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# blog: https://huggingface.co/blog/gradio-spaces | |
# It's so easy to demonstrate a Machine Learning project thanks to Gradio. | |
# In this blog post, we'll walk you through: | |
# the recent Gradio integration that helps you demo models from the Hub seamlessly with few lines of code leveraging the Inference API. | |
# how to use Hugging Face Spaces to host demos of your own models. | |
# Hugging Face Hub Integration in Gradio | |
# You can demonstrate your models in the Hub easily. You only need to define the Interface that includes: | |
# The repository ID of the model you want to infer with | |
# A description and title | |
# Example inputs to guide your audience | |
# After defining your Interface, just call .launch() and your demo will start running. You can do this in Colab, but if you want to share it with the community a great option is to use Spaces! | |
# Spaces are a simple, free way to host your ML demo apps in Python. To do so, you can create a repository at https://huggingface.co/new-space and select Gradio as the SDK. Once done, you can create a file called app.py, copy the code below, and your app will be up and running in a few seconds! | |
# pip install gradio before running the code below | |
# example for mGPT: Few-Shot Learners Go Multilingual (https://huggingface.co/sberbank-ai/mGPT) to load and launch a gradio demo in a few lines of code | |
import gradio as gr | |
gr.Interface.load("huggingface/sberbank-ai/mGPT").launch() |
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