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<h1>Installation</h1>
<p>Gradio requires <a href="https://www.python.org/downloads/">Python 3</a>. Once you have Python, you can download the latest version of <code>gradio</code> using pip, like this:</p>
<pre><code class="bash">pip install gradio</code></pre>
<p>Or you may need to do <code>pip3 install gradio</code> if you have multiple installations of Python.</p>
<h1>Basic Usage</h1>
<p>Creating an interface using gradio involves just adding a few lines to your existing code. For example, here's an
how to create a <code>gradio</code> interface using a pretrained <code>keras</code> model:</p>
<pre><code class="python">import gradio, tensorflow as tf
image_mdl = tf.keras.applications.inception_v3.InceptionV3()
io = gradio.Interface(inputs="imageupload", outputs="label", model_type="keras", model=image_mdl)
io.launch()</code></pre>
<p>Running the code above will open a new browser window with the following interface running:</p>
<div id="gradio">
<div class="panel">
<div class="gradio input image_file">
<div class="role">Input</div>
<div class="input_image drop_mode">
<div class="input_caption">Drop Image Here<br>- or -<br>Click to Upload</div>
<img />
</div>
<input class="hidden_upload" type="file" accept="image/x-png,image/gif,image/jpeg" />
</div>
<input class="submit" type="submit" value="Submit"/><!--
--><input class="clear" type="reset" value="Clear">
</div><!--
--><div class="panel">
<div class="gradio output classifier">
<div class="panel_head">
<div class="role">Output</div>
</div>
<div class="output_class"></div>
<div class="confidence_intervals">
</div>
</div>
</div>
</div>
<p>&nbsp;</p><p>&nbsp;</p>
<h1>Basic Parameters</h1>
<p>Running a GradIO interface requires creating an <code><span
class="func">Interface(</span><span class="var">inputs</span> : str,
<span class="var">outputs</span> : str, <span class="var">model_type</span>
: str, <span class="var">model</span> : Any<span
class="func">)</span></code> object, which takes as input
arguments:<br>
<code><span class="var">inputs</span></code> – the string representing
the input interface to be used, or a subclass of <code>gradio.AbstractInput</code> for additional customization (see <a href="#custom-interfaces">below</a>).<br>
<code><span class="var">outputs</span></code> – the string representing
the output interface to be used, , or a subclass of <code>gradio.AbstractOutput</code> for additional customization (see <a href="#custom-interfaces">below</a>).<br>
<code><span class="var">model_type</span></code> – the string
representing type of model being passed in. Supported types include
keras.<br>
<code><span class="var">model</span></code> – the actual model to use
for processing.</p>
<p>Instead of providing the string names for <code><span class="var">inputs</span></code> and <code><span class="var">outputs</span></code>, objects that represent input and output interfaces can be provided. For example, the code
in the Basic Usage section executes identically as:</p>
<pre><code class="python">import gradio, tensorflow as tf
image_mdl = tf.keras.applications.inception_v3.InceptionV3()
inp = gradio.inputs.ImageUpload()
out = gradio.outputs.Label()
io = gradio.Interface(inputs=inp, outputs=out, model_type="keras", model=mdl)
io.launch()</code></pre>
<p>This allows for customization of the interfaces, by passing in arguments to the input and output constructors. The parameters that each interface constructor accepts is described below.</p>
<h1>Supported Interfaces</h1>
<p id="interfaces_text">This is the list of currently supported interfaces
in GradIO. All input interfaces can be paired with any output interface.
