9-Building_and_sharing_demos-0-Introduction_to_Gradio

中英文对照学习，效果更佳！
原课程链接：https://huggingface.co/course/chapter9/1?fw=pt

Introduction to Gradio

GRadio简介

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In this chapter we will be learning about how to build interactive demos for your machine learning models.

在本章中，我们将学习如何为您的机器学习模型构建交互式演示。

Why build a demo or a GUI for your machine learning model in the first place? Demos allow:

为什么首先要为您的机器学习模型构建一个演示或图形用户界面？演示允许：

  • Machine learning developers to easily present their work to a wide audience including non-technical teams or customers
  • Researchers to more easily reproduce machine learning models and behavior
  • Quality testers or end users to more easily identify and debug failure points of models
  • Diverse users to discover algorithmic biases in models

We’ll be using the Gradio library to build demos for our models. Gradio allows you to build, customize, and share web-based demos for any machine learning model, entirely in Python.

机器学习开发人员可以轻松地向包括非技术团队或客户在内的广大受众展示他们的工作研究人员可以更轻松地复制机器学习模型和行为质量测试人员或最终用户可以更轻松地识别和调试模型的故障点让用户可以发现模型中的算法偏差我们将使用™库为我们的模型构建演示。GRadio允许您为任何机器学习模型构建、定制和共享基于Web的演示，完全使用Python语言。

Here are some examples of machine learning demos built with Gradio:

以下是使用GRadio构建的一些机器学习演示示例：

  • A sketch recognition model that takes in a sketch and outputs labels of what it thinks is being drawn:

    接受草图并输出其认为正在绘制的内容的标签的草图识别模型：

  • An extractive question answering model that takes in a context paragraph and a quest and outputs a response and a probability score (we discussed this kind of model in Chapter 7):

    一个提取的问答模型，它接受上下文段落和任务，并输出响应和概率分数(我们在第7章讨论了这种模型)：

  • A background removal model that takes in an image and outputs the image with the background removed:

    一种背景去除模型，它接收图像并输出去除了背景的图像：

This chapter is broken down into sections which include both concepts and applications. After you learn the concept in each section, you’ll apply it to build a particular kind of demo, ranging from image classification to speech recognition. By the time you finish this chapter, you’ll be able to build these demos (and many more!) in just a few lines of Python code.

本章分为几节，包括概念和应用。在您学习了每一节中的概念之后，您将应用它来构建从图像分类到语音识别的特定类型的演示。当您读完本章时，您将能够构建这些演示(以及更多！)™在短短的几行Python代码中。

👀 Check out Hugging Face Spaces to see many recent examples of machine learning demos built by the machine learning community!

FinŸ的EURO查看Hugging Face空间，看看机器学习社区最近构建的许多机器学习演示示例！