import gradio as gr
import os

hf_writer = gr.HuggingFaceDatasetSaver(
    os.getenv('HUGGING_FACE_HUB_TOKEN'),
    organization="society-ethics",
    dataset_name="featured-spaces-submissions",
    private=True
)

principles = [
    {
        "title": "Consentful",
        "content": """
        [What is consentful tech?](https://www.consentfultech.io)
        Consentful technology supports the self-determination of people who use and are affected by these technologies.
        
        For Spaces, some examples of this can include:
        
        - Demonstrating a commitment to acquiring data from willing, informed, and appropriately compensated sources.
        - Designing systems that respect end-user autonomy, e.g. with privacy-preserving techniques.
        - Avoiding extractive, chauvinist, ["dark"](https://www.deceptive.design), and otherwise "unethical" patterns of engagement.
        """
    },
    {
        "title": "Sustainable",
        "content": """
        These are Spaces that highlight and explore techniques for making machine learning ecologically sustainable.
        
        Examples could include:
        
        - Tracking emissions from training and running inferences on large language models.
        - Quantization and distillation methods to reduce carbon footprints without sacrificing model quality.
        """
    },
    {
        "title": "Socially Conscious",
        "content": """
        "Socially Conscious" Spaces show us how machine learning can be applied as a force for *good*!
        
        This is quite broad, but some examples could be:
        
        - Using machine learning as part of an effort to tackle climate change.
        - Building tools to assist with medical research and practice.
        - Developing models for text-to-speech, image captioning, and other tasks aimed at increasing accessibility.
        - Creating systems for the digital humanities, such as for Indigenous language revitalization.
        """
    },
    {
        "title": "Inclusive",
        "content": """
        These are projects which broaden the scope of who *builds* and *benefits* in the machine learning world.
        
        This could mean things like:
        
        - Curating diverse datasets that increase the representation of underserved groups.
        - Training language models on languages that aren't yet available on the Hugging Face Hub.
        - Creating no-code frameworks that allow non-technical folk to engage with AI.
        """
    },
    {
        "title": "Rigorous",
        "content": """
        Among the many concerns that go into creating new models is a seemingly simple question: "Does it work?"
        
        Rigorous projects pay special attention to examining failure cases, protecting privacy through security
        measures, and ensuring that potential users (technical and non-technical) are informed of the project's
        limitations.
        
        For example:
        
        - Projects built with models that are well-documented with [Model Cards](https://huggingface.co/docs/hub/model-cards).
        - Models that are evaluated against cutting-edge benchmarks, with results reported against disaggregated sets.
        - Demonstrations of models failing across ["gender, skin type, ethnicity, age or other attributes"](http://gendershades.org/overview.html).
        - Techniques for mitigating issues like over-fitting and training data memorization.
        """
    },
    {
        "title": "Inquisitive",
        "content": """
        Some projects take a radical new approach to concepts which may have become commonplace. These projects, often
        rooted in critical theory, shine a light on inequities and power structures which challenge the community to
        rethink its relationship to technology.
        
        For example:
        
        - [Reframing AI and machine learning from Indigenous perspectives](https://jods.mitpress.mit.edu/pub/lewis-arista-pechawis-kite/release/1).
        - [Highlighting LGBTQIA2S+ marginalization in AI](https://edri.org/our-work/computers-are-binary-people-are-not-how-ai-systems-undermine-lgbtq-identity/).
        - [Critiquing the harms perpetuated by AI systems](https://www.ajl.org).
        """
    },
]


def toggle_description(title, content):
    with gr.Accordion(label=title, open=False):
        gr.Markdown(content, elem_id="margin-top")


def submit_entry(URL, tags, suggestions, comments):
    hf_writer.flag(
        flag_data=[URL, tags, suggestions, comments]
    )

    return [
        gr.Markdown.update(
            visible=True,
            value="Thank you for your submission! 🤗"
        ),
        gr.Button.update(
            visible=False
        )
    ]


with gr.Blocks(css="#margin-top {margin-top: 15px}") as demo:
    gr.Markdown("## Call for submissions! 📢")
    gr.Markdown("""
    Hugging Face is collecting examples of Spaces that are ethically mindful to highlight and encourage these kinds of projects – and we would love your input!
    
    If you have built a Space that you think should be featured, or if you would like to nominate someone else's, paste the URL in the form below  🤗
    
    Want to learn more? Join us over at **#ethics-and-society** on the [Hugging Face Discord](https://hf.co/join/discord)!
    """)
    with gr.Row():
        with gr.Column():
            gr.Markdown("💡 Click on the terms below to view their description and some examples.")
            with gr.Column():
                [toggle_description(x["title"], x["content"]) for x in principles]

        with gr.Column():
            URL = gr.Text(label="URL")
            tags = gr.Checkboxgroup(
                label="Tags - Pick as many as you like!",
                choices=[
                    "Consentful",
                    "Sustainable",
                    "Socially Conscious",
                    "Inclusive",
                    "Rigorous",
                    "Inquisitive",
                ]
            )
            suggestions = gr.Text(label="[Optional] Do you have suggestions for other tags?")
            comments = gr.TextArea(label="[Optional] Any extra comments?")
            submit = gr.Button(value="Submit")
            thank_you = gr.Markdown(visible=False)

    submit.click(
        fn=submit_entry,
        inputs=[URL, tags, suggestions, comments],
        outputs=[thank_you, submit]
    )

hf_writer.setup(
    components=[URL, tags, suggestions, comments],
    flagging_dir="flagged"
)

demo.launch()