</p>
<div class="interfaces_set">
<div class="inputs_set">
<h2>Input Interfaces</h2>
<h2><code><span class="var">inputs</span>=“text”</code></h2>
<p>Use this interface to enter text as your input. Parameters: <em>None</em>
</p>
<div class="gradio input text">
<div class="role">Input</div>
<textarea class="input_text"
placeholder="Enter text here..."></textarea>
</div>
<h2><code><span class="var">inputs</span>=“imageupload”</code></h2>
<p>Use this interface to upload images to your model. Parameters: <br>
<code><span class="var">shape</span></code> – a tuple with the shape which the uploaded image should be resized to before passing into the model. Default: <code>(224, 224, 3)</code><br>
<code><span class="var">image_mode</span></code> – PIL Image mode that is used to convert the image to a numpy array. Typically either 'RGB' (3 channel RGB) or 'L' (1 channel grayscale). Default: <code>'RGB'</code><br>
<code><span class="var">scale</span></code> – A float used to rescale each pixel value in the image. Default: <code>1/127.5</code><br>
<code><span class="var">shift</span></code> – A float used to shift each pixel value in the image after scaling. Default: <code>-1</code><br>
<code><span class="var">cropper_aspect_ratio</span></code> – Either None or a float that is the aspect ratio of the cropper. Default: <code>None</code><br>
</p>
<div class="gradio input image_file">
<div class="role">Input</div>
<div class="input_image">
Drop Image Here<br>- or -<br>Click to Upload
</div>
</div>
<h2><code><span class="var">inputs</span>=“snapshot”</code></h2>
<p>Use this interface to take snapshots from the user's webcam. Parameters: <br>
<code><span class="var">shape</span></code> – a tuple with the shape which the uploaded image should be resized to before passing into the model. Default: <code>(224, 224, 3)</code><br>
<code><span class="var">image_mode</span></code> – PIL Image mode that is used to convert the image to a numpy array. Typically either 'RGB' (3 channel RGB) or 'L' (1 channel grayscale). Default: <code>'RGB'</code><br>
<code><span class="var">scale</span></code> – A float used to rescale each pixel value in the image. Default: <code>1/127.5</code><br>
<code><span class="var">shift</span></code> – A float used to shift each pixel value in the image after scaling. Default: <code>-1</code><br>
<code><span class="var">cropper_aspect_ratio</span></code> – Either None or a float that is the aspect ratio of the cropper. Default: <code>None</code><br>
</p>
<div class="gradio input snapshot">
<div class="role">Input</div>
<div class="input_snapshot">
<img class="webcam" src="img/webcam.png" />
<div class="input_directions">
Click to Upload a Snapshot from the Webcam.
</div>
</div>
</div>
<h2><code><span class="var">inputs</span>=“sketchpad”</code></h2>
<p>Use this interface to take simple monochrome cketches as input. Parameters: <br>
<code><span class="var">shape</span></code> – a tuple with the shape which the uploaded image should be resized to before passing into the model. Default: <code>(224, 224, 3)</code><br>
<code><span class="var">invert_colors</span></code> – a boolean that designates whether the colors should be inverted before passing into the model. Default: <code>True</code><br>
</p>
<div class="input sketchpad">
<div class="role">Input</div>
</div>
<h2><code><span class="var">inputs</span>=“microphone”</code></h2>
<p>Use this interface to audio input from the microphone.</p>
<div class="gradio input mic">
<div class="role">Input</div>
<div class="input_mic">
<img class="mic" src="img/mic.png" />
<div class="input_directions">
Click to Upload Audio from the Microphone.
</div>
</div>
</div>
<h2><code><span class="var">inputs</span>=“audio_file”</code></h2>
<p>Use this interface to upload audio to your model.</p>
<div class="gradio input audio_file">
<div class="role">Input</div>
<div class="input_audio">
Drop Audio File Here<br>- or -<br>Click to Upload
</div>
</div>
</div><!--
--><div class="outputs_set">
<h2>Output Interfaces</h2>
<h2><code><span class="var">outputs</span>=“classifier”</code></h2>
<p>Use this interface for classification. Responds with confidence
intervals. </p>
<div class="gradio output classifier">
<div class="role">Output</div>
<div class="output_class">happy</div>
<div class="confidence_intervals">
<div class="confidence"><div class="label">happy</div><div class="level" style="width: 219px">73%</div></div>
<div class="confidence"><div class="label">shocked</div><div class="level" style="width: 60px">20%</div></div>
<div class="confidence"><div class="label">sad</div><div class="level" style="width: 15px">&nbsp;</div></div>
<div class="confidence"><div class="label">angry</div><div class="level" style="width: 6px">&nbsp;</div></div>
</div>
</div>
<h2><code><span class="var">outputs</span>=“text”</code></h2>
<p>Use this interface to display the text of your output.</p>
<div class="gradio output text">
