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99e39f3
1
Parent(s):
6b1339b
Update app.py
Browse files
app.py
CHANGED
@@ -135,6 +135,9 @@ rl_envs = [
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}
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]
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def get_metadata(model_id):
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try:
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readme_path = hf_hub_download(model_id, filename="README.md")
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@@ -335,7 +338,7 @@ with block:
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grpath = gr.Variable(path_)
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with gr.Row():
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print("PATH USED", path_)
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gr_dataframe = gr.components.Dataframe(value=get_data(rl_env["rl_env"],
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with gr.Row():
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#gr_search_dataframe = gr.components.Dataframe(headers=["Ranking π", "User π€", "Model id π€", "Results", "Mean Reward", "Std Reward"], datatype=["number", "markdown", "markdown", "number", "number", "number"], visible=False)
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@@ -344,7 +347,7 @@ with block:
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with gr.Row():
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search_btn.click(fn=filter_data, inputs=[env, grpath, user_id], outputs=gr_dataframe, api_name="filter_data")
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reset_btn.click(fn=get_data, inputs=[env, grpath], outputs=gr_dataframe, api_name="get_data")
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-
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block.load(
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download_leaderboard_dataset,
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inputs=[],
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@@ -352,10 +355,13 @@ with block:
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grpath
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],
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)
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block.launch()
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scheduler = BackgroundScheduler()
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# Refresh every hour
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scheduler.add_job(func=run_update_dataset, trigger="interval", seconds=3600)
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#scheduler.add_job(download_leaderboard_dataset, 'interval', seconds=3600)
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scheduler.start()
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}
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]
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+
def restart():
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api.restart_space(repo_id="huggingface-projects/Deep-Reinforcement-Learning-Leaderboard")
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def get_metadata(model_id):
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try:
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readme_path = hf_hub_download(model_id, filename="README.md")
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grpath = gr.Variable(path_)
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with gr.Row():
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print("PATH USED", path_)
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gr_dataframe = gr.components.Dataframe(value=get_data(rl_env["rl_env"], path_), headers=["Ranking π", "User π€", "Model id π€", "Results", "Mean Reward", "Std Reward"], datatype=["number", "markdown", "markdown", "number", "number", "number"], row_count=(100, 'fixed'))
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with gr.Row():
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#gr_search_dataframe = gr.components.Dataframe(headers=["Ranking π", "User π€", "Model id π€", "Results", "Mean Reward", "Std Reward"], datatype=["number", "markdown", "markdown", "number", "number", "number"], visible=False)
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with gr.Row():
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search_btn.click(fn=filter_data, inputs=[env, grpath, user_id], outputs=gr_dataframe, api_name="filter_data")
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reset_btn.click(fn=get_data, inputs=[env, grpath], outputs=gr_dataframe, api_name="get_data")
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"""
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block.load(
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download_leaderboard_dataset,
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inputs=[],
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grpath
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],
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)
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"""
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block.launch()
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scheduler = BackgroundScheduler()
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# Refresh every hour
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#scheduler.add_job(func=run_update_dataset, trigger="interval", seconds=3600)
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#scheduler.add_job(download_leaderboard_dataset, 'interval', seconds=3600)
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scheduler.add_job(restart_space, 'interval', seconds=3600)
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scheduler.start()
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