import{s as Pe,f as Ke,n as Oe,o as el}from"../chunks/scheduler.37c15a92.js";import{S as ll,i as tl,g as i,s as a,r as m,A as sl,h as o,f as t,c as n,j as Be,u,x as p,k as c,y as al,a as s,v as y,d,t as J,w as T}from"../chunks/index.2bf4358c.js";import{Y as nl}from"../chunks/Youtube.1e50a667.js";import{C as Ge}from"../chunks/CodeBlock.4f5fc1ad.js";import{D as il}from"../chunks/DocNotebookDropdown.d9dfeb24.js";import{H as A}from"../chunks/Heading.8ada512a.js";function ol(Re){let U,D,q,K,w,P,b,O,j,Se=`现在您已经构建了一个演示，您可能希望与其他人分享它。 梯度演示
可以通过两种方式共享：使用 <strong><em>temporary share link</em></strong> 或 <strong><em>permanent hosting on Spaces</em></strong>。`,ee,g,$e="我们将很快介绍这两种方法。 但在分享演示之前，您可能需要完善它 💅.",le,f,te,h,We='<img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter9/gradio-demo-overview.png" alt="Overview of a gradio interface"/> <img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter9/gradio-demo-overview-dark.png" alt="Overview of a gradio interface"/>',se,C,Ze="为了给你的演示添加额外的内容，<code>Interface</code> 类支持一些可选参数：",ae,I,Qe=`<li><code>title</code>：你可以给你的演示一个标题，它出现在输入和输出组件的上方。</li> <li><code>description</code>：您可以为界面提供描述（文本、Markdown 或 HTML），显示在输入和输出组件的上方和标题下方。</li> <li><code>article</code>：您还可以编写扩展文章（文本、Markdown 或 HTML）来解释界面。如果提供，它会出现在输入和输出组件的_下方。</li> <li><code>theme</code>：不喜欢默认颜色？将主题设置为使用 <code>default</code>、<code>huggingface</code>、<code>grass</code>、<code>peach</code> 之一。您还可以添加 <code>dark-</code> 前缀，例如<code>dark-peach</code> 用于深色主题（或者只是 <code>dark</code> 用于默认的深色主题）。</li> <li><code>examples</code>：为了让您的演示<em>更易于使用</em>，您可以为函数提供一些示例输入。它们出现在 UI 组件下方，可用于填充界面。这些应该作为嵌套列表提供，其中外部列表​​由样本组成，每个内部列表对应于每个输入组件的输入组成。</li> <li><code>live</code>：如果你想让你的演示“活”，这意味着你的模型每次输入更改时都会重新运行，你可以设置 <code>live=True</code>。这对使用快速模型很有意义（我们将在本节末尾看到一个示例）
使用上面的选项，我们最终得到了一个更完整的界面。 运行下面的代码，以便与 Rick and Morty 聊天：</li>`,ne,k,ie,v,He="使用上面的选项，我们最终得到了一个更完整的界面。 试试下面的界面：",oe,r,xe,pe,B,ce,G,Ye=`现在我们已经有了机器学习模型的工作演示，让我们学习如何轻松共享指向我们界面的链接。
通过在 <code>launch()</code> 方法中设置 <code>share=True</code> 可以轻松地公开共享接口：`,re,R,Me,S,Fe="这会生成一个公开的、可共享的链接，您可以将其发送给任何人！ 当您发送此链接时，另一方的用户可以在浏览器中试用该模型长达 72 小时。 因为处理发生在您的设备上（只要您的设备保持开启！），您不必担心打包任何依赖项。 如果您使用 Google Colab 笔记本工作，则始终会自动创建共享链接。 它通常看起来像这样：<strong>XXXXX.gradio.app</strong>。 虽然链接是通过 Gradio 链接提供的，但我们只是您本地服务器的代理，不会存储通过接口发送的任何数据。",me,$,_e="但是请记住，这些链接是可公开访问的，这意味着任何人都可以使用您的模型进行预测！ 因此，请确保不要通过您编写的函数公开任何敏感信息，或允许在您的设备上发生任何关键更改。 如果设置 <code>share=False</code>（默认值），则仅创建本地链接。",ue,W,ye,Z,ze="可以传递给同事的共享链接很酷，但是如何永久托管您的演示并让它存在于互联网上自己的“空间”中？",de,Q,Le=`Hugging Face Spaces 提供了在互联网上永久托管 Gradio 模型的基础设施，<strong>免费</strong>！ Spaces 允许您创建并推送到（公共或私人）存储库，
