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import gradio as gr |
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from gradio_huggingfacehub_search import HuggingfaceHubSearch |
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from transformers import pipeline |
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with gr.Blocks() as demo: |
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gr.Markdown("## 🐇 Transformers Pipeline Playground") |
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gr.Markdown( |
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"Search for a model on the Hub en explore its output performance on CPU. Some interesting categories are [Text Classification](https://huggingface.co/models?pipeline_tag=image-classification&sort=trending), [Token Classification](https://huggingface.co/models?pipeline_tag=token-classification&sort=trending), [Question Answering](https://huggingface.co/models?pipeline_tag=question-answering&sort=trending) or [Image Classification](https://huggingface.co/models?pipeline_tag=image-classification&sort=trending)." |
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) |
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search_in = HuggingfaceHubSearch( |
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label="Hub Search", |
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placeholder="Search for a model", |
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search_type="model", |
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sumbit_on_select=True, |
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) |
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@gr.render(inputs=[search_in], triggers=[search_in.submit]) |
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def get_interface_from_repo(repo_id: str, progress: gr.Progress = gr.Progress()): |
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try: |
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progress(0.0, desc="Loading model") |
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pipe = pipeline(model=repo_id) |
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progress(1.0, desc="Model loaded") |
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gr.Interface.from_pipeline(pipe, flagging_mode="never") |
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except Exception as e: |
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gr.Markdown(f"This model is not supported. It might be too large or it does not work with Gradio. Try another model. Failed with expection: {e}") |
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if __name__ == "__main__": |
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demo.launch() |
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