Spaces:
Running
Running
Yuxuan-Zhang-Dexter
commited on
Commit
·
342518d
1
Parent(s):
1263816
update leaderboard
Browse files- app.py +36 -13
- data_visualization.py +75 -23
- gallery_tab.py +753 -231
- requirements.txt +2 -1
app.py
CHANGED
@@ -33,7 +33,6 @@ from gallery_tab import create_video_gallery
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# Try to import enhanced leaderboard, use standard DataFrame if not available
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from gradio_leaderboard import Leaderboard, SelectColumns, ColumnFilter
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-
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HAS_ENHANCED_LEADERBOARD = True
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@@ -86,6 +85,10 @@ def load_rank_data(time_point):
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return None
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return None
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# Function to prepare DataFrame for display
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def prepare_dataframe_for_display(df, for_game=None):
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"""Format DataFrame for better display in the UI"""
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@@ -623,11 +626,14 @@ def build_app():
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# Add leaderboard search box in its own row
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with gr.Row():
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-
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-
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# Get initial leaderboard dataframe
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initial_df = get_combined_leaderboard(rank_data, {
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@@ -654,16 +660,23 @@ def build_app():
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height=800
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)
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# Add search functionality
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def filter_table(search_term, current_df):
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# Filter the DataFrame by Player or Organization
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filtered_df = current_df[
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current_df["Player"].str.contains(search_term, case=False) |
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current_df["Organization"].str.contains(search_term, case=False)
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-
]
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return filtered_df
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# Connect search box to the table
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@@ -673,6 +686,16 @@ def build_app():
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outputs=[leaderboard_df]
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)
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# List of all checkboxes
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checkbox_list = [
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mario_overall, mario_details,
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# Try to import enhanced leaderboard, use standard DataFrame if not available
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from gradio_leaderboard import Leaderboard, SelectColumns, ColumnFilter
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HAS_ENHANCED_LEADERBOARD = True
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return None
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return None
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+
# Add a note about score values
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+
def add_score_note():
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return gr.Markdown("*Note: '-1' in the table indicates no data point for that model.*", elem_classes="score-note")
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+
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# Function to prepare DataFrame for display
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def prepare_dataframe_for_display(df, for_game=None):
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"""Format DataFrame for better display in the UI"""
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# Add leaderboard search box in its own row
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with gr.Row():
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with gr.Column(scale=8):
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search_box = gr.Textbox(
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label="🔍 Search by Player or Organization",
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placeholder="Type to filter the table...",
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show_label=True
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)
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with gr.Column(scale=1):
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search_clear_btn = gr.Button("Clear", variant="secondary")
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# Get initial leaderboard dataframe
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initial_df = get_combined_leaderboard(rank_data, {
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height=800
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)
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# Add the score note below the table
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with gr.Row():
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score_note = add_score_note()
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# Add search functionality
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def filter_table(search_term, current_df):
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# Create a fresh copy of the DataFrame to avoid modifying the original
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filtered_df = current_df.copy()
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# Only apply search if a term is provided
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if search_term:
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# Filter the DataFrame by Player or Organization
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filtered_df = filtered_df[
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filtered_df["Player"].str.contains(search_term, case=False) |
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filtered_df["Organization"].str.contains(search_term, case=False)
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]
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return filtered_df
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# Connect search box to the table
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outputs=[leaderboard_df]
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)
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# Clear search button functionality
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def clear_search():
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return "", initial_display_df
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search_clear_btn.click(
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clear_search,
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inputs=[],
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outputs=[search_box, leaderboard_df]
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)
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# List of all checkboxes
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checkbox_list = [
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mario_overall, mario_details,
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data_visualization.py
CHANGED
@@ -30,11 +30,12 @@ def get_model_prefix(name):
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return name.split('-')[0]
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-
def normalize_values(values, mean, std):
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-
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-
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def simplify_model_name(name):
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if name == "claude-3-7-sonnet-20250219(thinking)":
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@@ -174,37 +175,85 @@ def get_combined_leaderboard_with_radar(rank_data, selected_games):
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return df, create_radar_charts(df_viz)
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def create_group_bar_chart(df):
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-
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-
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-
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-
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df[f"norm_{col}"] = normalize_values(vals, mean, std)
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fig = go.Figure()
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for
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-
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-
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fig.add_trace(go.Bar(
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name=simplify_model_name(
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x=
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y=
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marker_color=
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))
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fig.update_layout(
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autosize=False,
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width=
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height=600,
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margin=dict(l=80, r=150, t=40, b=200),
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title=dict(
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text="Grouped Bar Chart of AI Models",
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pad=dict(t=10)
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),
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xaxis_title="Games",
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yaxis_title="Normalized Score",
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barmode='group',