<div class="role">Output</div>
<textarea readonly class="output_text">Lorem ipsum consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
</textarea>
</div>
<h2><code><span class="var">outputs</span>=“image”</code></h2>
<p>Use this interface to display the text of your output.</p>
<div class="gradio output image">
<div class="role">Output</div>
<div class="output_image">
<img src="img/altered_logo.png" />
</div>
</div>
</div>
</div>
<h1 id="custom-interfaces">Customizing Interfaces</h1>
<p>In practice, it is fairly typical to customize the input and output interfaces so they preprocess the inputs
in way your model accepts, or postprocesses the result of your model in the appropriate way so that the output interface
can display the result. For example, you may need to adapt the preprocessing of the image upload interface so that
the image is resized to the correct dimensions before being fed into your model. This can be done in one of two ways: (1) instantiating <code>gradio.Input</code> /
<code>gradio.Output</code> objects with custom parameters, or (2) supplying custom preprocessing/postprocessing functions.</p>
<h2>Input/Output Objects with Custom Parameters</h2>
<p>For small, common changes to the input and output interfaces, you can often simply change the parameters in
the constructor of the input and output objects to affect the preprocessing/postprocessing. Here is an example that
resizing the image to a different size before feeding it into the model, and tweaks the output interface to
hide the confidence bars and show the top 5 classes rather than the default 3:</p>
<pre><code class="python">import gradio, tensorflow as tf
image_mdl = tf.keras.applications.inception_v3.InceptionV3()
inp = gradio.inputs.ImageUpload(shape=(299, 299, 3))
out = gradio.outputs.Label(num_top_classes=5)
io = gradio.Interface(inputs=inp, outputs=out, model_type="keras", model=mdl)
io.launch()</code></pre>
<h2>Custom Preprocessing/Postprocessing Functions</h2>
<p>Alternatively, you can completely override the default preprocessing/postprocessing functions by supplying
your own. For example, here we modify the preprocessing function of the ImageUpload interface to add some
noise to the image before feeding it into the model.</p>
<pre><code class="python">import gradio, base64, numpy as np, tensorflow as tf
from io import BytesIO
from PIL import Image
image_mdl = tf.keras.applications.inception_v3.InceptionV3()
def pre(inp):
im = gradio.preprocessing_utils.encoding_to_image(inp)
im = gradio.preprocessing_utils.resize_and_crop(im, (299, 299))
im = np.array(im).flatten()
im = im * 1/127.5 - 1
im = im + np.random.normal(0, 0.1, im.shape) # Adding the noise
array = im.reshape(1, 299, 299, 3)
return array
inp = gradio.inputs.ImageUpload(preprocessing_fn=pre)
io = gradio.Interface(inputs=inp, outputs="label", model_type="keras", model=mdl)
io.launch()</code></pre>
<h1>Model Types</h1>
We currently support the following kinds of models:
<h3><code><span class="var">model_type</span>="sklearn"</code></h3>
<p>This allows you to pass in scikit-learn models, and get predictions from the model. Here's a complete example of training a <code>sklearn</code> model and creating a <code>gradio</code> interface around it.
</p>
<pre><code class="python">from sklearn import datasets, svm
import gradio
digits = datasets.load_digits()
n_samples = len(digits.images)
data = digits.images.reshape((n_samples, -1)) # flatten the images
# Create a classifier: a support vector classifier
classifier = svm.SVC(gamma=0.001)
classifier.fit(data, digits.target)
# The sklearn digits dataset is different from MNIST: it is 8x8 and consists of black digits on a white background.
inp = gradio.inputs.Sketchpad(shape=(8, 8), flatten=True, scale=16/255, invert_colors=False)
io = gradio.Interface(inputs=inp, outputs="label", model_type="sklearn", model=classifier)
io.launch()</code></pre>
<h3><code><span class="var">model_type</span>="keras"</code></h3>
<p>This allows you to pass in keras models, and get predictions from the model. Here's a complete example of training a <code>keras</code> model and creating a <code>gradio</code> interface around it.
</p>
<pre><code class="python">import gradio, tensorflow as tf
(x_train, y_train),(x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)
loss, accuracy = model.evaluate(x_test, y_test)
io = gradio.Interface(inputs="sketchpad", outputs="label", model=model, model_type='keras')
io.launch(inline=True, share=True)</code></pre>
<p><a href="https://colab.research.google.com/drive/1DQSuxGARUZ-v4ZOAuw-Hf-8zqegpmes-">Run this code in a colab notebook</a> to see the interface -- embedded in the notebook.</p>
<h3><code><span class="var">model_type</span>="pytorch"</code></h3>
<p>This allows you to pass in pytorch models, and get predictions from the model. Here's a complete example of training a <code>pytorch</code> model and creating a <code>gradio</code> interface around it.
