Hugging Face's logo

Spaces:
CVPR
/
README
Running

App Files Community
1
README / README.md
akhaliq's picture
akhaliq HF Staff
update agian
c420bd9
preview code
|
raw
history blame
4.18 kB
metadata
title: README
emoji: 📉
colorFrom: indigo
colorTo: indigo
sdk: static
pinned: false

CALL FOR DEMOS: https://cvpr2022.thecvf.com/call-demos

The demo track calling for papers is ended. This organization invites participants to showoff conference papers on huggingface as a Gradio Web Demo

Join organization by clicking here

Tutorial on Setting up Gradio Demo on Hugging Face: https://huggingface.co/blog/gradio-spaces

Gradio Banner

Hugging Face Spaces & Gradio for Showcasing your CVPR ‘22 Demo

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 so that conference attendees can easily find and try out your demos.

🚀 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