---

关于 Gradio

• 官网：https://www.gradio.app/
• 官方文档：https://www.gradio.app/docs/
• github : https://github.com/gradio-app/gradio
• playground : https://www.gradio.app/playground

---

安装

pip install gradio

import gradio as gr

def greet(name, intensity):
    return "Hello " * intensity + name + "!"

demo = gr.Interface(
    fn=greet,
    inputs=["text", "slider"],
    outputs=["text"],
)

demo.launch()

---

Interface

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")
demo.launch()

---

ChatInterface

import gradio as gr

def echo(message, history):
    return message

demo = gr.ChatInterface(fn=echo, examples=["hello", "hola", "merhaba"], title="Echo Bot")
demo.launch()

---

import gradio as gr

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

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

---

Text

Blocks

import gradio as gr


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


with gr.Blocks() as demo:
    name = gr.Textbox(label="Name")
    output = gr.Textbox(label="Output Box")
    greet_btn = gr.Button("Greet")
    greet_btn.click(fn=greet, inputs=name, outputs=output, api_name="greet")

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

---

Sentence Builder

import gradio as gr


def sentence_builder(quantity, animal, countries, place, activity_list, morning):
    return f"""The {quantity} {animal}s from {" and ".join(countries)} went to the {place} where they {" and ".join(activity_list)} until the {"morning" if morning else "night"}"""


demo = gr.Interface(
    sentence_builder,
    [
        gr.Slider(2, 20, value=4, label="Count", info="Choose between 2 and 20"),
        gr.Dropdown(
            ["cat", "dog", "bird"], label="Animal", info="Will add more animals later!"
        ),
        gr.CheckboxGroup(["USA", "Japan", "Pakistan"], label="Countries", info="Where are they from?"),
        gr.Radio(["park", "zoo", "road"], label="Location", info="Where did they go?"),
        gr.Dropdown(
            ["ran", "swam", "ate", "slept"], value=["swam", "slept"], multiselect=True, label="Activity", info="Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl."
        ),
        gr.Checkbox(label="Morning", info="Did they do it in the morning?"),
    ],
    "text",
    examples=[
        [2, "cat", ["Japan", "Pakistan"], "park", ["ate", "swam"], True],
        [4, "dog", ["Japan"], "zoo", ["ate", "swam"], False],
        [10, "bird", ["USA", "Pakistan"], "road", ["ran"], False],
        [8, "cat", ["Pakistan"], "zoo", ["ate"], True],
    ]
)

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

---

Diff Texts

from difflib import Differ

import gradio as gr


def diff_texts(text1, text2):
    d = Differ()
    return [
        (token[2:], token[0] if token[0] != " " else None)
        for token in d.compare(text1, text2)
    ]


demo = gr.Interface(
    diff_texts,
    [
        gr.Textbox(
            label="Text 1",
            info="Initial text",
            lines=3,
            value="The quick brown fox jumped over the lazy dogs.",
        ),
        gr.Textbox(
            label="Text 2",
            info="Text to compare",
            lines=3,
            value="The fast brown fox jumps over lazy dogs.",
        ),
    ],
    gr.HighlightedText(
        label="Diff",
        combine_adjacent=True,
        show_legend=True,
        color_map={"+": "red", "-": "green"}),
    theme=gr.themes.Base()
)
if __name__ == "__main__":
    demo.launch()

---

Media

Sepia Filter

import numpy as np
import gradio as gr

def sepia(input_img):
    sepia_filter = np.array([
        [0.393, 0.769, 0.189], 
        [0.349, 0.686, 0.168], 
        [0.272, 0.534, 0.131]
    ])
    sepia_img = input_img.dot(sepia_filter.T)
    sepia_img /= sepia_img.max()
    return sepia_img

demo = gr.Interface(sepia, gr.Image(), "image")
if __name__ == "__main__":
    demo.launch()

---

Video Identity

import gradio as gr

def video_identity(video):
    return video


demo = gr.Interface(video_identity, 
                    gr.Video(), 
                    "playable_video", 
                    )

