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 ve,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,$e=`現在您已經構建了一個演示，您可能希望與其他人分享它。 梯度演示
可以通過兩種方式共享：使用 <strong><em>temporary share link</em></strong> 或 <strong><em>permanent hosting on Spaces</em></strong>。`,ee,g,Se="我們將很快介紹這兩種方法。 但在分享演示之前，您可能需要完善它 💅.",le,f,te,h,We='<img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter9/gradio-demo-overview.png" alt="Overview of a gradio interface"/> <img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter9/gradio-demo-overview-dark.png" alt="Overview of a gradio interface"/>',se,C,Ze="為了給你的演示添加額外的內容，<code>Interface</code> 類支持一些可選參數：",ae,I,Qe=`<li><code>title</code>：你可以給你的演示一個標題，它出現在輸入和輸出組件的上方。</li> <li><code>description</code>：您可以為界面提供描述（文本、Markdown 或 HTML），顯示在輸入和輸出組件的上方和標題下方。</li> <li><code>article</code>：您還可以編寫擴展文章（文本、Markdown 或 HTML）來解釋界面。如果提供，它會出現在輸入和輸出組件的_下方。</li> <li><code>theme</code>：不喜歡默認顏色？將主題設置為使用 <code>default</code>、<code>huggingface</code>、<code>grass</code>、<code>peach</code> 之一。您還可以添加 <code>dark-</code> 前綴，例如<code>dark-peach</code> 用於深色主題（或者只是 <code>dark</code> 用於默認的深色主題）。</li> <li><code>examples</code>：為了讓您的演示<em>更易於使用</em>，您可以為函數提供一些示例輸入。它們出現在 UI 組件下方，可用於填充界面。這些應該作為嵌套列表提供，其中外部列表​​由樣本組成，每個內部列表對應於每個輸入組件的輸入組成。</li> <li><code>live</code>：如果你想讓你的演示“活”，這意味著你的模型每次輸入更改時都會重新運行，你可以設置 <code>live=True</code>。這對使用快速模型很有意義（我們將在本節末尾看到一個示例）
使用上面的選項，我們最終得到了一個更完整的界面。 運行下面的代碼，以便與 Rick and Morty 聊天：</li>`,ne,k,ie,B,He="使用上面的選項，我們最終得到了一個更完整的界面。 試試下面的界面：",oe,r,xe,pe,v,ce,G,Ye=`現在我們已經有了機器學習模型的工作演示，讓我們學習如何輕鬆共享指向我們界面的鏈接。
通過在 <code>launch()</code> 方法中設置 <code>share=True</code> 可以輕鬆地公開共享接口：`,re,R,Me,$,Fe="這會生成一個公開的、可共享的鏈接，您可以將其發送給任何人！ 當您發送此鏈接時，另一方的用戶可以在瀏覽器中試用該模型長達 72 小時。 因為處理發生在您的設備上（只要您的設備保持開啟！），您不必擔心打包任何依賴項。 如果您使用 Google Colab 筆記本工作，則始終會自動創建共享鏈接。 它通常看起來像這樣：<strong>XXXXX.gradio.app</strong>。 雖然鏈接是通過 Gradio 鏈接提供的，但我們只是您本地服務器的代理，不會存儲通過接口發送的任何數據。",me,S,_e="但是請記住，這些鏈接是可公開訪問的，這意味著任何人都可以使用您的模型進行預測！ 因此，請確保不要通過您編寫的函數公開任何敏感信息，或允許在您的設備上發生任何關鍵更改。 如果設置 <code>share=False</code>（默認值），則僅創建本地鏈接。",ue,W,ye,Z,ze="可以傳遞給同事的共享鏈接很酷，但是如何永久託管您的演示並讓它存在於互聯網上自己的“空間”中？",de,Q,Le=`Hugging Face Spaces 提供了在互聯網上永久託管 Gradio 模型的基礎設施，<strong>免費</strong>！ Spaces 允許您創建並推送到（公共或私人）存儲庫，
你的 Gradio 在哪裡
