Create app.py

app.py

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+import gradio as gr
+import os
+
+hf_writer = gr.HuggingFaceDatasetSaver(
+    os.getenv('HF_API_TOKEN'),
+    organization="society-ethics",
+    dataset_name="featured-spaces-submissions",
+    private=True
+)
+
+principles = [
+    {
+        "title": "Consentful",
+        "content": """
+        Consentful technology is built with [consent at its core](https://www.consentfultech.io).
+        
+        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", 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. What does that mean? There are a lot of ways to define this, so we've suggested a couple categories to get started 🤗
+    """)
+
+    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",
+                    "Critical",
+                ]
+            )
+            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()