Gradio入门到进阶全网最详细教程[一]：快速搭建AI算法可视化部署演示(侧重项目搭建和案例分享)

pip install gradio

#为了更快安装，可以使用清华镜像源
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple gradio

import gradio as gr
#输入文本处理程序
def greet(name):
    return "Hello " + name + "!"
#接口创建函数
#fn设置处理函数，inputs设置输入接口组件，outputs设置输出接口组件
#fn,inputs,outputs都是必填函数
demo = gr.Interface(fn=greet, inputs="text", outputs="text")
demo.launch()

#执行结果
Running on local URL:  http://127.0.0.1:7860

To create a public link, set `share=True` in `launch()`.

gradio app.py

import gradio as gr
def greet(name):
    return "Hello " + name + "!"
demo = gr.Interface(
    fn=greet,
    # 自定义输入框
    # 具体设置方法查看官方文档
    inputs=gr.Textbox(lines=3, placeholder="Name Here...",label="my input"),
    outputs="text",
)
demo.launch()

import gradio as gr

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

iface = gr.Interface(
    fn=greet,
    inputs=gr.inputs.Textbox(lines=2, placeholder="Name Here..."),
    outputs="text",
)
if __name__ == "__main__":
    app, local_url, share_url = iface.launch()

import gradio as gr
#该函数有3个输入参数和2个输出参数
def greet(name, is_morning, temperature):
    salutation = "Good morning" if is_morning else "Good evening"
    greeting = f"{salutation} {name}. It is {temperature} degrees today"
    celsius = (temperature - 32) * 5 / 9
    return greeting, round(celsius, 2)
demo = gr.Interface(
    fn=greet,
    #按照处理程序设置输入组件
    inputs=["text", "checkbox", gr.Slider(0, 100)],
    #按照处理程序设置输出组件
    outputs=["text", "number"],
)
demo.launch()

import numpy as np
import gradio as gr
def sepia(input_img):
    #处理图像
    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
#shape设置输入图像大小
demo = gr.Interface(sepia, gr.Image(shape=(200, 200)), "image")
demo.launch()

import gradio as gr

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":
        return num1 / num2

iface = gr.Interface(
    calculator,
    ["number", gr.inputs.Radio(["add", "subtract", "multiply", "divide"]), "number"],
    "number",
    live=True,
)

iface.launch()

import gradio as gr
#一个简单计算器，含实例说明
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:
            # 设置报错弹窗
            raise gr.Error("Cannot divide by zero!")
        return num1 / num2
demo = gr.Interface(
    calculator,
    # 设置输入
    [
        "number",
        gr.Radio(["add", "subtract", "multiply", "divide"]),
        "number"
    ],
    # 设置输出
    "number",
    # 设置输入参数示例
    examples=[
        [5, "add", 3],
        [4, "divide", 2],
        [-4, "multiply", 2.5],
        [0, "subtract", 1.2],
    ],
    # 设置网页标题
    title="Toy Calculator",
    # 左上角的描述文字
    description="Here's a sample toy calculator. Enjoy!",
    # 左下角的文字
    article = "Check out the examples",
)
demo.launch()

import gradio as gr
scores = []
def track_score(score):
    scores.append(score)
    #返回分数top3
    top_scores = sorted(scores, reverse=True)[:3]
    return top_scores
demo = gr.Interface(
    track_score,
    gr.Number(label="Score"),
    gr.JSON(label="Top Scores")
)
demo.launch()

import random
import gradio as gr
def chat(message, history):
    history = history or []
    message = message.lower()
    if message.startswith("how many"):
        response = random.randint(1, 10)
    elif message.startswith("how"):
        response = random.choice(["Great", "Good", "Okay", "Bad"])
    elif message.startswith("where"):
        response = random.choice(["Here", "There", "Somewhere"])
    else:
        response = "I don't know"
    history.append((message, response))
    return history, history
#设置一个对话窗
chatbot = gr.Chatbot().style(color_map=("green", "pink"))
demo = gr.Interface(
    chat,
    # 添加state组件
    ["text", "state"],
    [chatbot, "state"],
    # 设置没有保存数据的按钮
    allow_flagging="never",
)
demo.launch()

