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Ali Abdalla
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README.md
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README.md
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<img src="https://i.ibb.co/GHRk2JP/header-2.png" alt="drawing" width="1000"/>
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At Gradio, we often try to understand what inputs that a model is particularly sensitive to. To help facilitate this, we've developed and open-sourced `gradio`, a python library that allows you to easily create input and output interfaces over trained models to make it easy for you to "play around" with your model in your browser by dragging-and-dropping in your own images (or pasting your own text, recording your own voice, etc.) and seeing what the model outputs. We are working on making creating a shareable, public link to your model so you can share the interface with others (e.g. your client, your advisor, or your dad), who can use the model without writing any code.
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At Gradio, we often try to understand what inputs a model is particularly sensitive to. To help facilitate this, we've developed and open-sourced `gradio`, a python library that allows you to quickly create input and output interfaces over trained models to make it easy for you to "play around" with your model in your browser by dragging-and-dropping in your own images (or pasting your own text, recording your own voice, etc.) and seeing what the model outputs. `gradio` also allows you to generate a share link which allows anyone, anywhere to use the interface as the model continues to run on your machine.
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Gradio is useful for:
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* Creating demos of your machine learning code for clients / collaborators / users
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* Getting feedback on model performance from users
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* Debugging your model interactively during development
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For more details, see the accompanying paper: ["Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild"](https://arxiv.org/pdf/1906.02569.pdf), *ICML HILL 2019*, and please use the citation below.
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To get a sense of `gradio`, take a look at a few of these examples and colab notebooks, and find more on our website: www.gradio.app.
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## Installation
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```
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pip install gradio
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```
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(you may need to replace `pip` with `pip3` if you're running `python3`).
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## Usage
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Gradio is very easy to use with your existing code. Here are a few working examples:
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### 0. Hello World [](https://colab.research.google.com/drive/18ODkJvyxHutTN0P5APWyGFO_xwNcgHDZ?usp=sharing)
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Let's start with a basic function (no machine learning yet!) that greets an input name. We'll wrap the function with a `Text` to `Text` interface.
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```python
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import gradio as gr
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def greet(name):
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return "Hello " + name + "!"
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gr.Interface(fn=greet, inputs="text", outputs="text").launch()
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```
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The core Interface class is initialized with three parameters:
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- `fn`: the function to wrap
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- `inputs`: the name of the input interface
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- `outputs`: the name of the output interface
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Calling the `launch()` function of the `Interface` object produces the interface shown in image below.
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### 1. Inception Net [](https://colab.research.google.com/drive/1c6gQiW88wKBwWq96nqEwuQ1Kyt5LejiU?usp=sharing)
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Now, let's do a machine learning example. We're going to wrap an
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interface around the InceptionV3 image classifier, which we'll load
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using Tensorflow! Since this is an image classification model, we will use the `Image` input interface.
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We'll output a dictionary of labels and their corresponding confidence scores with the `Label` output
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interface. (The original Inception Net architecture [can be found here](https://arxiv.org/abs/1409.4842))
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```python
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import gradio as gr
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import tensorflow as tf
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import numpy as np
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import requests
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inception_net = tf.keras.applications.InceptionV3() # load the model
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# Download human-readable labels for ImageNet.
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response = requests.get("https://git.io/JJkYN")
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labels = response.text.split("\n")
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def classify_image(inp):
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inp = inp.reshape((-1, 299, 299, 3))
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inp = tf.keras.applications.inception_v3.preprocess_input(inp)
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prediction = inception_net.predict(inp).flatten()
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return {labels[i]: float(prediction[i]) for i in range(1000)}
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image = gr.inputs.Image(shape=(299, 299, 3))
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label = gr.outputs.Label(num_top_classes=3)
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gr.Interface(fn=classify_image, inputs=image, outputs=label).launch()
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```
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This code will produce the interface below. The interface gives you a way to test
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Inception Net by dragging and dropping images, and also allows you to use naturally modify the input image using image editing tools that
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appear when you click EDIT. Notice here we provided actual `gradio.inputs` and `gradio.outputs` objects to the Interface
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function instead of using string shortcuts. This lets us use built-in preprocessing (e.g. image resizing)
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and postprocessing (e.g. choosing the number of labels to display) provided by these
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interfaces.
