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import gradio as gr |
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import requests |
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import pandas as pd |
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from huggingface_hub.hf_api import SpaceInfo |
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from huggingface_hub import HfApi, hf_hub_download |
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from huggingface_hub.repocard import metadata_load |
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path = f"https://huggingface.co/api/spaces" |
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def get_blocks_party_spaces(): |
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r = requests.get(path) |
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d = r.json() |
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spaces = [SpaceInfo(**x) for x in d] |
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blocks_spaces = {} |
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for i in range(0,len(spaces)): |
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if spaces[i].id.split('/')[0] == 'Gradio-Blocks' and hasattr(spaces[i], 'likes') and spaces[i].id != 'Gradio-Blocks/Leaderboard' and spaces[i].id != 'Gradio-Blocks/README': |
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blocks_spaces[spaces[i].id]=spaces[i].likes |
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df = pd.DataFrame( |
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[{"Spaces_Name": Spaces, "likes": likes} for Spaces,likes in blocks_spaces.items()]) |
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df = df.sort_values(by=['likes'],ascending=False) |
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return df |
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def make_clickable_model(model_name): |
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model_name_show = ' '.join(model_name.split('/')[1:]) |
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link = "https://huggingface.co/" + model_name |
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return f'<a target="_blank" href="{link}">{model_name_show}</a>' |
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def get_mteb_data(task="Clustering", metric="v_measure"): |
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api = HfApi() |
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models = api.list_models(filter="mteb") |
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df_list = [] |
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for model in models: |
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readme_path = hf_hub_download(model.modelId, filename="README.md") |
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meta = metadata_load(readme_path) |
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out = list( |
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map( |
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lambda x: {x["dataset"]["name"].replace("MTEB ", ""): round(list(filter(lambda x: x["type"] == metric, x["metrics"]))[0]["value"], 2)}, |
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filter(lambda x: x["task"]["type"] == task, meta["model-index"][0]["results"]) |
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) |
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) |
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out = {k: v for d in out for k, v in d.items()} |
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out["Model"] = model.modelId |
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df_list.append(out) |
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df = pd.DataFrame(df_list) |
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df = df[[df.columns[-1]] + sorted(df.columns[:-1])] |
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return df |
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block = gr.Blocks() |
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with block: |
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gr.Markdown("""Leaderboard for XX most popular Blocks Event Spaces. To learn more and join, see <a href="https://huggingface.co/Gradio-Blocks" target="_blank" style="text-decoration: underline">Blocks Party Event</a>""") |
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with gr.Tabs(): |
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with gr.TabItem("Blocks Party Leaderboard"): |
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with gr.Row(): |
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data = gr.components.Dataframe(type="pandas") |
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with gr.Row(): |
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data_run = gr.Button("Refresh") |
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data_run.click(get_blocks_party_spaces, inputs=None, outputs=data) |
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with gr.TabItem("Clustering"): |
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with gr.Row(): |
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gr.Markdown("""Leaderboard for Clustering""") |
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with gr.Row(): |
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data = gr.components.Dataframe(type="pandas") |
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with gr.Row(): |
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data_run = gr.Button("Refresh") |
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task = gr.Variable(value="Clustering") |
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metric = gr.Variable(value="v_measure") |
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data_run.click(get_mteb_data, inputs=[task, metric], outputs=data) |
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with gr.TabItem("Blocks Party Leaderboard2"): |
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with gr.Row(): |
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data = gr.components.Dataframe(type="pandas") |
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with gr.Row(): |
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data_run = gr.Button("Refresh") |
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data_run.click(get_blocks_party_spaces, inputs=None, outputs=data) |
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block.load(get_blocks_party_spaces, inputs=None, outputs=data) |
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block.launch() |
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