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Update app.py
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app.py
CHANGED
@@ -569,10 +569,10 @@ with gr.Blocks(css="""
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This formula rewards models with higher win rates and more battles. As the number of battles increases, the score approaches the win rate.
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""")
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leaderboard = gr.Dataframe(
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headers=["Model", "Score", "Wins", "Losses", "Total Battles", "Win Rate"],
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row_count=10,
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col_count=
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interactive=
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label="Leaderboard"
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)
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@@ -630,10 +630,10 @@ with gr.Blocks(css="""
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The ELO rating is calculated based on wins and losses, with adjustments made based on the relative strengths of opponents.
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""")
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elo_leaderboard = gr.Dataframe(
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headers=["Model", "ELO Rating", "Wins", "Losses", "Total Battles", "Win Rate"],
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row_count=10,
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col_count=
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interactive=
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label="ELO Leaderboard"
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)
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@@ -667,7 +667,7 @@ with gr.Blocks(css="""
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value=table_data,
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row_count=len(table_data),
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col_count=len(headers),
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interactive=
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label="Model Performance Statistics"
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)
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else:
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This formula rewards models with higher win rates and more battles. As the number of battles increases, the score approaches the win rate.
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""")
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leaderboard = gr.Dataframe(
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headers=["#", "Model", "Score", "Wins", "Losses", "Total Battles", "Win Rate"],
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row_count=10,
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col_count=7,
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interactive=True,
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label="Leaderboard"
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)
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The ELO rating is calculated based on wins and losses, with adjustments made based on the relative strengths of opponents.
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""")
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elo_leaderboard = gr.Dataframe(
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headers=["#", "Model", "ELO Rating", "Wins", "Losses", "Total Battles", "Win Rate"],
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row_count=10,
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+
col_count=7,
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interactive=True,
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label="ELO Leaderboard"
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)
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value=table_data,
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row_count=len(table_data),
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col_count=len(headers),
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interactive=True,
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label="Model Performance Statistics"
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)
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else:
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