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

---

Iterative Output

import gradio as gr
import numpy as np
import time

def fake_diffusion(steps):
    rng = np.random.default_rng()
    for i in range(steps):
        time.sleep(1)
        image = rng.random(size=(600, 600, 3))
        yield image
    image = np.ones((1000,1000,3), np.uint8)
    image[:] = [255, 124, 0]
    yield image


demo = gr.Interface(fake_diffusion,
                    inputs=gr.Slider(1, 10, 3, step=1),
                    outputs="image")

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

---

Generate Tone

import numpy as np
import gradio as gr

notes = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]

def generate_tone(note, octave, duration):
    sr = 48000
    a4_freq, tones_from_a4 = 440, 12 * (octave - 4) + (note - 9)
    frequency = a4_freq * 2 ** (tones_from_a4 / 12)
    duration = int(duration)
    audio = np.linspace(0, duration, duration * sr)
    audio = (20000 * np.sin(audio * (2 * np.pi * frequency))).astype(np.int16)
    return sr, audio

demo = gr.Interface(
    generate_tone,
    [
        gr.Dropdown(notes, type="index"),
        gr.Slider(4, 6, step=1),
        gr.Textbox(value=1, label="Duration in seconds"),
    ],
    "audio",
)
if __name__ == "__main__":
    demo.launch()

---

Tabular

Filter Records

import gradio as gr


def filter_records(records, gender):
    return records[records["gender"] == gender]


demo = gr.Interface(
    filter_records,
    [
        gr.Dataframe(
            headers=["name", "age", "gender"],
            datatype=["str", "number", "str"],
            row_count=5,
            col_count=(3, "fixed"),
        ),
        gr.Dropdown(["M", "F", "O"]),
    ],
    "dataframe",
    description="Enter gender as 'M', 'F', or 'O' for other.",
)

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

---

Video Identity

import gradio as gr

def video_identity(video):
    return video


demo = gr.Interface(video_identity, 
                    gr.Video(), 
                    "playable_video", 
                    )

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

---

Iterative Output

import gradio as gr
import numpy as np
import time

def fake_diffusion(steps):
    rng = np.random.default_rng()
    for i in range(steps):
        time.sleep(1)
        image = rng.random(size=(600, 600, 3))
        yield image
    image = np.ones((1000,1000,3), np.uint8)
    image[:] = [255, 124, 0]
    yield image


demo = gr.Interface(fake_diffusion,
                    inputs=gr.Slider(1, 10, 3, step=1),
                    outputs="image")

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

---

Generate Tone

import numpy as np
import gradio as gr

notes = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]

def generate_tone(note, octave, duration):
    sr = 48000
    a4_freq, tones_from_a4 = 440, 12 * (octave - 4) + (note - 9)
    frequency = a4_freq * 2 ** (tones_from_a4 / 12)
    duration = int(duration)
    audio = np.linspace(0, duration, duration * sr)
    audio = (20000 * np.sin(audio * (2 * np.pi * frequency))).astype(np.int16)
    return sr, audio

demo = gr.Interface(
    generate_tone,
    [
        gr.Dropdown(notes, type="index"),
        gr.Slider(4, 6, step=1),
        gr.Textbox(value=1, label="Duration in seconds"),
    ],
    "audio",
)
if __name__ == "__main__":
    demo.launch()

---

Tabular

Filter Records

import gradio as gr


def filter_records(records, gender):
    return records[records["gender"] == gender]


demo = gr.Interface(
    filter_records,
    [
        gr.Dataframe(
            headers=["name", "age", "gender"],
            datatype=["str", "number", "str"],
            row_count=5,
            col_count=(3, "fixed"),
        ),
        gr.Dropdown(["M", "F", "O"]),
    ],
    "dataframe",
    description="Enter gender as 'M', 'F', or 'O' for other.",
)

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

---

Transpose Matrix

import numpy as np

import gradio as gr


def transpose(matrix):
    return matrix.T


demo = gr.Interface(
    transpose,
    gr.Dataframe(type="numpy", datatype="number", row_count=5, col_count=3),
    "numpy",
    examples=[
        [np.zeros((3, 3)).tolist()],
        [np.ones((2, 2)).tolist()],
        [np.random.randint(0, 10, (3, 10)).tolist()],
        [np.random.randint(0, 10, (10, 3)).tolist()],
        [np.random.randint(0, 10, (10, 10)).tolist()],
    ],
    cache_examples=False
)