你的 Gradio 在哪里
接口代码将存在于 <code>app.py</code> 文件中。 <a href="https://huggingface.co/blog/gradio-spaces" rel="nofollow">阅读分步教程</a> 开始使用，或观看下面的示例视频。`,Je,H,Te,x,Ue,Y,Ne='使用到目前为止我们在各节中学到的知识，让我们创建我们在<a href="/course/chapter9/1">本章第一节</a>中看到的草图识别演示。 让我们为我们的界面添加一些自定义并设置 <code>share=True</code> 以创建一个我们可以传递的公共链接。',he,F,Ee='我们可以从 <a href="https://huggingface.co/spaces/dawood/Sketch-Recognition/blob/main/class_names.txt" rel="nofollow">class_names.txt</a> 加载标签，并从 <a href="https://huggingface.co/spaces/dawood/Sketch-Recognition/blob/main/pytorch_model.bin" rel="nofollow">pytorch_model.bin</a>加载预训练的 pytorch 模型 。 通过点击链接并单击文件预览左上角的下载来下载这些文件。 让我们看看下面的代码，看看我们如何使用这些文件来加载我们的模型并创建一个<code>predict()</code>函数：',we,_,be,z,Ve="现在我们有了一个<code>predict()</code>函数。 下一步是定义并启动我们的渐变界面：",je,L,ge,M,qe,fe,N,Xe=`注意 <code>Interface</code> 中的 <code>live=True</code> 参数，这意味着草图演示使
每次有人在画板上画画时的预测（没有提交按钮！）。`,Ce,E,Ae=`此外，我们还在 <code>launch()</code> 方法中设置了 <code>share=True</code> 参数。
这将创建一个公共链接，您可以发送给任何人！ 当您发送此链接时，对方的用户可以尝试草图识别模型。 重申一下，您还可以在 Hugging Face Spaces 上托管模型，这就是我们能够嵌入上面的演示的方式。`,Ie,V,De="接下来，我们将介绍 Gradio 可用于 Hugging Face 生态系统的其他方式！",ke,X,ve;return w=new A({props:{title:"与他人分享演示",local:"与他人分享演示",headingTag:"h1"}}),b=new il({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Google Colab",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/chapter9/section4.ipynb"},{label:"Aws Studio",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/chapter9/section4.ipynb"}]}}),f=new A({props:{title:"打磨你的 Gradio 演示:",local:"打磨你的 Gradio 演示:",headingTag:"h3"}}),k=new Ge({props:{code:"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",highlighted:`title = <span class="hljs-string">&quot;Ask Rick a Question&quot;</span>
description = <span class="hljs-string">&quot;&quot;&quot;
The bot was trained to answer questions based on Rick and Morty dialogues. Ask Rick anything!
&lt;img src=&quot;https://huggingface.co/spaces/course-demos/Rick_and_Morty_QA/resolve/main/rick.png&quot; width=200px&gt;
&quot;&quot;&quot;</span>