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legend=dict(
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font=dict(size=9),
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@@ -218,8 +267,11 @@ def create_group_bar_chart(df):
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borderwidth=1
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)
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)
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return fig
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def get_combined_leaderboard_with_group_bar(rank_data, selected_games):
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df = get_combined_leaderboard(rank_data, selected_games)
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# Create a copy for visualization to avoid modifying the original
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return name.split('-')[0]
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def normalize_values(values, mean=None, std=None):
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min_val = min(values)
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max_val = max(values)
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if max_val == min_val:
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return [100 for _ in values] # or 50
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return [(v - min_val) / (max_val - min_val) * 100 for v in values]
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def simplify_model_name(name):
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if name == "claude-3-7-sonnet-20250219(thinking)":
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return df, create_radar_charts(df_viz)
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def create_group_bar_chart(df):
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game_cols = {}
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for game in GAME_ORDER:
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col = f"{game} Score"
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if col in df.columns:
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df[col] = df[col].replace("_", np.nan).astype(float)
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if df[col].notna().any():
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game_cols[game] = col
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if not game_cols:
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return go.Figure().update_layout(title="No data available")
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# Drop players with no data
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df = df.dropna(subset=game_cols.values(), how='all')
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# Normalize scores per game
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for game, col in game_cols.items():
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valid = df[col].dropna()
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norm_col = f"norm_{col}"
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if valid.empty:
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df[norm_col] = np.nan
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else:
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mean, std = valid.mean(), valid.std()
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normalized = normalize_values(valid, mean, std)
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df[norm_col] = np.nan
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df.loc[valid.index, norm_col] = normalized
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# Build consistent game order (X-axis)
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sorted_games = [game for game in GAME_ORDER if f"norm_{game} Score" in df.columns]
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# Group models by prefix, then sort alphabetically
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model_groups = {}
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for player in df["Player"].unique():
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prefix = player.split('-')[0]
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model_groups.setdefault(prefix, []).append(player)
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ordered_players = []
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for prefix in sorted(model_groups):
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ordered_players.extend(sorted(model_groups[prefix]))
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# Create one trace per player
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fig = go.Figure()
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for player in ordered_players:
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row = df[df["Player"] == player]
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if row.empty:
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continue
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row = row.iloc[0]
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y_vals = []
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has_data = False
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for game in sorted_games:
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col = f"norm_{game} Score"
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val = row.get(col, np.nan)
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if not np.isnan(val):
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has_data = True
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y_vals.append(val if not np.isnan(val) else 0)
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if not has_data:
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continue
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fig.add_trace(go.Bar(
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name=simplify_model_name(player),
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x=sorted_games,
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y=y_vals,
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marker_color=MODEL_COLORS.get(player, '#808080'),
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hovertemplate="%{x}<br>%{y:.1f}<extra></extra>"
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))
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fig.update_layout(
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autosize=False,
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width=1000,
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height=600,
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margin=dict(l=80, r=150, t=40, b=200),
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title=dict(text="Grouped Bar Chart of AI Models (Consistent Trace Grouping)", pad=dict(t=10)),
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xaxis_title="Games",
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yaxis_title="Normalized Score",
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xaxis=dict(
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categoryorder='array',
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categoryarray=sorted_games
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),
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barmode='group',
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legend=dict(
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font=dict(size=9),
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borderwidth=1
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)
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)
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+
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return fig
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+
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+
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def get_combined_leaderboard_with_group_bar(rank_data, selected_games):
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df = get_combined_leaderboard(rank_data, selected_games)
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# Create a copy for visualization to avoid modifying the original
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gallery_tab.py
CHANGED
@@ -1,6 +1,73 @@
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import gradio as gr
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import json
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# Load video links and news data
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with open('assets/game_video_link.json', 'r') as f:
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with open('assets/news.json', 'r') as f:
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NEWS_DATA = json.load(f)
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def
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"""
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for
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formatted_date = date_obj.strftime("%B %d, %Y")
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news_items.append(f'''
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<div class="news-item">
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<div class="news-date">{formatted_date}</div>
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<div class="news-content">
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<div class="news-video">
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<div class="video-wrapper">
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<iframe src="https://www.youtube.com/embed/{video_id}"></iframe>
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</div>
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</div>
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<div class="news-text">
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<a href="{item["twitter_link"]}" target="_blank" class="twitter-link">
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<span class="twitter-icon">📢</span>
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{item["twitter_text"]}
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</a>
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</div>
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</div>
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</div>
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''')
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|
55 |
<style>
|
56 |
-
.
