Duplicate from keras-dreambooth/leaderboard

Co-authored-by: Merve Noyan <merve@users.noreply.huggingface.co>

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README.md

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+---
+title: Leaderboard
+emoji: 🌌
+colorFrom: yellow
+colorTo: red
+sdk: gradio
+sdk_version: 3.18.0
+app_file: app.py
+pinned: true
+license: apache-2.0
+duplicated_from: keras-dreambooth/leaderboard
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+
+__all__ = ['block', 'make_clickable_model', 'make_clickable_user', 'get_submissions']
+
+import gradio as gr
+import pandas as pd
+from huggingface_hub import HfApi, repocard
+
+def is_duplicated(space_id:str)->None:
+    card = repocard.RepoCard.load(space_id, repo_type="space")
+    return getattr(card.data, "duplicated_from", None) is not None
+
+
+
+def make_clickable_model(model_name, link=None):
+    if link is None:
+        link = "https://huggingface.co/" + "spaces/" + model_name
+    return f'<a target="_blank" href="{link}">{model_name.split("/")[-1]}</a>'
+
+def get_space_ids(category):
+    api = HfApi()
+    spaces = api.list_spaces(filter=["keras-dreambooth", category])
+    print(spaces)
+    space_ids = [x for x in spaces]
+    return space_ids
+
+
+def make_clickable_user(user_id):
+    link = "https://huggingface.co/" + user_id
+    return f'<a  target="_blank" href="{link}">{user_id}</a>'
+
+def get_submissions(category):
+    submissions = get_space_ids(category)
+    leaderboard_models = []
+
+    for submission in submissions:
+        # user, model, likes
+        if not is_duplicated(submission.id):
+            user_id = submission.id.split("/")[0]
+            leaderboard_models.append(
+                (
+                    make_clickable_user(user_id),
+                    make_clickable_model(submission.id),
+                    submission.likes,
+                )
+            )
+
+    df = pd.DataFrame(data=leaderboard_models, columns=["User", "Space", "Likes"])
+    df.sort_values(by=["Likes"], ascending=False, inplace=True)
+    df.insert(0, "Rank", list(range(1, len(df) + 1)))
+    return df
+
+block = gr.Blocks()
+
+with block:
+    gr.Markdown(
+        """# Keras DreamBooth Leaderboard
+    
+    Welcome to the leaderboard for the Keras DreamBooth Event! This is a community event where participants **personalise a Stable Diffusion model** by fine-tuning it with a powerful technique called [_DreamBooth_](https://arxiv.org/abs/2208.12242). This technique allows one to implant a subject into the output domain of the model such that it can be synthesized with a _unique identifier_ in the prompt. 
+    
+    This competition is composed of 4 _themes_, where each theme will collect models belong to one of the categories shown in the tabs below. We'll be **giving out prizes to the top 3 most liked models per theme**, and you're encouraged to submit as many models as you want!
+    
+    """
+    )
+    with gr.Tabs():
+        with gr.TabItem("Nature 🐨 🌳 "):
+            with gr.Row():
+                nature_data = gr.components.Dataframe(
+                    type="pandas", datatype=["number", "markdown", "markdown", "number"]
+                )
+            with gr.Row():
+                data_run = gr.Button("Refresh")
+                data_run.click(
+                    get_submissions, inputs=gr.Variable("nature"), outputs=nature_data
+                )
+        with gr.TabItem("Science Fiction & Fantasy 🧙‍♀️ 🧛‍♀️ 🤖  "):
+            with gr.Row():
+                scifi_data = gr.components.Dataframe(
+                    type="pandas", datatype=["number", "markdown", "markdown", "number"]
+                )
+            with gr.Row():
+                data_run = gr.Button("Refresh")
+                data_run.click(
+                    get_submissions, inputs=gr.Variable("scifi"), outputs=scifi_data
+                )
+        with gr.TabItem("Consentful 🖼️ 🎨 "):
+            with gr.Row():
+                consentful_data = gr.components.Dataframe(
+                    type="pandas", datatype=["number", "markdown", "markdown", "number"]
+                )
+            with gr.Row():
+                data_run = gr.Button("Refresh")
+                data_run.click(
+                    get_submissions, inputs=gr.Variable("consentful"), outputs=consentful_data
+                )
+        with gr.TabItem("Wild Card 🃏"):
+            with gr.Row():
+                wildcard_data = gr.components.Dataframe(
+                    type="pandas", datatype=["number", "markdown", "markdown", "number"]
+                )
+            with gr.Row():
+                data_run = gr.Button("Refresh")
+                data_run.click(
+                    get_submissions,
+                    inputs=gr.Variable("wildcard"),
+                    outputs=wildcard_data,
+                )
+
+    block.load(get_submissions, inputs=gr.Variable("nature"), outputs=nature_data)
+    block.load(get_submissions, inputs=gr.Variable("scifi"), outputs=scifi_data)
+    block.load(get_submissions, inputs=gr.Variable("consentful"), outputs=consentful_data)
+    block.load(get_submissions, inputs=gr.Variable("wildcard"), outputs=wildcard_data)
+
+
+block.launch()