接口代碼將存在於 <code>app.py</code> 文件中。 <a href="https://huggingface.co/blog/gradio-spaces" rel="nofollow">閱讀分步教程</a> 開始使用，或觀看下面的示例視頻。`,Je,H,Te,x,Ue,Y,Ne='使用到目前為止我們在各節中學到的知識，讓我們創建我們在<a href="/course/chapter9/1">本章第一節</a>中看到的草圖識別演示。 讓我們為我們的界面添加一些自定義並設置 <code>share=True</code> 以創建一個我們可以傳遞的公共鏈接。',he,F,Ee='我們可以從 <a href="https://huggingface.co/spaces/dawood/Sketch-Recognition/blob/main/class_names.txt" rel="nofollow">class_names.txt</a> 加載標籤，並從 <a href="https://huggingface.co/spaces/dawood/Sketch-Recognition/blob/main/pytorch_model.bin" rel="nofollow">pytorch_model.bin</a>加載預訓練的 pytorch 模型 。 通過點擊鏈接並單擊文件預覽左上角的下載來下載這些文件。 讓我們看看下面的代碼，看看我們如何使用這些文件來加載我們的模型並創建一個<code>predict()</code>函數：',we,_,be,z,Ve="現在我們有了一個<code>predict()</code>函數。 下一步是定義並啟動我們的漸變界面：",je,L,ge,M,qe,fe,N,Xe=`注意 <code>Interface</code> 中的 <code>live=True</code> 參數，這意味著草圖演示使
每次有人在畫板上畫畫時的預測（沒有提交按鈕！）。`,Ce,E,Ae=`此外，我們還在 <code>launch()</code> 方法中設置了 <code>share=True</code> 參數。
這將創建一個公共鏈接，您可以發送給任何人！ 當您發送此鏈接時，對方的用戶可以嘗試草圖識別模型。 重申一下，您還可以在 Hugging Face Spaces 上託管模型，這就是我們能夠嵌入上面的演示的方式。`,Ie,V,De="接下來，我們將介紹 Gradio 可用於 Hugging Face 生態系統的其他方式！",ke,X,Be;return w=new A({props:{title:"與他人分享演示",local:"與他人分享演示",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}}),v=new A({props:{title:"使用臨時鏈接分享您的演示",local:"使用臨時鏈接分享您的演示",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 上託管您的演示",local:"在-hugging-face-spaces-上託管您的演示",headingTag:"h3"}}),H=new nl({props:{id:"LS9Y2wDVI0k"}}),x=new A({props:{title:"✏️ 讓我們應用它!",local:"-讓我們應用它",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:"aW50ZXJmYWNlJTIwJTNEJTIwZ3IuSW50ZXJmYWNlKCUwQSUyMCUyMCUyMCUyMHByZWRpY3QlMkMlMEElMjAlMjAlMjAlMjBpbnB1dHMlM0QlMjJza2V0Y2hwYWQlMjIlMkMlMEElMjAlMjAlMjAlMjBvdXRwdXRzJTNEJTIybGFiZWwlMjIlMkMlMEElMjAlMjAlMjAlMjB0aGVtZSUzRCUyMmh1Z2dpbmdmYWNlJTIyJTJDJTBBJTIwJTIwJTIwJTIwdGl0bGUlM0QlMjJTa2V0Y2glMjBSZWNvZ25pdGlvbiUyMiUyQyUwQSUyMCUyMCUyMCUyMGRlc2NyaXB0aW9uJTNEJTIyV2hvJTIwd2FudHMlMjB0byUyMHBsYXklMjBQaWN0aW9uYXJ5JTNGJTIwRHJhdyUyMGElMjBjb21tb24lMjBvYmplY3QlMjBsaWtlJTIwYSUyMHNob3ZlbCUyMG9yJTIwYSUyMGxhcHRvcCUyQyUyMGFuZCUyMHRoZSUyMGFsZ29yaXRobSUyMHdpbGwlMjBndWVzcyUyMGluJTIwcmVhbCUyMHRpbWUhJTIyJTJDJTBBJTIwJTIwJTIwJTIwYXJ0aWNsZSUzRCUyMiUzQ3AlMjBzdHlsZSUzRCd0ZXh0LWFsaWduJTNBJTIwY2VudGVyJyUzRVNrZXRjaCUyMFJlY29nbml0aW9uJTIwJTdDJTIwRGVtbyUyME1vZGVsJTNDJTJGcCUzRSUyMiUyQyUwQSUyMCUyMCUyMCUyMGxpdmUlM0RUcnVlJTJDJTBBKSUwQWludGVyZmFjZS5sYXVuY2goc2hhcmUlM0RUcnVlKQ==",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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