stripping it down to the booones

app.py

@@ -1,26 +1,8 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
-
-# %% auto 0
-__all__ = ['block', 'make_clickable_model', 'make_clickable_user', 'get_submissions']
-
-# %% app.ipynb 0
 import gradio as gr
 import pandas as pd
 from huggingface_hub import list_models
 
-# %% app.ipynb 1
-def make_clickable_model(model_name, link=None):
-    if link is None:
-        link = "https://huggingface.co/" + model_name
-    # Remove user from model name
-    return f'<a target="_blank" href="{link}">{model_name.split("/")[-1]}</a>'
-
-
-def make_clickable_user(user_id):
-    link = "https://huggingface.co/" + user_id
-    return f'<a  target="_blank" href="{link}">{user_id}</a>'
 
-# %% app.ipynb 2
 def get_submissions(category):
     submissions = list_models(filter=["dreambooth-hackathon", category], full=True)
     leaderboard_models = []
@@ -42,80 +24,40 @@ def get_submissions(category):
     return df
 
 # %% app.ipynb 3
-block = gr.Blocks()
+demo = gr.Blocks()
 
-with block:
+with demo:
     gr.Markdown(
-        """# The DreamBooth Hackathon Leaderboard
-    
-    Welcome to the leaderboard for the DreamBooth Hackathon! This is a community event where particpants **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 (e.g. your pet or favourite dish) 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 5 _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!
-    
-    For details on how to participate, check out the hackathon's guide [here](https://github.com/huggingface/diffusion-models-class/blob/main/hackathon/README.md).
-    """
+        """# Energy Star Leaderboard
+
+    TODO """
     )
     with gr.Tabs():
-        with gr.TabItem("Animal 🐨"):
+        with gr.TabItem("Text Generation 💬"):
             with gr.Row():
                 animal_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("animal"), outputs=animal_data
-                )
-        with gr.TabItem("Science 🔬"):
+        with gr.TabItem("Image Generation 📷"):
             with gr.Row():
                 science_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("science"), outputs=science_data
-                )
-        with gr.TabItem("Food 🍔"):
+        with gr.TabItem("Text Classification 🎭"):
             with gr.Row():
                 food_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("food"), outputs=food_data
-                )
-        with gr.TabItem("Landscape 🏔"):
+        with gr.TabItem("Image Classification 🖼️"):
             with gr.Row():
                 landscape_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("landscape"),
-                    outputs=landscape_data,
-                )
-        with gr.TabItem("Wilcard 🔥"):
+        with gr.TabItem("Extractive QA ❔"):
             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("animal"), outputs=animal_data)
-    block.load(get_submissions, inputs=gr.Variable("science"), outputs=science_data)
-    block.load(get_submissions, inputs=gr.Variable("food"), outputs=food_data)
-    block.load(get_submissions, inputs=gr.Variable("landscape"), outputs=landscape_data)
-    block.load(get_submissions, inputs=gr.Variable("wildcard"), outputs=wildcard_data)
 
 
-block.launch()
+demo.launch()