Update app.py
Browse files
app.py
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import gradio as gr
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import pandas as pd
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import numpy as np
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import json
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from functools import partial
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custom_css = """
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@@ -36,72 +35,8 @@ custom_css = """
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.default-underline {
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text-decoration: underline !important;
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}
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.customrangeslider {
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display: flex;
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flex-direction: column;
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width: 100%;
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}
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.customrangeslider input[type="range"] {
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-webkit-appearance: none;
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width: 100%;
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height: 10px;
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border-radius: 5px;
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background: #d3d3d3;
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outline: none;
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opacity: 0.7;
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transition: opacity .2s;
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}
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.customrangeslider input[type="range"]::-webkit-slider-thumb {
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-webkit-appearance: none;
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appearance: none;
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width: 20px;
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height: 20px;
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border-radius: 50%;
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background: #4CAF50;
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cursor: pointer;
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}
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.customrangeslider input[type="range"]::-moz-range-thumb {
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width: 20px;
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height: 20px;
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border-radius: 50%;
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background: #4CAF50;
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cursor: pointer;
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}
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"""
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class CustomRangeSlider(gr.components.Component):
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def __init__(self, minimum, maximum, value, step, label):
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super().__init__(self)
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self.minimum = minimum
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self.maximum = maximum
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self.value = value
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self.step = step
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self.label = label
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def get_template_context(self):
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return {
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"min": self.minimum,
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"max": self.maximum,
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"value": self.value,
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"step": self.step,
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"label": self.label,
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}
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@classmethod
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def get_component_instance(cls, props):
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return cls(**props)
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def preprocess(self, x):
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return json.loads(x)
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def postprocess(self, y):
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return json.dumps(y)
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def get_block_name(self):
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return "customrangeslider"
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gr.components.Component.register_component("customrangeslider")(CustomRangeSlider)
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# Define the columns for the different leaderboards
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UGI_COLS = ['#P', 'Model', 'UGI ๐', 'W/10 ๐', 'Unruly', 'Internet', 'Stats', 'Writing', 'PolContro']
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WRITING_STYLE_COLS = ['#P', 'Model', 'Reg+MyScore ๐', 'Reg+Int ๐', 'MyScore ๐', 'ASSSโฌ๏ธ', 'SMOGโฌ๏ธ', 'Yuleโฌ๏ธ']
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@@ -128,7 +63,7 @@ def load_leaderboard_data(csv_file_path):
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return pd.DataFrame(columns=UGI_COLS + WRITING_STYLE_COLS + ANIME_RATING_COLS)
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# Update the leaderboard table based on the search query and parameter range filters
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def update_table(df: pd.DataFrame, query: str, param_ranges: list, columns: list,
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filtered_df = df.copy()
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if param_ranges:
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param_mask = pd.Series(False, index=filtered_df.index)
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# Apply W/10 filtering
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if 'W/10 ๐' in filtered_df.columns:
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filtered_df = filtered_df[(filtered_df['W/10 ๐'] >=
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return filtered_df[columns]
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elem_id="filter-columns-size",
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)
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with gr.Row():
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# Load the initial leaderboard data
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leaderboard_df = load_leaderboard_data("ugi-leaderboard-data.csv")
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**NA:** When models either reply with one number for every anime, give ratings not between 1 and 10, or don't give every anime in the list a rating.