</p>
<pre><code class="python">import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import gradio
# Device configuration
device = torch.device('cpu')
# Hyper-parameters
input_size = 784
hidden_size = 500
num_classes = 10
num_epochs = 2
batch_size = 100
learning_rate = 0.001
# MNIST dataset
train_dataset = torchvision.datasets.MNIST(root='../../data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = torchvision.datasets.MNIST(root='../../data',train=False, transform=transforms.ToTensor())
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size,shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)
# Fully connected neural network with one hidden layer
class NeuralNet(nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNet, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(hidden_size, num_classes)
def forward(self, x):
out = self.fc1(x)
out = self.relu(out)
out = self.fc2(out)
return out
model = NeuralNet(input_size, hidden_size, num_classes).to(device)
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# Train the model
total_step = len(train_loader)
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
# Move tensors to the configured device
images = images.reshape(-1, 28*28).to(device)
labels = labels.to(device)
# Forward pass
outputs = model(images)
loss = criterion(outputs, labels)
# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
inp = gradio.inputs.Sketchpad(flatten=True, scale=1/255, dtype='float32')
io = gradio.Interface(inputs=inp, outputs="label", model_type="pytorch", model=model)
io.launch()
</code></pre>
<h3><code><span class="var">model_type</span>="pyfunc"</code></h3>
<p>This allows you to pass in an arbitrary python function, and get the outputs from the function. Here's a very simple example of a "model" with a <code>gradio</code> interface around it.
</p>
<pre><code class="python">import gradio
# A very simplistic function that capitalizes each letter in the given string
def big(x):
return x.upper()
io = gradio.Interface(inputs="textbox", outputs="textbox", model=big, model_type='pyfunc')
io.launch(inline=True, share=True)</code></pre>
<p>A more realistic examples of the <code>pyfunc</code> use case may be the following, where we would like to
use a TensorFlow session with a trained model to do predictions. So we wrap the session inside a python function
like this:</p>
<pre><code class="python">import tensorflow as tf
import gradio
n_classes = 10
(x_train, y_train),(x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train, x_test = x_train.reshape(-1, 784) / 255.0, x_test.reshape(-1, 784) / 255.0
y_train = tf.keras.utils.to_categorical(y_train, n_classes).astype(float)
y_test = tf.keras.utils.to_categorical(y_test, n_classes).astype(float)
learning_rate = 0.5
epochs = 5
batch_size = 100
x = tf.placeholder(tf.float32, [None, 784], name="x")
y = tf.placeholder(tf.float32, [None, 10], name="y")
W1 = tf.Variable(tf.random_normal([784, 300], stddev=0.03), name='W1')
b1 = tf.Variable(tf.random_normal([300]), name='b1')
W2 = tf.Variable(tf.random_normal([300, 10], stddev=0.03), name='W2')
hidden_out = tf.add(tf.matmul(x, W1), b1)
hidden_out = tf.nn.relu(hidden_out)
y_ = tf.matmul(hidden_out, W2)
probs = tf.nn.softmax(y_)
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=y_, labels=y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cross_entropy)
init_op = tf.global_variables_initializer()
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
sess = tf.Session()
sess.run(init_op)
total_batch = int(len(y_train) / batch_size)
for epoch in range(epochs):
avg_cost = 0
for start, end in zip(range(0, len(y_train), batch_size), range(batch_size, len(y_train)+1, batch_size)):
batch_x = x_train[start: end]
batch_y = y_train[start: end]
_, c = sess.run([optimizer, cross_entropy], feed_dict={x: batch_x, y: batch_y})
avg_cost += c / total_batch
def predict(inp):
return sess.run(probs, feed_dict={x:inp})
inp = gradio.inputs.Sketchpad(flatten=True)
io = gradio.Interface(inputs=inp, outputs="label", model_type="pyfunc", model=predict)
io.launch(inline=True, share=True)</code></pre>
<h1>Launch Options</h1>
<p>When launching the interface, you have the option to pass in several boolean parameters that determine how the interface is displayed. Here
is an example showing all of the possible parameters:</p>
<pre><code class="python">io.launch(inbrowser=True, inline=False, validate=False, share=True)
</code></pre>
<p><code><span class="var">inbrowser</span></code> – whether the model should launch in a new browser window.<br>
<code><span class="var">inline</span></code> – whether the model should launch embedded in an interactive python environment (like jupyter notebooks or colab notebooks).<br>
<code><span class="var">validate</span></code> – whether gradio should try to validate the interface-model compatibility before launch.<br>
<code><span class="var">share</span></code> – whether a public link to share the model should be created.
for processing.</p>
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