|
57 |
-
width:
|
58 |
-
|
59 |
-
|
60 |
-
|
61 |
-
}}
|
62 |
-
.highlight-section {{
|
63 |
-
margin-bottom: 40px;
|
64 |
-
}}
|
65 |
-
.highlight-card {{
|
66 |
-
background: #ffffff;
|
67 |
-
border-radius: 10px;
|
68 |
-
box-shadow: 0 4px 20px rgba(0,0,0,0.15);
|
69 |
-
overflow: hidden;
|
70 |
-
transition: transform 0.3s;
|
71 |
-
border: 2px solid #2196F3;
|
72 |
-
}}
|
73 |
-
.highlight-card:hover {{
|
74 |
-
transform: translateY(-5px);
|
75 |
-
}}
|
76 |
-
.highlight-header {{
|
77 |
-
background: #2196F3;
|
78 |
-
color: white;
|
79 |
-
padding: 15px 20px;
|
80 |
-
font-size: 1.2em;
|
81 |
-
font-weight: bold;
|
82 |
display: flex;
|
83 |
align-items: center;
|
84 |
-
|
85 |
-
|
86 |
-
.highlight-date {{
|
87 |
-
font-size: 0.9em;
|
88 |
-
opacity: 0.9;
|
89 |
-
}}
|
90 |
-
.highlight-content {{
|
91 |
-
padding: 20px;
|
92 |
-
}}
|
93 |
-
.video-grid {{
|
94 |
-
display: grid;
|
95 |
-
grid-template-columns: repeat(2, 1fr);
|
96 |
-
gap: 20px;
|
97 |
-
margin-top: 20px;
|
98 |
-
margin-bottom: 40px;
|
99 |
-
}}
|
100 |
-
.video-card {{
|
101 |
-
background: #ffffff;
|
102 |
-
border-radius: 10px;
|
103 |
-
box-shadow: 0 2px 10px rgba(0,0,0,0.1);
|
104 |
-
overflow: hidden;
|
105 |
-
transition: transform 0.2s;
|
106 |
-
}}
|
107 |
-
.video-card:hover {{
|
108 |
-
transform: translateY(-5px);
|
109 |
-
}}
|
110 |
-
.video-wrapper {{
|
111 |
position: relative;
|
112 |
-
|
113 |
-
|
114 |
-
|
115 |
-
|
116 |
-
.video-wrapper iframe {{
|
117 |
-
position: absolute;
|
118 |
-
top: 0;
|
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-
left: 0;
|
120 |
width: 100%;
|
|
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|
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height: 100%;
|
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|
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|
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|
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|
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|
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|
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border-
|
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|
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|
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|
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|
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|
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|
149 |
-
margin-bottom: 20px;
|
150 |
-
overflow: hidden;
|
151 |
-
}}
|
152 |
-
.news-date {{
|
153 |
-
padding: 10px 20px;
|
154 |
-
background: #f8f9fa;
|
155 |
-
color: #666;
|
156 |
-
font-size: 0.9em;
|
157 |
-
border-bottom: 1px solid #eee;
|
158 |
-
}}
|
159 |
-
.news-content {{
|
160 |
-
display: flex;
|
161 |
-
padding: 20px;
|
162 |
-
align-items: center;
|
163 |
-
gap: 30px;
|
164 |
-
}}
|
165 |
-
.news-video {{
|
166 |
-
flex: 0 0 300px;
|
167 |
-
}}
|
168 |
-
.news-text {{
|
169 |
-
flex: 1;
|
170 |
-
display: flex;
|
171 |
-
align-items: center;
|
172 |
-
min-height: 169px;
|
173 |
-
}}
|
174 |
-
.twitter-link {{
|
175 |
-
color: #2c3e50;
|
176 |
-
text-decoration: none;
|
177 |
-
display: flex;
|
178 |
-
align-items: center;
|
179 |
-
gap: 15px;
|
180 |
-
font-size: 1.4em;
|
181 |
font-weight: 600;
|
182 |
-
|
183 |
-
|
184 |
-
|
185 |
-
|
186 |
-
}}
|
187 |
-
.twitter-icon {{
|
188 |
-
font-size: 1.5em;
|
189 |
-
color: #1da1f2;
|
190 |
-
}}
|
191 |
</style>
|
192 |
-
|
193 |
-
|
194 |
-
|
195 |
-
|
196 |
-
<div class="highlight-header">
|
197 |
-
<span>🌟 Latest Update</span>
|
198 |
-
<span class="highlight-date">{formatted_latest_date}</span>
