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