Spaces:
Running
Running
| import gradio as gr | |
| import requests | |
| import pandas as pd | |
| from huggingface_hub.hf_api import SpaceInfo | |
| from huggingface_hub import HfApi, hf_hub_download | |
| from huggingface_hub.repocard import metadata_load | |
| path = f"https://huggingface.co/api/spaces" | |
| #api = HfApi() | |
| #models = api.list_models(filter="mteb") | |
| #readme_path = hf_hub_download(models[0].modelId, filename="README.md") | |
| #meta = metadata_load(readme_path) | |
| #list(filter(lambda x: x["task"]["type"] == "Retrieval", meta["model-index"][0]["results"])) | |
| def get_blocks_party_spaces(): | |
| r = requests.get(path) | |
| d = r.json() | |
| spaces = [SpaceInfo(**x) for x in d] | |
| blocks_spaces = {} | |
| for i in range(0,len(spaces)): | |
| 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': | |
| blocks_spaces[spaces[i].id]=spaces[i].likes | |
| df = pd.DataFrame( | |
| [{"Spaces_Name": Spaces, "likes": likes} for Spaces,likes in blocks_spaces.items()]) | |
| df = df.sort_values(by=['likes'],ascending=False) | |
| return df | |
| def get_clustering(task="Clustering", metric="v_measure"): | |
| api = HfApi() | |
| models = api.list_models(filter="mteb") | |
| df_list = [] | |
| for model in models: | |
| readme_path = hf_hub_download(model.modelId, filename="README.md") | |
| meta = metadata_load(readme_path) | |
| out = list( | |
| map( | |
| lambda x: {x["dataset"]["name"]: list(filter(lambda x: x["type"] == metric, x["metrics"]))[0]["value"]}, | |
| filter(lambda x: x["task"]["type"] == task, meta["model-index"][0]["results"]) | |
| ) | |
| ) | |
| out = {k: v for d in out for k, v in d.items()} | |
| out["Model"] = model.modelId | |
| df_list.append(out) | |
| df = pd.DataFrame(df_list) | |
| # Put Model in the beginning & sort the others | |
| df = df[[df.columns[-1]] + sorted(df.columns[:-1])] | |
| return df | |
| block = gr.Blocks() | |
| with block: | |
| 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>""") | |
| with gr.Tabs(): | |
| with gr.TabItem("Blocks Party Leaderboard"): | |
| with gr.Row(): | |
| data = gr.components.Dataframe(type="pandas") | |
| with gr.Row(): | |
| data_run = gr.Button("Refresh") | |
| data_run.click(get_blocks_party_spaces, inputs=None, outputs=data) | |
| with gr.TabItem("Clustering"): | |
| with gr.Row(): | |
| gr.Markdown("""Leaderboard for Clustering""") | |
| with gr.Row(): | |
| data = gr.components.Dataframe(type="pandas") | |
| with gr.Row(): | |
| data_run = gr.Button("Refresh") | |
| data_run.click(get_clustering, inputs=None, outputs=data) | |
| with gr.TabItem("Blocks Party Leaderboard2"): | |
| with gr.Row(): | |
| data = gr.components.Dataframe(type="pandas") | |
| with gr.Row(): | |
| data_run = gr.Button("Refresh") | |
| data_run.click(get_blocks_party_spaces, inputs=None, outputs=data) | |
| # running the function on page load in addition to when the button is clicked | |
| block.load(get_blocks_party_spaces, inputs=None, outputs=data) | |
| block.launch() | |