Gradio requires Python 3. Once you have Python, you can download the latest version of gradio using pip, like this:
gradio
- pip install gradio -
Or you may want to do pip3 install gradio if you have multiple installations of Python.
pip3 install gradio
Creating an interface using gradio involves just adding a few lines to your existing code. For example, if you - have trained a Tensorflow Keras model, you can create an interface like this:
pip install gradio
- import gradio, tensorflow as tf - image_mdl = tf.keras.models.Sequential() - # ... define and train the model as you would - normally - io = gradio.Interface(inputs=“imageupload", outputs=“label”, model_type=“keras”,model=image_mdl) - io.launch() -
Or you may need to do pip3 install gradio if you have multiple installations of Python.
Creating an interface using gradio involves just adding a few lines to your existing code. For example, here's an + how to create a gradio interface using a pretrained keras model:
keras
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()
Running the code above will open a new browser window with the following interface running:
Running a GradIO interface requires creating an Interface(inputs : str, @@ -86,44 +77,41 @@ keras. model – the actual model to use for processing.
Interface(inputs : str, @@ -86,44 +77,41 @@ keras. model – the actual model to use for processing.
model
Instead of providing the string names for inputs and outputs, objects that represent input and output interfaces can be provided. For example, the above code is identical to:
inputs
outputs
- import gradio, tensorflow as tf - mdl = tf.keras.models.Sequential() - # ... - define and train the model as you would normally - inp = gradio.inputs.ImageUpload() - out = gradio.outputs.Label() - io = gradio.Interface(inputs=inp, outputs=out, model_type=“keras”, model=mdl) - io.launch() -
This allows for customization of the interfaces, by passing in arguments to the input and output constructors. The parameters that each interface accepts is described below.
Instead of providing the string names for inputs and outputs, objects that represent input and output interfaces can be provided. For example, the code + in the Basic Usage section executes identically as:
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()
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.
This is the list of currently supported interfaces in GradIO. All input interfaces can be paired with any output interface.
inputs=“text”
Use this interface to enter text as your input.
Use this interface to enter text as your input. Parameters: None +
inputs=“image_file”
Use this interface to upload images to your model.
inputs=“imageupload”
Use this interface to upload images to your model. Parameters: + shape – a tuple with the shape which the uploaded image should be resized to before passing into the model. Default: (224, 224, 3) + image_mode – 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: 'RGB' + scale – A float used to rescale each pixel value in the image. Default: 1/127.5 + shift – A float used to shift each pixel value in the image after scaling. Default: -1 + cropper_aspect_ratio – Either None or a float that is the aspect ratio of the cropper. Default: None +
shape
(224, 224, 3)
image_mode
'RGB'
scale
1/127.5
shift
-1
cropper_aspect_ratio
None
inputs=“snapshot”
Use this interface to take snapshots from the user's webcam.
Use this interface to take snapshots from the user's webcam. Parameters: + shape – a tuple with the shape which the uploaded image should be resized to before passing into the model. Default: (224, 224, 3) + image_mode – 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: 'RGB' + scale – A float used to rescale each pixel value in the image. Default: 1/127.5 + shift – A float used to shift each pixel value in the image after scaling. Default: -1 + cropper_aspect_ratio – Either None or a float that is the aspect ratio of the cropper. Default: None +
inputs=“sketchpad”
Use this interface to take simple monochrome cketches as input.
Use this interface to take simple monochrome cketches as input. Parameters: + shape – a tuple with the shape which the uploaded image should be resized to before passing into the model. Default: (224, 224, 3) + invert_colors – a boolean that designates whether the colors should be inverted before passing into the model. Default: True +
invert_colors
True
outputs=“classifier”
Use this interface for classification. Responds with confidence intervals.
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 gradio.Input / + gradio.Output objects with custom parameters, or (2) supplying custom preprocessing/postprocessing functions.
gradio.Input
gradio.Output
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:
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, show_confidences=False) +io = gradio.Interface(inputs=inp, outputs=out, model_type="keras", model=mdl) +io.launch()
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.
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()
model_type="sklearn"
This allows you to pass in scikit-learn models, and get predictions from the model. Here's a complete example of training a sklearn model and creating a gradio interface around it. +
sklearn
model_type="keras"
model_type="pytorch"
model_type="pyfunc"
Running - inbrowser – the string representing +
inbrowser
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:
io.launch(inbrowser=True, inline=False, validate=False, share=True) +
inbrowser – the string representing the input interface to be used, or a subclass of gradio.AbstractInput for additional customization (see below). inline – the string representing the output interface to be used, , or a subclass of gradio.AbstractOutput for additional customization (see below).
gradio.AbstractInput
inline
gradio.AbstractOutput