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You can supply your own model instead of the pretrained model above, as well as use different kinds of models or functions. Here's a list of the interfaces we currently support, along with their preprocessing / postprocessing parameters:
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**Input Interfaces**:
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- Sketchpad(shape=(28, 28), invert_colors=True, flatten=False, scale=1/255, shift=0, dtype='float64')
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- Webcam(image_width=224, image_height=224, num_channels=3, label=None)
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- Textbox(lines=1, placeholder=None, label=None, numeric=False)
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- Radio(choices, label=None)
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- Dropdown(choices, label=None)
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- CheckboxGroup(choices, label=None)
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- Slider(minimum=0, maximum=100, default=None, label=None)
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- Image(shape=(224, 224, 3), image_mode='RGB', scale=1/127.5, shift=-1, label=None)
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- Microphone()
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**Output Interfaces**:
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- Label(num_top_classes=None, label=None)
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- KeyValues(label=None)
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- Textbox(lines=1, placeholder=None, label=None)
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- Image(label=None, plot=False)
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Interfaces can also be combined together, for multiple-input or multiple-output models.
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### 2. Real-Time MNIST [](https://colab.research.google.com/drive/1LXJqwdkZNkt1J_yfLWQ3FLxbG2cAF8p4?usp=sharing)
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Let's wrap a fun `Sketchpad`-to-`Label` UI around MNIST. For this example, we'll take advantage of the `live`
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feature in the library. Set `live=True` inside `Interface()`> to have it run continuous predictions.
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We've abstracted the model training from the code below, but you can see the full code on the colab link.
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```python
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import tensorflow as tf
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import gradio as gr
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from urllib.request import urlretrieve
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urlretrieve("https://gr-models.s3-us-west-2.amazonaws.com/mnist-model.h5","mnist-model.h5")
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model = tf.keras.models.load_model("mnist-model.h5")
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def recognize_digit(inp):
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prediction = model.predict(inp.reshape(1, 28, 28, 1)).tolist()[0]
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return {str(i): prediction[i] for i in range(10)}
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sketchpad = gr.inputs.Sketchpad()
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label = gr.outputs.Label(num_top_classes=3)
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gr.Interface(fn=recognize_digit, inputs=sketchpad,
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outputs=label, live=True).launch()
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```
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This code will produce the interface below.
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You can find many more examples on our website (GPT-2, model comparison, multiple inputs, numerical interfaces): www.gradio.app
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## Contributing:
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If you would like to contribute and your contribution is small, you can directly open a pull request (PR). If you would like to contribute a larger feature, we recommend first creating an issue with a proposed design for discussion. Please see our contributing guidelines for more info.
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## License:
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Gradio is licensed under the Apache License 2.0
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## See more:
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Find more info on usage here: www.gradio.app.
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See, also, the accompanying paper: ["Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild"](https://arxiv.org/pdf/1906.02569.pdf), *ICML HILL 2019*, and please use the citation below.
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```
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@article{abid2019gradio,
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@ -22,80 +158,4 @@ year={2019}
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}
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```
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To get a sense of `gradio`, take a look at the at the `examples` and `demo` folders, or read on below! And be sure to visit the gradio website: www.gradio.app.
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## Installation
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```
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pip install gradio
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```
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(you may need to replace `pip` with `pip3` if you're running `python3`).
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## Usage
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Gradio is very easy to use with your existing code. Here's a working example:
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```python
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import gradio
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import tensorflow as tf
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from imagenetlabels import idx_to_labels
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def classify_image(inp):
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inp = inp.reshape((1, 224, 224, 3))
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prediction = mobile_net.predict(inp).flatten()
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return {idx_to_labels[i].split(',')[0]: float(prediction[i]) for i in range(1000)}
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imagein = gradio.inputs.Image(shape=(224, 224, 3))
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label = gradio.outputs.Label(num_top_classes=3)
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gr.Interface(classify_image, imagein, label, capture_session=True).launch();
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```
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You can supply your own model instead of the pretrained model above, as well as use different kinds of models or functions. Changing the `input` and `output` parameters in the `Interface` face object allow you to create different interfaces, depending on the needs of your model. Take a look at the python notebooks for more examples. The currently supported interfaces are as follows:
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**Input interfaces**:
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* Sketchpad
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* ImageUplaod
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* Webcam
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* Textbox
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**Output interfaces**:
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* Label
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* Textbox
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## Screenshots
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Here are a few screenshots that show examples of gradio interfaces
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#### MNIST Digit Recognition (Input: Sketchpad, Output: Label)
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```python
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sketchpad = Sketchpad()
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label = Label(num_top_classes=4)
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gradio.Interface(predict, sketchpad, label).launch();
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```
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#### Human DNA Variant Effect Prediction (Input: Textbox, Output: Label)
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```python
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gradio.Interface(predict, 'textbox', 'label').launch()
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```
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### Contributing:
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If you would like to contribute and your contribution is small, you can directly open a pull request (PR). If you would like to contribute a larger feature, we recommend first creating an issue with a proposed design for discussion. Please see our contributing guidelines for more info.
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### License:
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Gradio is licensed under the Apache License 2.0
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### See more:
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Find more info on usage here: www.gradio.app.
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