|
199 |
-
</div>
|
200 |
-
<div class="highlight-content">
|
201 |
-
<div class="video-wrapper">
|
202 |
-
<iframe src="https://www.youtube.com/embed/{latest_video_id}"></iframe>
|
203 |
-
</div>
|
204 |
-
<div class="video-title">
|
205 |
-
<a href="{latest_news["twitter_link"]}" target="_blank" class="twitter-link">
|
206 |
-
<span class="twitter-icon">📢</span>
|
207 |
-
{latest_news["twitter_text"]}
|
208 |
-
</a>
|
209 |
-
</div>
|
210 |
-
</div>
|
211 |
</div>
|
212 |
</div>
|
|
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|
213 |
|
214 |
-
|
215 |
-
|
216 |
-
|
217 |
-
|
218 |
-
|
219 |
-
|
220 |
-
|
221 |
-
|
222 |
-
<div class="video-card">
|
223 |
-
<div class="video-wrapper">
|
224 |
-
<iframe src="https://www.youtube.com/embed/{sokoban_id}"></iframe>
|
225 |
-
</div>
|
226 |
-
<div class="video-title">📦 Sokoban</div>
|
227 |
-
</div>
|
228 |
-
<div class="video-card">
|
229 |
-
<div class="video-wrapper">
|
230 |
-
<iframe src="https://www.youtube.com/embed/{game_2048_id}"></iframe>
|
231 |
-
</div>
|
232 |
-
<div class="video-title">🔢 2048</div>
|
233 |
-
</div>
|
234 |
-
<div class="video-card">
|
235 |
-
<div class="video-wrapper">
|
236 |
-
<iframe src="https://www.youtube.com/embed/{candy_id}"></iframe>
|
237 |
-
</div>
|
238 |
-
<div class="video-title">🍬 Candy Crash</div>
|
239 |
-
</div>
|
240 |
-
</div>
|
241 |
|
242 |
-
|
243 |
-
|
244 |
-
|
245 |
-
|
246 |
-
|
247 |
-
|
248 |
-
|
249 |
-
|
250 |
-
|
251 |
-
|
252 |
-
|
253 |
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|
254 |
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|
255 |
-
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|
|
1 |
import gradio as gr
|
2 |
+
import os
|
3 |
+
import pandas as pd
|
4 |
import json
|
5 |
+
from PIL import Image, ImageSequence
|
6 |
+
import io
|
7 |
+
from functools import reduce
|
8 |
+
import numpy as np
|
9 |
+
from datetime import datetime, timedelta
|
10 |
+
import matplotlib.pyplot as plt
|
11 |
+
from leaderboard_utils import (
|
12 |
+
get_organization,
|
13 |
+
get_mario_leaderboard,
|
14 |
+
get_sokoban_leaderboard,
|
15 |
+
get_2048_leaderboard,
|
16 |
+
get_candy_leaderboard,
|
17 |
+
get_tetris_leaderboard,
|
18 |
+
get_tetris_planning_leaderboard,
|
19 |
+
get_combined_leaderboard,
|
20 |
+
GAME_ORDER
|
21 |
+
)
|
22 |
+
from data_visualization import (
|
23 |
+
get_combined_leaderboard_with_group_bar,
|
24 |
+
create_organization_radar_chart,
|
25 |
+
create_top_players_radar_chart,
|
26 |
+
create_player_radar_chart,
|
27 |
+
create_horizontal_bar_chart,
|
28 |
+
normalize_values,
|
29 |
+
get_combined_leaderboard_with_single_radar
|
30 |
+
)
|
31 |
+
from gallery_tab import create_video_gallery
|
32 |
+
|
33 |
+
# Try to import enhanced leaderboard, use standard DataFrame if not available
|
34 |
+
|
35 |
+
from gradio_leaderboard import Leaderboard, SelectColumns, ColumnFilter
|
36 |
+
from leaderboard_config import ON_LOAD_COLUMNS, TYPES
|
37 |
+
HAS_ENHANCED_LEADERBOARD = True
|
38 |
+
|
39 |
+
|
40 |
+
# Define time points and their corresponding data files
|
41 |
+
TIME_POINTS = {
|
42 |
+
"03/25/2025": "rank_data_03_25_2025.json",
|
43 |
+
# Add more time points here as they become available
|
44 |
+
}
|
45 |
+
|
46 |
+
# Load the initial JSON file with rank data
|
47 |
+
with open(TIME_POINTS["03/25/2025"], "r") as f:
|
48 |
+
rank_data = json.load(f)
|
49 |
+
|
50 |
+
# Add leaderboard state at the top level
|
51 |
+
leaderboard_state = {
|
52 |
+
"current_game": None,
|
53 |
+
"previous_overall": {
|
54 |
+
"Super Mario Bros": True,
|
55 |
+
"Sokoban": True,