article = <span class="hljs-string">&quot;Check out [the original Rick and Morty Bot](https://huggingface.co/spaces/kingabzpro/Rick_and_Morty_Bot) that this demo is based off of.&quot;</span>

gr.Interface(
    fn=predict,
    inputs=<span class="hljs-string">&quot;textbox&quot;</span>,
    outputs=<span class="hljs-string">&quot;text&quot;</span>,
    title=title,
    description=description,
    article=article,
    examples=[[<span class="hljs-string">&quot;What are you doing?&quot;</span>], [<span class="hljs-string">&quot;Where should we time travel to?&quot;</span>]],
).launch()`,wrap:!1}}),B=new A({props:{title:"使用临时链接分享您的演示",local:"使用临时链接分享您的演示",headingTag:"h3"}}),R=new Ge({props:{code:"Z3IuSW50ZXJmYWNlKGNsYXNzaWZ5X2ltYWdlJTJDJTIwJTIyaW1hZ2UlMjIlMkMlMjAlMjJsYWJlbCUyMikubGF1bmNoKHNoYXJlJTNEVHJ1ZSk=",highlighted:'gr.Interface(classify_image, <span class="hljs-string">&quot;image&quot;</span>, <span class="hljs-string">&quot;label&quot;</span>).launch(share=<span class="hljs-literal">True</span>)',wrap:!1}}),W=new A({props:{title:"在 Hugging Face Spaces 上托管您的演示",local:"在 Hugging Face Spaces 上托管您的演示",headingTag:"h3"}}),H=new nl({props:{id:"LS9Y2wDVI0k"}}),x=new A({props:{title:"✏️ 让我们应用它!",local:"✏️ 让我们应用它!",headingTag:"h2"}}),_=new Ge({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> pathlib <span class="hljs-keyword">import</span> Path
<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> gradio <span class="hljs-keyword">as</span> gr
<span class="hljs-keyword">from</span> torch <span class="hljs-keyword">import</span> nn

LABELS = Path(<span class="hljs-string">&quot;class_names.txt&quot;</span>).read_text().splitlines()

model = nn.Sequential(
    nn.Conv2d(<span class="hljs-number">1</span>, <span class="hljs-number">32</span>, <span class="hljs-number">3</span>, padding=<span class="hljs-string">&quot;same&quot;</span>),
    nn.ReLU(),
    nn.MaxPool2d(<span class="hljs-number">2</span>),
    nn.Conv2d(<span class="hljs-number">32</span>, <span class="hljs-number">64</span>, <span class="hljs-number">3</span>, padding=<span class="hljs-string">&quot;same&quot;</span>),
    nn.ReLU(),
    nn.MaxPool2d(<span class="hljs-number">2</span>),
    nn.Conv2d(<span class="hljs-number">64</span>, <span class="hljs-number">128</span>, <span class="hljs-number">3</span>, padding=<span class="hljs-string">&quot;same&quot;</span>),
    nn.ReLU(),
    nn.MaxPool2d(<span class="hljs-number">2</span>),
    nn.Flatten(),
    nn.Linear(<span class="hljs-number">1152</span>, <span class="hljs-number">256</span>),
    nn.ReLU(),
    nn.Linear(<span class="hljs-number">256</span>, <span class="hljs-built_in">len</span>(LABELS)),
)
state_dict = torch.load(<span class="hljs-string">&quot;pytorch_model.bin&quot;</span>, map_location=<span class="hljs-string">&quot;cpu&quot;</span>)
model.load_state_dict(state_dict, strict=<span class="hljs-literal">False</span>)
model.<span class="hljs-built_in">eval</span>()


<span class="hljs-keyword">def</span> <span class="hljs-title function_">predict</span>(<span class="hljs-params">im</span>):
    x = torch.tensor(im, dtype=torch.float32).unsqueeze(<span class="hljs-number">0</span>).unsqueeze(<span class="hljs-number">0</span>) / <span class="hljs-number">255.0</span>
    <span class="hljs-keyword">with</span> torch.no_grad():
        out = model(x)
    probabilities = torch.nn.functional.softmax(out[<span class="hljs-number">0</span>], dim=<span class="hljs-number">0</span>)
    values, indices = torch.topk(probabilities, <span class="hljs-number">5</span>)
    <span class="hljs-keyword">return</span> {LABELS[i]: v.item() <span class="hljs-keyword">for</span> i, v <span class="hljs-keyword">in</span> <span class="hljs-built_in">zip</span>(indices, values)}`,wrap:!1}}),L=new Ge({props:{code:"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",highlighted:`interface = gr.Interface(
    predict,
    inputs=<span class="hljs-string">&quot;sketchpad&quot;</span>,
    outputs=<span class="hljs-string">&quot;label&quot;</span>,
    theme=<span class="hljs-string">&quot;huggingface&quot;</span>,
    title=<span class="hljs-string">&quot;Sketch Recognition&quot;</span>,
    description=<span class="hljs-string">&quot;Who wants to play Pictionary? Draw a common object like a shovel or a laptop, and the algorithm will guess in real time!&quot;</span>,
    article=<span class="hljs-string">&quot;&lt;p style=&#x27;text-align: center&#x27;&gt;Sketch Recognition | Demo Model&lt;/p&gt;&quot;</span>,
    live=<span class="hljs-literal">True</span>,
)
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