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

---

Tax Calculator

import gradio as gr

def tax_calculator(income, marital_status, assets):
    tax_brackets = [(10, 0), (25, 8), (60, 12), (120, 20), (250, 30)]
    total_deductible = sum(assets["Cost"])
    taxable_income = income - total_deductible

    total_tax = 0
    for bracket, rate in tax_brackets:
        if taxable_income > bracket:
            total_tax += (taxable_income - bracket) * rate / 100

    if marital_status == "Married":
        total_tax *= 0.75
    elif marital_status == "Divorced":
        total_tax *= 0.8

    return round(total_tax)

demo = gr.Interface(
    tax_calculator,
    [
        "number",
        gr.Radio(["Single", "Married", "Divorced"]),
        gr.Dataframe(
            headers=["Item", "Cost"],
            datatype=["str", "number"],
            label="Assets Purchased this Year",
        ),
    ],
    "number",
    examples=[
        [10000, "Married", [["Suit", 5000], ["Laptop", 800], ["Car", 1800]]],
        [80000, "Single", [["Suit", 800], ["Watch", 1800], ["Car", 800]]],
    ],
)

demo.launch()

---

Kinematics

import pandas as pd
import numpy as np

import gradio as gr


def plot(v, a):
    g = 9.81
    theta = a / 180 * 3.14
    tmax = ((2 * v) * np.sin(theta)) / g
    timemat = tmax * np.linspace(0, 1, 40)

    x = (v * timemat) * np.cos(theta)
    y = ((v * timemat) * np.sin(theta)) - ((0.5 * g) * (timemat**2))
    df = pd.DataFrame({"x": x, "y": y})
    return df


demo = gr.Blocks()

with demo:
    gr.Markdown(
        r"Let's do some kinematics! Choose the speed and angle to see the trajectory. Remember that the range $R = v_0^2 \cdot \frac{\sin(2\theta)}{g}$"
    )

    with gr.Row():
        speed = gr.Slider(1, 30, 25, label="Speed")
        angle = gr.Slider(0, 90, 45, label="Angle")
    output = gr.LinePlot(
        x="x",
        y="y",
        overlay_point=True,
        tooltip=["x", "y"],
        x_lim=[0, 100],
        y_lim=[0, 60],
        width=350,
        height=300,
    )
    btn = gr.Button(value="Run")
    btn.click(plot, [speed, angle], output)

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

---

Stock Forecast

import matplotlib.pyplot as plt
import numpy as np

import gradio as gr


def plot_forecast(final_year, companies, noise, show_legend, point_style):
    start_year = 2020
    x = np.arange(start_year, final_year + 1)
    year_count = x.shape[0]
    plt_format = ({"cross": "X", "line": "-", "circle": "o--"})[point_style]
    fig = plt.figure()
    ax = fig.add_subplot(111)
    for i, company in enumerate(companies):
        series = np.arange(0, year_count, dtype=float)
        series = series**2 * (i + 1)
        series += np.random.rand(year_count) * noise
        ax.plot(x, series, plt_format)
    if show_legend:
        plt.legend(companies)
    return fig


demo = gr.Interface(
    plot_forecast,
    [
        gr.Radio([2025, 2030, 2035, 2040], label="Project to:"),
        gr.CheckboxGroup(["Google", "Microsoft", "Gradio"], label="Company Selection"),
        gr.Slider(1, 100, label="Noise Level"),
        gr.Checkbox(label="Show Legend"),
        gr.Dropdown(["cross", "line", "circle"], label="Style"),
    ],
    gr.Plot(label="forecast"),
)

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

---

Other

---

Tabbed Interface

import gradio as gr

hello_world = gr.Interface(lambda name: "Hello " + name, "text", "text")
bye_world = gr.Interface(lambda name: "Bye " + name, "text", "text")

demo = gr.TabbedInterface([hello_world, bye_world], ["Hello World", "Bye World"])

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

---

Chatbot

import random
import gradio as gr

def random_response(message, history):
    return random.choice(["Yes", "No"])

demo = gr.ChatInterface(random_response)

