetch.click(create_pip_plot, inputs=[libraries, pip], outputs=pip_plot)
    fetch.click(create_star_plot, inputs=[libraries, stars], outputs=star_plot)
    fetch.click(create_issue_plot, inputs=[libraries, issues], outputs=issue_plot)


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

🎤 Audio & Speech

import tempfile
import gradio as gr
from neon_tts_plugin_coqui import CoquiTTS

LANGUAGES = list(CoquiTTS.langs.keys())
coquiTTS = CoquiTTS()

def tts(text: str, language: str):
    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
        coquiTTS.get_tts(text, fp, speaker = {"language" : language})
        return fp.name

inputs = [gr.Textbox(label="Input", value=CoquiTTS.langs["en"]["sentence"], max_lines=3), 
            gr.Radio(label="Language", choices=LANGUAGES, value="en")]
outputs = gr.Audio(label="Output")

demo = gr.Interface(fn=tts, inputs=inputs, outputs=outputs)

demo.launch()