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from huggingface_hub import logging, login |
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login(token=os.environ.get("HF_TOKEN"), write_permission=True) |
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logging.set_verbosity_debug() |
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import os |
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from pathlib import Path |
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import gradio as gr |
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from app_1M_image import get_demo as get_demo_1M_image |
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from app_image import get_demo as get_demo_image |
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from app_json import get_demo as get_demo_json |
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def _get_demo_code(path: str) -> str: |
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code = Path(path).read_text() |
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code = code.replace("def get_demo():", "with gr.Blocks() as demo:") |
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code += "\n\ndemo.launch()" |
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return code |
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DEMO_EXPLANATION = """ |
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<h1 style='text-align: center; margin-bottom: 1rem'> How to persist data from a Space to a Dataset? </h1> |
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This demo shows how to leverage both `gradio` and `huggingface_hub` to save data from a Space to a Dataset on the Hub. |
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When doing so, a few things must be taken care of: file formats, concurrent writes, name collision, number of commits, |
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number of files,... The tabs below shows different ways of implementing a "save to dataset" feature. Depending on the |
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complexity and usage of your app, you might want to use one or the other. |
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This Space demo comes as a pair with this guide. If you need more technical details, please refer to it. |
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""" |
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JSON_DEMO_EXPLANATION = """ |
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## Use case |
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- Save inputs and outputs |
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- Build an annotation platform |
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## Data |
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Json-able only: text and numeric but no binaries. |
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## Robustness |
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Works with concurrent users and replicas. |
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## Limitations |
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if you expect millions of lines, you will need to split the local JSON file into multiple files to avoid getting your file tracked as LFS (5MB) on the Hub. |
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## Demo |
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""" |
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IMAGE_DEMO_EXPLANATION = """ |
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## Use case |
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Save images with metadata (caption, parameters, datetime,...). |
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## Robustness |
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Works with concurrent users and replicas. |
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## Limitations |
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- only 10k images/folder supported on the Hub. If you expect more usage, you must save data in subfolders. |
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- only 1M images/repo supported on the Hub. If you expect more usage, you can zip your data before upload. See the _1M images Dataset_ demo. |
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## Demo |
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""" |
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IMAGE_1M_DEMO_EXPLANATION = """ |
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## Use case: |
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Same as _Image Dataset_ example, but with very high usage expected. |
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## Robustness |
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Works with concurrent users and replicas. |
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## Limitations |
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None. |
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## Demo |
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""" |
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with gr.Blocks() as demo: |
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gr.Markdown(DEMO_EXPLANATION) |
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with gr.Tab("JSON Dataset"): |
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gr.Markdown(JSON_DEMO_EXPLANATION) |
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get_demo_json() |
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gr.Markdown("## Result\n\nhttps://huggingface.co/datasets/Wauplin/example-commit-scheduler-json\n\n## Code") |
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with gr.Accordion("Source code", open=True): |
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gr.Code(_get_demo_code("app_json.py"), language="python") |
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with gr.Tab("Image Dataset"): |
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gr.Markdown(IMAGE_DEMO_EXPLANATION) |
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get_demo_image() |
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gr.Markdown("## Result\n\nhttps://huggingface.co/datasets/Wauplin/example-commit-scheduler-image\n\n## Code") |
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with gr.Accordion("Source code", open=True): |
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gr.Code(_get_demo_code("app_image.py"), language="python") |
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with gr.Tab("1M images Dataset"): |
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gr.Markdown(IMAGE_1M_DEMO_EXPLANATION) |
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get_demo_1M_image() |
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gr.Markdown( |
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"## Result\n\nhttps://huggingface.co/datasets/Wauplin/example-commit-scheduler-image-zip\n\n## Code" |
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) |
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with gr.Accordion("Source code", open=True): |
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gr.Code(_get_demo_code("app_1M_image.py"), language="python") |
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demo.launch() |
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