import gradio as gr
import numpy as np
def flip(im):
    return np.flipud(im)
demo = gr.Interface(
    flip,
    gr.Image(source="webcam", streaming=True),
    "image",
    live=True
)
demo.launch()

import gradio as gr
def greet(name):
    return "Hello " + name + "!"
with gr.Blocks() as demo:
    #设置输入组件
    name = gr.Textbox(label="Name")
    # 设置输出组件
    output = gr.Textbox(label="Output Box")
    #设置按钮
    greet_btn = gr.Button("Greet")
    #设置按钮点击事件
    greet_btn.click(fn=greet, inputs=name, outputs=output)
demo.launch()

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:
    #用markdown语法编辑输出一段话
    gr.Markdown("Flip text or image files using this demo.")
    # 设置tab选项卡
    with gr.Tab("Flip Text"):
        #Blocks特有组件，设置所有子组件按垂直排列
        #垂直排列是默认情况，不加也没关系
        with gr.Column():
            text_input = gr.Textbox()
            text_output = gr.Textbox()
            text_button = gr.Button("Flip")
    with gr.Tab("Flip Image"):
        #Blocks特有组件，设置所有子组件按水平排列
        with gr.Row():
            image_input = gr.Image()
            image_output = gr.Image()
        image_button = gr.Button("Flip")
    #设置折叠内容
    with gr.Accordion("Open for More!"):
        gr.Markdown("Look at me...")
    text_button.click(flip_text, inputs=text_input, outputs=text_output)
    image_button.click(flip_image, inputs=image_input, outputs=image_output)
demo.launch()

img = gr.Image("lion.jpg").style(height='24', rounded=False)

demo = gr.Interface(...).queue()
demo.launch()
#或
with gr.Blocks() as demo:
    #...
demo.queue()
demo.launch()

import gradio as gr
import numpy as np
import time
#生成steps张图片，每隔1秒钟返回
def fake_diffusion(steps):
    for _ in range(steps):
        time.sleep(1)
        image = np.random.randint(255, size=(300, 600, 3))
        yield image
demo = gr.Interface(fake_diffusion,
                    #设置滑窗，最小值为1，最大值为10，初始值为3，每次改动增减1位
                    inputs=gr.Slider(1, 10, value=3, step=1),
                    outputs="image")
#生成器必须要queue函数
demo.queue()
demo.launch()

import gradio as gr
def greet(name):
    return "Hello " + name + "!"
with gr.Blocks() as demo:
    name = gr.Textbox(label="Name")
    # 不可交互
    # output = gr.Textbox(label="Output Box")
    # 可交互
    output = gr.Textbox(label="Output", interactive=True)
    greet_btn = gr.Button("Greet")
    greet_btn.click(fn=greet, inputs=name, outputs=output)
demo.launch()

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()
    #设置change事件
    inp.change(fn = welcome, inputs = inp, outputs = out)
demo.launch()

import gradio as gr
def increase(num):
    return num + 1
with gr.Blocks() as demo:
    a = gr.Number(label="a")
    b = gr.Number(label="b")
    # 要想b>a，则使得b = a+1
    atob = gr.Button("b > a")
    atob.click(increase, a, b)
    # 要想a>b，则使得a = b+1
    btoa = gr.Button("a > b")
    btoa.click(increase, b, a)
demo.launch()

import gradio as gr
with gr.Blocks() as demo:
    food_box = gr.Number(value=10, label="Food Count")
    status_box = gr.Textbox()
    def eat(food):
        if food > 0:
            return food - 1, "full"
        else:
            return 0, "hungry"
    gr.Button("EAT").click(
        fn=eat,
        inputs=food_box,
        #根据返回值改变输入组件和输出组件
        outputs=[food_box, status_box]
    )
demo.launch()

