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<h1 class="relative group"><a id="advanced-interface-features" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#advanced-interface-features"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Advanced Interface features
	</span></h1>

<p>Now that we can build and share a basic interface, let’s explore some more advanced features such as state, interpretation and authentication.</p>
<h3 class="relative group"><a id="using-state-to-persist-data" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#using-state-to-persist-data"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Using state to persist data
	</span></h3>

<p>Gradio supports <em>session state</em>, where data persists across multiple submits within a
page load. Session state is useful for building demos of, for example, chatbots where you want to
persist data as the user interacts with the model. Note that session state does not share data between different users of your model.</p>
<p>To store data in a session state, you need to do three things:</p>
<ol><li>Pass in an <em>extra parameter</em> into your function, which represents the state of the interface.</li>
<li>At the end of the function, return the updated value of the state as an <em>extra return value</em>.</li>
<li>Add the ‘state’ input and ‘state’ output components when creating your <code>Interface</code>.</li></ol>
<p>See the chatbot example below:</p>

	<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
	<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">import</span> random

<span class="hljs-keyword">import</span> gradio <span class="hljs-keyword">as</span> gr


<span class="hljs-keyword">def</span> <span class="hljs-title function_">chat</span>(<span class="hljs-params">message, history</span>):
    history = history <span class="hljs-keyword">or</span> []
    <span class="hljs-keyword">if</span> message.startswith(<span class="hljs-string">&quot;How many&quot;</span>):
        response = random.randint(<span class="hljs-number">1</span>, <span class="hljs-number">10</span>)
    <span class="hljs-keyword">elif</span> message.startswith(<span class="hljs-string">&quot;How&quot;</span>):
        response = random.choice([<span class="hljs-string">&quot;Great&quot;</span>, <span class="hljs-string">&quot;Good&quot;</span>, <span class="hljs-string">&quot;Okay&quot;</span>, <span class="hljs-string">&quot;Bad&quot;</span>])
    <span class="hljs-keyword">elif</span> message.startswith(<span class="hljs-string">&quot;Where&quot;</span>):
        response = random.choice([<span class="hljs-string">&quot;Here&quot;</span>, <span class="hljs-string">&quot;There&quot;</span>, <span class="hljs-string">&quot;Somewhere&quot;</span>])
    <span class="hljs-keyword">else</span>:
        response = <span class="hljs-string">&quot;I don&#x27;t know&quot;</span>
    history.append((message, response))
    <span class="hljs-keyword">return</span> history, history


iface = gr.Interface(
    chat,
    [<span class="hljs-string">&quot;text&quot;</span>, <span class="hljs-string">&quot;state&quot;</span>],
    [<span class="hljs-string">&quot;chatbot&quot;</span>, <span class="hljs-string">&quot;state&quot;</span>],
    allow_screenshot=<span class="hljs-literal">False</span>,
    allow_flagging=<span class="hljs-string">&quot;never&quot;</span>,
)
iface.launch()<!-- HTML_TAG_END --></pre></div>
<iframe src="https://hf.space/gradioiframe/dawood/Chatbot-Demo/+" frameborder="0" height="350" title="Gradio app" class="container p-0 flex-grow space-iframe" allow="accelerometer; ambient-light-sensor; autoplay; battery; camera; document-domain; encrypted-media; fullscreen; geolocation; gyroscope; layout-animations; legacy-image-formats; magnetometer; microphone; midi; oversized-images; payment; picture-in-picture; publickey-credentials-get; sync-xhr; usb; vr ; wake-lock; xr-spatial-tracking" sandbox="allow-forms allow-modals allow-popups allow-popups-to-escape-sandbox allow-same-origin allow-scripts allow-downloads"></iframe>
<p>Notice how the state of the output component persists across submits.
Note: you can pass in a default value to the state parameter,
which is used as the initial value of the state.</p>
<h3 class="relative group"><a id="using-interpretation-to-understand-predictions" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#using-interpretation-to-understand-predictions"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Using interpretation to understand predictions
	</span></h3>

<p>Most machine learning models are black boxes and the internal logic of the function is hidden from the end user. To encourage transparency, we’ve made it very easy to add interpretation to your model by simply setting the interpretation keyword in the Interface class to default. This allows your users to understand what parts of the input are responsible for the output. Take a look at the simple interface below which shows an image classifier that also includes interpretation:</p>

	<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
	<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">import</span> requests
<span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-keyword">import</span> gradio <span class="hljs-keyword">as</span> gr

inception_net = tf.keras.applications.MobileNetV2()  <span class="hljs-comment"># load the model</span>

<span class="hljs-comment"># Download human-readable labels for ImageNet.</span>
response = requests.get(<span class="hljs-string">&quot;https://git.io/JJkYN&quot;</span>)
labels = response.text.split(<span class="hljs-string">&quot;\n&quot;</span>)


