Gradio快速搭建AI演示应用：5分钟上线你的模型

pip install gradio

import gradio as gr

def analyze_sentiment(text):
    # 这里替换成你的模型逻辑
    score = len([c for c in text if c in "好评满意赞"]) / max(len(text), 1)
    return {"正面": score, "负面": 1 - score}

demo = gr.Interface(
    fn=analyze_sentiment,
    inputs=gr.Textbox(label="输入文本", lines=3, placeholder="请输入要分析的文本"),
    outputs=gr.Label(label="情感分析结果"),
    title="文本情感分析",
    description="输入一段中文文本，自动分析情感倾向"
)

demo.launch()

import gradio as gr
from openai import OpenAI

client = OpenAI(base_url="http://localhost:11434/v1", api_key="ollama")

def chat(message, history):
    messages = [{"role": "system", "content": "你是一个技术写作助手，回答简洁专业"}]
    for user_msg, assistant_msg in history:
        messages.append({"role": "user", "content": user_msg})
        messages.append({"role": "assistant", "content": assistant_msg})
    messages.append({"role": "user", "content": message})

    response = client.chat.completions.create(
        model="deepseek-r1:7b", messages=messages, stream=True
    )

    partial = ""
    for chunk in response:
        if chunk.choices[0].delta.content:
            partial += chunk.choices[0].delta.content
            yield partial

demo = gr.ChatInterface(fn=chat, title="本地AI助手")
demo.launch()

import gradio as gr
from PIL import Image, ImageFilter

def process_image(image, operation):
    ops = {
        "灰度": lambda img: img.convert("L"),
        "模糊": lambda img: img.filter(ImageFilter.GaussianBlur(5)),
        "边缘检测": lambda img: img.filter(ImageFilter.FIND_EDGES),
        "锐化": lambda img: img.filter(ImageFilter.SHARPEN),
    }
    return ops.get(operation, lambda img: img)(image)

demo = gr.Interface(
    fn=process_image,
    inputs=[gr.Image(type="pil", label="上传图片"),
            gr.Radio(["灰度", "模糊", "边缘检测", "锐化"], label="处理方式")],
    outputs=gr.Image(label="结果"),
    title="图片处理工具"
)
demo.launch()

with gr.Blocks(title="AI文档问答") as demo:
    gr.Markdown("# AI文档问答系统")

    with gr.Row():
        with gr.Column(scale=1):
            file = gr.File(label="上传文档", file_types=[".pdf", ".md", ".txt"])
            question = gr.Textbox(label="输入问题", lines=2)
            btn = gr.Button("提问", variant="primary")
        with gr.Column(scale=2):
            answer = gr.Markdown(label="回答")
            sources = gr.JSON(label="引用来源")

    btn.click(handle_ask, inputs=[file, question], outputs=[answer, sources])

demo.launch()