mirror of
https://github.com/gradio-app/gradio.git
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138 lines
6.3 KiB
HTML
138 lines
6.3 KiB
HTML
<html>
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<head>
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<title>GradIO</title>
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<link href="https://fonts.googleapis.com/css?family=Open+Sans" rel="stylesheet">
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<link href="style/style.css" rel="stylesheet">
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<link href="style/home.css" rel="stylesheet">
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<link href="gradio/gradio.css" rel="stylesheet">
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<link href="gradio/vendor/cropper.css" rel="stylesheet">
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</head>
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<body>
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<nav>
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<a class="selected" href="home.html">GradIO</a>
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<a href="getting_started.html">Getting Started</a>
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<a href="sharing.html">Sharing</a>
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</nav>
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<div id="hero-section"><!--
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--><div id="intro">
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<img id="logo" src="img/logo.png"/>
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<p>Gradio is a free, open-source python library that helps machine
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learning researchers <strong>interact</strong> with and <strong>share</strong> their machine
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learning models with collaborators and clients with only a few lines of extra code.</p>
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<p>With gradio, you can easily generate in-browser interfaces that
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enable you to enter various forms of input for your model and explore
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the behavior of your model immediately. Gradio also generates <strong>links</strong>
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that can be shared with collaborators and other audiences, so they can
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interact with the model without setting up any software or even having
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any background in machine learning or software at all!</p>
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<p>Visit the <a href="https://github.com/abidlabs/gradio"
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target="_blank">Gradio GitHub >></a></p>
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<p>Gradio was developed by researchers at Stanford University and is
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under the Apache license.</p>
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</div><!--
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--><div id="demos">
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<!-- <div id="demo-nav">
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<button demo="1" class="selected demo-link">
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<span class="demo-count">Demo 1</span><br>Handwriting Digit
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</button>
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<button demo="2" class="demo-link">
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<span class="demo-count">Demo 2</span><br>Impressionist Painter
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</button>
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<button demo="3" class="demo-link">
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<span class="demo-count">Demo 3</span><br>Expression Detection
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</button>
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<button demo="4" class="demo-link">
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<span class="demo-count">Demo 4</span><br>Text Summaries
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</button>
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<button demo="5" class="demo-link">
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<span class="demo-count">Demo 5</span><br>Hotdog or Not Hotdog
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</button>
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</div> -->
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<div id="demo-code">
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<div id="demo-code-label">image_detector.py</div>
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<div class="codeblock"><code>
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import <span class="var">gradio</span>, tensorflow as <span
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class="var">tf</span><br>
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<span class="var">image_mdl</span> = tf.keras.models.<span
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class="func">Sequential()</span><br>
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<span class="comm"># ... define and train the model as you would
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normally</span><br>
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<span class="var">io</span> = gradio.<span
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class="func">Interface(</span><span
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class="var">input</span>=“imageupload", <span
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class="var">output</span>=“label”, <span
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class="var">model_type</span>=“keras”,<span
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class="var">model</span>=image_mdl<span
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class="func">)</span><br>
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io.<span class="func">launch()</span>
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</code></div>
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</div>
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<div class="instructions">
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The code above produces produces an interface like this, in which you can upload an image and receive labelling as output. Click
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submit to get the prediction!
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</div>
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<div id="gradio">
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<div class="panel">
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<div class="gradio input image_file">
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<div class="role">Input</div>
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<div class="input_image drop_mode">
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<div class="input_caption">Drop Image Here<br>- or -<br>Click to Upload</div>
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<img />
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</div>
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<input class="hidden_upload" type="file" accept="image/x-png,image/gif,image/jpeg" />
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</div>
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<input class="submit" type="submit" value="Submit"/><!--
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--><input class="clear" type="reset" value="Clear">
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</div><!--
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--><div class="panel">
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<div class="gradio output classifier">
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<div class="panel_head">
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<div class="role">Output</div>
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</div>
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<div class="output_class"></div>
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<div class="confidence_intervals">
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</div>
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</div>
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</div>
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</div>
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</div><!--
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--></div>
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<img id="invisible_img" style="display: none" />
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<div id="summaries">
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<div id="setup" class="summary_box">
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<h2>Fast, easy setup</h2>
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<p>Using Gradio only requires adding a couple lines of code to your
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project. You can install Gradio from pip and deploy your model in
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seconds. Once launched, you can choose from a variety of interface
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types to interact with, iterate over, and improve your models.</p>
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<p>More on <a href="getting_started.html">Getting Stared >></a><p>
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</div>
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<div id="present" class="summary_box">
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<h2>Present and share</h2>
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<p>Gradio present an interface that is intuitive to engineers and
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non-engineers alike, and thus a valuable tool in sharing insights from
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your models. When Gradio launches a model, it also creates a link you
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can share with colleagues that lets them interact with the model
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on your computer remotely from their own devices.</p>
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<p>More on <a href="sharing.html">Sharing >></a><p>
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</div>
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<div id="embed" class="summary_box">
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<h2>Embed and go</h2>
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<p>Gradio can be embedded in Jupyter and Colab notebooks, in blogs and
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websites, and screenshotted for use in research papers. These features
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all help your models be more easily shared and consumed with a larger
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audience.</p>
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</div>
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</div>
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<footer>
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<img src="img/logo_inline.png" />
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</footer>
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<script src="gradio/vendor/jquery.min.js"></script>
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<script src="gradio/image_upload.js"></script>
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<script src="gradio/label.js"></script>
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<script src="gradio/vendor/cropper.js"></script>
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<script src="https://unpkg.com/ml5@0.1.3/dist/ml5.min.js"></script>
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<script src="js/models.js"></script>
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<body>
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</html>
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