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""")
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def update_all_tables(query, param_ranges,
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ugi_table = update_table(leaderboard_df, query, param_ranges, UGI_COLS,
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ws_df = leaderboard_df.sort_values(by='Reg+MyScore ๐', ascending=False)
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ws_table = update_table(ws_df, query, param_ranges, WRITING_STYLE_COLS,
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arp_df = leaderboard_df.sort_values(by='Score ๐', ascending=False)
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arp_df_na = arp_df[arp_df[['Dif', 'Cor']].isna().any(axis=1)]
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arp_df = arp_df[~arp_df[['Dif', 'Cor']].isna().any(axis=1)]
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arp_table = update_table(arp_df, query, param_ranges, ANIME_RATING_COLS,
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arp_na_table = update_table(arp_df_na, query, param_ranges, ANIME_RATING_COLS,
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return ugi_table, ws_table, arp_table, arp_na_table
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search_bar.change(
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fn=update_all_tables,
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inputs=[search_bar, filter_columns_size,
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outputs=[leaderboard_table_ugi, leaderboard_table_ws, leaderboard_table_arp, leaderboard_table_arp_na]
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)
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filter_columns_size.change(
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fn=update_all_tables,
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inputs=[search_bar, filter_columns_size,
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outputs=[leaderboard_table_ugi, leaderboard_table_ws, leaderboard_table_arp, leaderboard_table_arp_na]
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)
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fn=update_all_tables,
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inputs=[search_bar, filter_columns_size,
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outputs=[leaderboard_table_ugi, leaderboard_table_ws, leaderboard_table_arp, leaderboard_table_arp_na]
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)
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import gradio as gr
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import pandas as pd
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import numpy as np
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from functools import partial
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custom_css = """
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.default-underline {
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text-decoration: underline !important;
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}
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"""
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# Define the columns for the different leaderboards
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UGI_COLS = ['#P', 'Model', 'UGI ๐', 'W/10 ๐', 'Unruly', 'Internet', 'Stats', 'Writing', 'PolContro']
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WRITING_STYLE_COLS = ['#P', 'Model', 'Reg+MyScore ๐', 'Reg+Int ๐', 'MyScore ๐', 'ASSSโฌ๏ธ', 'SMOGโฌ๏ธ', 'Yuleโฌ๏ธ']
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return pd.DataFrame(columns=UGI_COLS + WRITING_STYLE_COLS + ANIME_RATING_COLS)
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# Update the leaderboard table based on the search query and parameter range filters
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def update_table(df: pd.DataFrame, query: str, param_ranges: list, columns: list, w10_min: float, w10_max: float) -> pd.DataFrame:
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filtered_df = df.copy()
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if param_ranges:
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param_mask = pd.Series(False, index=filtered_df.index)
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# Apply W/10 filtering
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if 'W/10 ๐' in filtered_df.columns:
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filtered_df = filtered_df[(filtered_df['W/10 ๐'] >= w10_min) & (filtered_df['W/10 ๐'] <= w10_max)]
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return filtered_df[columns]
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elem_id="filter-columns-size",
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)
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with gr.Row():
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w10_min = gr.Slider(minimum=0, maximum=10, value=0, step=0.1, label="Min W/10")
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w10_max = gr.Slider(minimum=0, maximum=10, value=10, step=0.1, label="Max W/10")
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# Load the initial leaderboard data
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leaderboard_df = load_leaderboard_data("ugi-leaderboard-data.csv")
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**NA:** When models either reply with one number for every anime, give ratings not between 1 and 10, or don't give every anime in the list a rating.
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""")
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def update_all_tables(query, param_ranges, w10_min, w10_max):
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ugi_table = update_table(leaderboard_df, query, param_ranges, UGI_COLS, w10_min, w10_max)
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ws_df = leaderboard_df.sort_values(by='Reg+MyScore ๐', ascending=False)
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ws_table = update_table(ws_df, query, param_ranges, WRITING_STYLE_COLS, w10_min, w10_max)
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arp_df = leaderboard_df.sort_values(by='Score ๐', ascending=False)
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arp_df_na = arp_df[arp_df[['Dif', 'Cor']].isna().any(axis=1)]
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arp_df = arp_df[~arp_df[['Dif', 'Cor']].isna().any(axis=1)]
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arp_table = update_table(arp_df, query, param_ranges, ANIME_RATING_COLS, w10_min, w10_max)
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arp_na_table = update_table(arp_df_na, query, param_ranges, ANIME_RATING_COLS, w10_min, w10_max).fillna('NA')
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return ugi_table, ws_table, arp_table, arp_na_table
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search_bar.change(
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fn=update_all_tables,
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inputs=[search_bar, filter_columns_size, w10_min, w10_max],
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outputs=[leaderboard_table_ugi, leaderboard_table_ws, leaderboard_table_arp, leaderboard_table_arp_na]
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)
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filter_columns_size.change(
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fn=update_all_tables,
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inputs=[search_bar, filter_columns_size, w10_min, w10_max],
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outputs=[leaderboard_table_ugi, leaderboard_table_ws, leaderboard_table_arp, leaderboard_table_arp_na]
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)
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w10_min.change(
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fn=update_all_tables,
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inputs=[search_bar, filter_columns_size, w10_min, w10_max],
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outputs=[leaderboard_table_ugi, leaderboard_table_ws, leaderboard_table_arp, leaderboard_table_arp_na]
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)
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w10_max.change(
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fn=update_all_tables,
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inputs=[search_bar, filter_columns_size, w10_min, w10_max],
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outputs=[leaderboard_table_ugi, leaderboard_table_ws, leaderboard_table_arp, leaderboard_table_arp_na]
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)
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