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

---

Streaming Chatbot

import time
import gradio as gr


def slow_echo(message, history):
    for i in range(len(message)):
        time.sleep(0.05)
        yield "You typed: " + message[: i + 1]


demo = gr.ChatInterface(slow_echo).queue()

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

---

Layouts

import numpy as np
import gradio as gr


def flip_text(x):
    return x[::-1]


def flip_image(x):
    return np.fliplr(x)


with gr.Blocks() as demo:
    gr.Markdown("Flip text or image files using this demo.")
    with gr.Tab("Flip Text"):
        text_input = gr.Textbox()
        text_output = gr.Textbox()
        text_button = gr.Button("Flip")
    with gr.Tab("Flip Image"):
        with gr.Row():
            image_input = gr.Image()
            image_output = gr.Image()
        image_button = gr.Button("Flip")

    with gr.Accordion("Open for More!", open=False):
        gr.Markdown("Look at me...")
        temp_slider = gr.Slider(
            minimum=0.0,
            maximum=1.0,
            value=0.1,
            step=0.1,
            interactive=True,
            label="Slide me",
        )
        temp_slider.change(lambda x: x, [temp_slider])

    text_button.click(flip_text, inputs=text_input, outputs=text_output)
    image_button.click(flip_image, inputs=image_input, outputs=image_output)

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

---

Error

import gradio as gr
#from foo import BAR
#
def calculator(num1, operation, num2):
    if operation == "add":
        return num1 + num2
    elif operation == "subtract":
        return num1 - num2
    elif operation == "multiply":
        return num1 * num2
    elif operation == "divide":
        if num2 == 0:
            raise gr.Error("Cannot divide by zero!")
        return num1 / num2

demo = gr.Interface(
    calculator,
    [
        "number", 
        gr.Radio(["add", "subtract", "multiply", "divide"]),
        "number"
    ],
    "number",
    examples=[
        [45, "add", 3],
        [3.14, "divide", 2],
        [144, "multiply", 2.5],
        [0, "subtract", 1.2],
    ],
    title="Toy Calculator",
    description="Here's a sample toy calculator. Allows you to calculate things like $2+2=4$",
)

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

---

Chained Events

import gradio as gr
import time


def failure():
    raise gr.Error("This should fail!")

def exception():
    raise ValueError("Something went wrong")

def success():
    return True

def warning_fn():
    gr.Warning("This is a warning!")

def info_fn():
    gr.Info("This is some info")


with gr.Blocks() as demo:
    gr.Markdown("Used in E2E tests of success event trigger. The then event covered in chatbot E2E tests."
                " Also testing that the status modals show up.")
    with gr.Row():
        result = gr.Textbox(label="Result")
        result_2 = gr.Textbox(label="Consecutive Event")
    with gr.Row():
        success_btn = gr.Button(value="Trigger Success")
        success_btn_2 = gr.Button(value="Trigger Consecutive Success")
        failure_btn = gr.Button(value="Trigger Failure")
        failure_exception = gr.Button(value="Trigger Failure With ValueError")
    with gr.Row():
        trigger_warning = gr.Button(value="Trigger Warning")
        trigger_info = gr.Button(value="Trigger Info")

        success_btn_2.click(success, None, None).success(lambda: "First Event Trigered", None, result).success(lambda: "Consecutive Event Triggered", None, result_2)
        success_btn.click(success, None, None).success(lambda: "Success event triggered", inputs=None, outputs=result)
        failure_btn.click(failure, None, None).success(lambda: "Should not be triggered", inputs=None, outputs=result)
        failure_exception.click(exception, None, None)
        trigger_warning.click(warning_fn, None, None)
        trigger_info.click(info_fn, None, None)

demo.queue()

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

---

Change Listener

import gradio as gr

def welcome(name):
    return f"Welcome to Gradio, {name}!"

with gr.Blocks() as demo:
    gr.Markdown(
    """
    # Hello World!
    Start typing below to see the output.
    """)
    inp = gr.Textbox(placeholder="What is your name?")
    out = gr.Textbox()
    inp.change(welcome, inp, out)

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

---

2024-05-15（三）