import gradio as gr
def change_textbox(choice):
    #根据不同输入对输出控件进行更新
    if choice == "short":
        return gr.update(lines=2, visible=True, value="Short story: ")
    elif choice == "long":
        return gr.update(lines=8, visible=True, value="Long story...")
    else:
        return gr.update(visible=False)
with gr.Blocks() as demo:
    radio = gr.Radio(
        ["short", "long", "none"], label="Essay Length to Write?"
    )
    text = gr.Textbox(lines=2, interactive=True)
    radio.change(fn=change_textbox, inputs=radio, outputs=text)
demo.launch()

import gradio as gr
with gr.Blocks() as demo:
    with gr.Row():
        img1 = gr.Image()
        text1 = gr.Text()
    btn1 = gr.Button("button")
demo.launch()

import gradio as gr
with gr.Blocks() as demo:
    with gr.Row():
        text1 = gr.Textbox(label="t1")
        slider2 = gr.Textbox(label="s2")
        drop3 = gr.Dropdown(["a", "b", "c"], label="d3")
    with gr.Row():
        # scale与相邻列相比的相对宽度。例如，如果列A的比例为2，列B的比例为1，则A的宽度将是B的两倍。
        # min_width设置最小宽度，防止列太窄
        with gr.Column(scale=2, min_width=600):
            text1 = gr.Textbox(label="prompt 1")
            text2 = gr.Textbox(label="prompt 2")
            inbtw = gr.Button("Between")
            text4 = gr.Textbox(label="prompt 1")
            text5 = gr.Textbox(label="prompt 2")
        with gr.Column(scale=1, min_width=600):
            img1 = gr.Image("test.jpg")
            btn = gr.Button("Go")
demo.launch()

import gradio as gr
with gr.Blocks() as demo:
    # 出错提示框
    error_box = gr.Textbox(label="Error", visible=False)
    # 输入框
    name_box = gr.Textbox(label="Name")
    age_box = gr.Number(label="Age")
    symptoms_box = gr.CheckboxGroup(["Cough", "Fever", "Runny Nose"])
    submit_btn = gr.Button("Submit")
    # 输出不可见
    with gr.Column(visible=False) as output_col:
        diagnosis_box = gr.Textbox(label="Diagnosis")
        patient_summary_box = gr.Textbox(label="Patient Summary")
    def submit(name, age, symptoms):
        if len(name) == 0:
            return {error_box: gr.update(value="Enter name", visible=True)}
        if age < 0 or age > 200:
            return {error_box: gr.update(value="Enter valid age", visible=True)}
        return {
            output_col: gr.update(visible=True),
            diagnosis_box: "covid" if "Cough" in symptoms else "flu",
            patient_summary_box: f"{name}, {age} y/o"
        }
    submit_btn.click(
        submit,
        [name_box, age_box, symptoms_box],
        [error_box, diagnosis_box, patient_summary_box, output_col],
    )
demo.launch()

import gradio as gr
input_textbox = gr.Textbox()
with gr.Blocks() as demo:
    #提供示例输入给input_textbox，示例输入以嵌套列表形式设置
    gr.Examples(["hello", "bonjour", "merhaba"], input_textbox)
    # render函数渲染input_textbox
    input_textbox.render()
demo.launch()

import gradio as gr
#修改blocks的背景颜色
with gr.Blocks(css=".gradio-container {background-color: red}") as demo:
    box1 = gr.Textbox(value="Good Job")
    box2 = gr.Textbox(value="Failure")
demo.launch()

import gradio as gr
# 这里用的是id属性设置
with gr.Blocks(css="#warning {background-color: red}") as demo:
    box1 = gr.Textbox(value="Good Job", elem_id="warning")
    box2 = gr.Textbox(value="Failure")
    box3 = gr.Textbox(value="None", elem_id="warning")
demo.launch()

demo.launch(share=True)

#show_error为True表示在控制台显示错误信息。
demo.launch(server_name='0.0.0.0', server_port=8080, show_error=True)

demo.launch(auth=("admin", "pass1234"))

#账户和密码相同就可以通过
def same_auth(username, password):
    return username == password
demo.launch(auth=same_auth,auth_message="username and password must be the same")