<span class="hljs-keyword">def</span> <span class="hljs-title function_">classify_image</span>(<span class="hljs-params">inp</span>):
    inp = inp.reshape((-<span class="hljs-number">1</span>, <span class="hljs-number">224</span>, <span class="hljs-number">224</span>, <span class="hljs-number">3</span>))
    inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
    prediction = inception_net.predict(inp).flatten()
    <span class="hljs-keyword">return</span> {labels[i]: <span class="hljs-built_in">float</span>(prediction[i]) <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">1000</span>)}


image = gr.Image(shape=(<span class="hljs-number">224</span>, <span class="hljs-number">224</span>))
label = gr.Label(num_top_classes=<span class="hljs-number">3</span>)

title = <span class="hljs-string">&quot;Gradio Image Classifiction + Interpretation Example&quot;</span>
gr.Interface(
    fn=classify_image, inputs=image, outputs=label, interpretation=<span class="hljs-string">&quot;default&quot;</span>, title=title
).launch()<!-- HTML_TAG_END --></pre></div>
<p>Test the interpretation function by submitting an input then clicking Interpret under the output component.</p>
<iframe src="https://hf.space/gradioiframe/akhaliq/gradio-image-interpretation/+" frameborder="0" height="570" title="Gradio app" class="container p-0 flex-grow space-iframe" allow="accelerometer; ambient-light-sensor; autoplay; battery; camera; document-domain; encrypted-media; fullscreen; geolocation; gyroscope; layout-animations; legacy-image-formats; magnetometer; microphone; midi; oversized-images; payment; picture-in-picture; publickey-credentials-get; sync-xhr; usb; vr ; wake-lock; xr-spatial-tracking" sandbox="allow-forms allow-modals allow-popups allow-popups-to-escape-sandbox allow-same-origin allow-scripts allow-downloads"></iframe>
<p>Besides the default interpretation method Gradio provides, you can also specify <code>shap</code> for the <code>interpretation</code> parameter and set the <code>num_shap</code> parameter. This uses Shapley-based interpretation, which you can read more about <a href="https://christophm.github.io/interpretable-ml-book/shap.html" rel="nofollow">here</a>.
Lastly, you can also pass in your own interpretation function into the <code>interpretation</code> parameter. See an example in Gradio’s getting started page <a href="https://gradio.app/getting_started/" rel="nofollow">here</a>.</p>
<h3 class="relative group"><a id="adding-authentication" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#adding-authentication"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Adding authentication
	</span></h3>

<p>You may want to authenticate to your Gradio interface in order to control who can access and use your demo.</p>
<p>Authentication can be added by providing a list of username/password tuples to the <code>auth</code> parameter in the <code>launch()</code> method. For more complex authentication handling, you can pass a function that takes a username and password as arguments, and returns <code>True</code> to allow authentication, <code>False</code> otherwise.</p>
<p>Let’s take the image classification demo above and add authentication:</p>

	<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
	<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">import</span> requests
<span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-keyword">import</span> gradio <span class="hljs-keyword">as</span> gr

inception_net = tf.keras.applications.MobileNetV2()  <span class="hljs-comment"># load the model</span>

<span class="hljs-comment"># Download human-readable labels for ImageNet.</span>
response = requests.get(<span class="hljs-string">&quot;https://git.io/JJkYN&quot;</span>)
labels = response.text.split(<span class="hljs-string">&quot;\n&quot;</span>)


<span class="hljs-keyword">def</span> <span class="hljs-title function_">classify_image</span>(<span class="hljs-params">inp</span>):
    inp = inp.reshape((-<span class="hljs-number">1</span>, <span class="hljs-number">224</span>, <span class="hljs-number">224</span>, <span class="hljs-number">3</span>))
    inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
    prediction = inception_net.predict(inp).flatten()
    <span class="hljs-keyword">return</span> {labels[i]: <span class="hljs-built_in">float</span>(prediction[i]) <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">1000</span>)}


image = gr.Image(shape=(<span class="hljs-number">224</span>, <span class="hljs-number">224</span>))
label = gr.Label(num_top_classes=<span class="hljs-number">3</span>)

title = <span class="hljs-string">&quot;Gradio Image Classifiction + Interpretation Example&quot;</span>
gr.Interface(
    fn=classify_image, inputs=image, outputs=label, interpretation=<span class="hljs-string">&quot;default&quot;</span>, title=title
).launch(auth=(<span class="hljs-string">&quot;admin&quot;</span>, <span class="hljs-string">&quot;pass1234&quot;</span>))<!-- HTML_TAG_END --></pre></div>
<iframe src="https://hf.space/gradioiframe/dawood/Auth-Image-Classification/+" frameborder="0" height="570" title="Gradio app" class="container p-0 flex-grow space-iframe" allow="accelerometer; ambient-light-sensor; autoplay; battery; camera; document-domain; encrypted-media; fullscreen; geolocation; gyroscope; layout-animations; legacy-image-formats; magnetometer; microphone; midi; oversized-images; payment; picture-in-picture; publickey-credentials-get; sync-xhr; usb; vr ; wake-lock; xr-spatial-tracking" sandbox="allow-forms allow-modals allow-popups allow-popups-to-escape-sandbox allow-same-origin allow-scripts allow-downloads"></iframe>
<p>This wraps up our deep dive into the <code>Interface</code> class of Gradio. As we’ve seen, this class makes it simple to create machine learning demos in a few lines of Python code. However, sometimes you’ll want to customise your demo by changing the layout or chaining multiple prediction functions together. Wouldn’t it be nice if we could somehow split the <code>Interface</code> into customizable “blocks”? Fortunately, there is! That’s the topic of the final section.</p>


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