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Gradio NYC Hackathon

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This organization invites participants to add gradio demos for the Gradio NYC Hackathon


Hugging Face Gradio NYC Hackathon 2022

We will be hosting an in-person kick-off for the hackathon at the Hugging Face NYC Office. For details on location and logistics email hackathon@hf.co. The Gradio NYC Hackathon is accepting Gradio demo submissions from anyone for a chance to win prizes from Hugging Face, see prizes section and the leaderboard below. The deadline to submit demos is end of day July 31st, 2022 (AOE Time Zone). Find tutorial on getting started with Gradio on Hugging Face here and to get started with the new Gradio Blocks API here


Potential ideas for creating spaces:

Hugging Face Prizes


LeaderBoard for Most Popular Gradio NYC Hackathon

See the Gradio NYC Hackathon Spaces Leaderboard


Getting Started with Hugging Face Spaces & Gradio for the Gradio NYC Hackathon

In this tutorial, we will demonstrate how to showcase your demo with an easy to use web interface using the Gradio Python library and host it on Hugging Face Spaces for the Gradio NYC Hackathon. Also, see https://gradio.app/introduction_to_blocks/, for a more flexible way to build Gradio Demos


πŸš€ Create a Gradio Demo from your Model

The first step is to create a web demo from your model. As an example, we will be creating a demo from an image classification model (called model) which we will be uploading to Spaces. The full code for steps 1-4 can be found in this colab notebook.



1. Install the gradio library

All you need to do is to run this in the terminal: pip install gradio


2. Define a function in your Python code that performs inference with your model on a data point and returns the prediction

Here’s we define our image classification model prediction function in PyTorch (any framework, like TensorFlow, scikit-learn, JAX, or a plain Python will work as well):

def predict(inp):

inp = Image.fromarray(inp.astype('uint8'), 'RGB')

inp = transforms.ToTensor()(inp).unsqueeze(0)

with torch.no_grad():

prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)

return {labels[i]: float(prediction[i]) for i in range(1000)}

3. Then create a Gradio Interface using the function and the appropriate input and output types

For the image classification model from Step 2, it would like like this:


inputs = gr.inputs.Image()

outputs = gr.outputs.Label(num_top_classes=3)

io = gr.Interface(fn=predict, inputs=inputs, outputs=outputs)

If you need help creating a Gradio Interface for your model, check out the Gradio Getting Started guide.


4. Then launch() you Interface to confirm that it runs correctly locally (or wherever you are running Python)


io.launch() 

You should see a web interface like the following where you can drag and drop your data points and see the predictions:

Gradio Interface

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