#!pip install gradio, ultralytics, transformers, torchkeras

import gradio as gr 
from transformers import pipeline
 
pipe = pipeline("text-classification")
 
def clf(text):
    result = pipe(text)
    label = result[0]['label']
    score = result[0]['score']
    res = {label:score,'POSITIVE' if label=='NEGATIVE' else 'NEGATIVE': 1-score}
    return res 
 
demo = gr.Interface(fn=clf, inputs="text", outputs="label")
gr.close_all()
demo.launch(share=True)

import gradio as gr 
import pandas as pd 
from ultralytics import YOLO
from skimage import data
from PIL import Image
 
model = YOLO('yolov8n-cls.pt')
def predict(img):
    result = model.predict(source=img)
    df = pd.Series(result[0].names).to_frame()
    df.columns = ['names']
    df['probs'] = result[0].probs
    df = df.sort_values('probs',ascending=False)
    res = dict(zip(df['names'],df['probs']))
    return res
gr.close_all() 
demo = gr.Interface(fn = predict,inputs = gr.Image(type='pil'), outputs = gr.Label(num_top_classes=5), 
                    examples = ['cat.jpeg','people.jpeg','coffee.jpeg'])
demo.launch()

import gradio as gr 
import pandas as pd 
from skimage import data
from ultralytics.yolo.data import utils 
 
model = YOLO('yolov8n.pt')
 
#load class_names
yaml_path = str(Path(ultralytics.__file__).parent/'datasets/coco128.yaml') 
class_names = utils.yaml_load(yaml_path)['names']

def detect(img):
    if isinstance(img,str):
        img = get_url_img(img) if img.startswith('http') else Image.open(img).convert('RGB')
    result = model.predict(source=img)
    if len(result[0].boxes.boxes)>0:
        vis = plots.plot_detection(img,boxes=result[0].boxes.boxes,
                     class_names=class_names, min_score=0.2)
    else:
        vis = img
    return vis
    
with gr.Blocks() as demo:
    gr.Markdown("# yolov8目标检测演示")
 
    with gr.Tab("捕捉摄像头喔"):
        in_img = gr.Image(source='webcam',type='pil')
        button = gr.Button("执行检测",variant="primary")
 
        gr.Markdown("## 预测输出")
        out_img = gr.Image(type='pil')
 
        button.click(detect,
                     inputs=in_img, 
                     outputs=out_img)
        
    
gr.close_all() 
demo.queue(concurrency_count=5)
demo.launch()

#!pip install -U torchkeras

import torchkeras 
from torchkeras.data import download_baidu_pictures 
download_baidu_pictures('猫咪表情包',100)

import gradio as gr
from PIL import Image
import time,os
from pathlib import Path 
base_dir = '猫咪表情包'
selected_dir = 'selected'
files = [str(x) for x in 
         Path(base_dir).rglob('*.jp*g') 
         if 'checkpoint' not in str(x)]
def show_img(path):
    return Image.open(path)
def fn_before(done,todo):
    ...
    return done,todo,path,img
def fn_next(done,todo):
    ...
    return done,todo,path,img
def save_selected(img_path):
    ...
    return msg 
def get_default_msg():
    ...
    return msg
    
    
with gr.Blocks() as demo:
    with gr.Row():
        total = gr.Number(len(files),label='总数量')
        with gr.Row(scale = 1):
            bn_before = gr.Button("上一张")
            bn_next = gr.Button("下一张")
        with gr.Row(scale = 2):
            done = gr.Number(0,label='已完成')
            todo = gr.Number(len(files),label='待完成')
    path = gr.Text(files[0],lines=1, label='当前图片路径')
    feedback_button = gr.Button("选择图片",variant="primary")
    msg = gr.TextArea(value=get_default_msg,lines=3,max_lines = 5)
    img = gr.Image(value = show_img(files[0]),type='pil')
    
    bn_before.click(fn_before,
                 inputs= [done,todo], 
                 outputs=[done,todo,path,img])
    bn_next.click(fn_next,
                 inputs= [done,todo], 
                 outputs=[done,todo,path,img])
    feedback_button.click(save_selected,
                         inputs = path,
                         outputs = msg
                         )

demo.launch()