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| import gradio as gr | |
| import uuid | |
| import asyncio | |
| from substra_launcher import launch_substra_space | |
| from huggingface_hub import HfApi | |
| hf_api = HfApi() | |
| theme = gr.themes.Default(primary_hue="blue").set( | |
| background_fill_primary="#F9F2EA", | |
| block_background_fill="#FFFFFF", | |
| ) | |
| async def launch_experiment(hospital_a, hospital_b): | |
| experiment_id = str(uuid.uuid4()) | |
| asyncio.create_task(launch_substra_space( | |
| hf_api=hf_api, | |
| repo_id=experiment_id, | |
| hospital_a=hospital_a, | |
| hospital_b=hospital_b, | |
| )) | |
| url = f"https://hf.space/owkin/trainer-{experiment_id}" | |
| return ( | |
| gr.Button.update(interactive=False), | |
| gr.Markdown.update( | |
| visible=True, | |
| value=f"Your experiment is available at [hf.space/owkin/trainer-{experiment_id}]({url})! - If the image does not build in under a minute, please refresh and try again" | |
| ) | |
| ) | |
| demo = gr.Blocks(theme=theme, css="""\ | |
| @font-face { | |
| font-family: "Didact Gothic"; | |
| src: url('https://huggingface.co/datasets/NimaBoscarino/assets/resolve/main/substra/DidactGothic-Regular.ttf') format('truetype'); | |
| } | |
| @font-face { | |
| font-family: "Inter"; | |
| src: url('https://huggingface.co/datasets/NimaBoscarino/assets/resolve/main/substra/Inter-Regular.ttf') format('truetype'); | |
| } | |
| h1 { | |
| font-family: "Didact Gothic"; | |
| font-size: 40px !important; | |
| } | |
| p { | |
| font-family: "Inter"; | |
| } | |
| .gradio-container { | |
| min-width: 100% !important; | |
| } | |
| .margin-top { | |
| margin-top: 20px; | |
| } | |
| .white { | |
| background-color: white; | |
| } | |
| .column { | |
| border-radius: 20px; | |
| padding: 30px; | |
| } | |
| .blue { | |
| background-image: url("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/substra-banner.png"); | |
| background-size: cover; | |
| } | |
| .blue p { | |
| color: white !important; | |
| } | |
| .blue strong { | |
| color: white !important; | |
| } | |
| .info-box { | |
| background: transparent !important; | |
| border-radius: 20px !important; | |
| border-color: white !important; | |
| border-width: 4px !important; | |
| padding: 20px !important; | |
| } | |
| """) | |
| with demo: | |
| gr.HTML(""" | |
| <img src="https://raw.githubusercontent.com/substra/substra/main/Substra-logo-colour.svg" style="height: 2em;" /> | |
| """) | |
| gr.Markdown("# Federated Learning with Substra") | |
| with gr.Row(): | |
| with gr.Column(scale=1, elem_classes=["blue", "column"]): | |
| gr.Markdown("Here you can run a **quick simulation of Federated Learning**.") | |
| gr.Markdown("Check out the accompanying [blog post](https://huggingface.co/blog/owkin-substra/) to learn more.") | |
| with gr.Box(elem_classes=["info-box"]): | |
| gr.Markdown("""\ | |
| This space is an introduction to federated learning. \ | |
| We will create new spaces soon where you will be able to control the models, datasets and \ | |
| federation strategies.\ | |
| """) | |
| with gr.Column(scale=3, elem_classes=["white", "column"]): | |
| gr.Markdown("""\ | |
| Data scientists doing medical research often face a shortage of high quality and diverse data to \ | |
| effectively train models. This challenge can be overcome by securely allowing training on protected \ | |
| data through Federated Learning. [Substra](https://docs.substra.org/) is a Python based Federated \ | |
| Learning software that enables researchers to easily train ML models on remote data regardless of the \ | |
| ML library they are using or the data type they are working with. | |
| """) | |
| gr.Markdown("### Here we show an example of image data located in **two different hospitals**.") | |
| gr.Markdown("""\ | |
| By playing with the distribution of data in the two simulated hospitals, you'll be able to compare how \ | |
| the federated models compare with models trained on single datasets. The data used is from the \ | |
| Camelyon17 dataset, a commonly used benchmark in the medical world that comes from \ | |
| [this challenge](https://camelyon17.grand-challenge.org/). The sample below shows normal cells on the \ | |
| left compared with cancer cells on the right. | |
| """) | |
| gr.HTML(""" | |
| <img | |
| src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/substra-tumor.png" | |
| style="height: 300px; margin: auto;" | |
| /> | |
| """) | |
| gr.Markdown("""\ | |
| A problem often faced by researchers is that datasets lack the necessary amount of positive samples \ | |
| (samples containing cancer tissues) that are needed to reliably classify cancer. In this interface you \ | |
| can use the slider to control the percentage of negative and positive samples in each hospital. \ | |
| Setting this slider to minimum will mean there are 0 positive samples, whereas 50 would mean that \ | |
| half the dataset contains slides with positive tumor samples.\ | |
| """) | |
| with gr.Row(elem_classes=["margin-top"]): | |
| hospital_a_slider = gr.Slider( | |
| label="Percentage of positive samples in Hospital A", | |
| value=80, | |
| ) | |
| hospital_b_slider = gr.Slider( | |
| label="Percentage of positive samples in Hospital B", | |
| value=20, | |
| ) | |
| launch_experiment_button = gr.Button(value="Launch Experiment 🚀") | |
| visit_experiment_text = gr.Markdown(visible=False) | |
| launch_experiment_button.click( | |
| fn=launch_experiment, | |
| inputs=[hospital_a_slider, hospital_b_slider], | |
| outputs=[launch_experiment_button, visit_experiment_text] | |
| ) | |
| demo.launch() | |