|
56 |
+
"2048": True,
|
57 |
+
"Candy Crash": True,
|
58 |
+
"Tetris (complete)": True,
|
59 |
+
"Tetris (planning only)": True
|
60 |
+
},
|
61 |
+
"previous_details": {
|
62 |
+
"Super Mario Bros": False,
|
63 |
+
"Sokoban": False,
|
64 |
+
"2048": False,
|
65 |
+
"Candy Crash": False,
|
66 |
+
"Tetris (complete)": False,
|
67 |
+
"Tetris (planning only)": False
|
68 |
+
}
|
69 |
+
}
|
70 |
+
|
71 |
|
72 |
# Load video links and news data
|
73 |
with open('assets/game_video_link.json', 'r') as f:
|
|
|
76 |
with open('assets/news.json', 'r') as f:
|
77 |
NEWS_DATA = json.load(f)
|
78 |
|
79 |
+
def load_rank_data(time_point):
|
80 |
+
"""Load rank data for a specific time point"""
|
81 |
+
if time_point in TIME_POINTS:
|
82 |
+
try:
|
83 |
+
with open(TIME_POINTS[time_point], "r") as f:
|
84 |
+
return json.load(f)
|
85 |
+
except FileNotFoundError:
|
86 |
+
return None
|
87 |
+
return None
|
88 |
+
|
89 |
+
# Add a note about score values
|
90 |
+
def add_score_note():
|
91 |
+
return gr.Markdown("*Note: '-1' in the table indicates no data point for that model.*", elem_classes="score-note")
|
92 |
+
|
93 |
+
# Function to prepare DataFrame for display
|
94 |
+
def prepare_dataframe_for_display(df, for_game=None):
|
95 |
+
"""Format DataFrame for better display in the UI"""
|
96 |
+
# Clone the DataFrame to avoid modifying the original
|
97 |
+
display_df = df.copy()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
98 |
|
99 |
+
# Filter out normalized score columns
|
100 |
+
norm_columns = [col for col in display_df.columns if col.startswith('norm_')]
|
101 |
+
if norm_columns:
|
102 |
+
display_df = display_df.drop(columns=norm_columns)
|
103 |
|
104 |
+
# Replace '_' with '-' for better display
|
105 |
+
for col in display_df.columns:
|
106 |
+
if col.endswith(' Score'):
|
107 |
+
display_df[col] = display_df[col].apply(lambda x: '-' if x == '_' else x)
|
108 |
+
|
109 |
+
# If we're in detailed view, add a formatted rank column
|
110 |
+
if for_game:
|
111 |
+
# Sort by relevant score column
|
112 |
+
score_col = f"{for_game} Score"
|
113 |
+
if score_col in display_df.columns:
|
114 |
+
# Convert to numeric for sorting, treating '-' as NaN
|
115 |
+
display_df[score_col] = pd.to_numeric(display_df[score_col], errors='coerce')
|
116 |
+
# Sort by score in descending order
|
117 |
+
display_df = display_df.sort_values(by=score_col, ascending=False)
|
118 |
+
# Add rank column based on the sort
|
119 |
+
display_df.insert(0, 'Rank', range(1, len(display_df) + 1))
|
120 |
+
# Filter out models that didn't participate
|
121 |
+
display_df = display_df[~display_df[score_col].isna()]
|
122 |
+
|
123 |
+
return display_df
|
124 |
+
|
125 |
+
# Helper function to ensure leaderboard updates maintain consistent height
|
126 |
+
def update_df_with_height(df):
|
127 |
+
"""Update DataFrame with consistent height parameter."""
|
128 |
+
return gr.update(value=df, height=800)
|
129 |
+
|
130 |
+
def update_leaderboard(mario_overall, mario_details,
|
131 |
+
sokoban_overall, sokoban_details,
|
132 |
+
_2048_overall, _2048_details,
|
133 |
+
candy_overall, candy_details,
|
134 |
+
tetris_overall, tetris_details,
|
135 |
+
tetris_plan_overall, tetris_plan_details):
|
136 |
+
global leaderboard_state
|
137 |
+
|
138 |
+
# Convert current checkbox states to dictionary for easier comparison
|
139 |
+
current_overall = {
|
140 |
+
"Super Mario Bros": mario_overall,
|
141 |
+
"Sokoban": sokoban_overall,
|
142 |
+
"2048": _2048_overall,
|
143 |
+
"Candy Crash": candy_overall,
|
144 |
+
"Tetris (complete)": tetris_overall,
|
145 |
+
"Tetris (planning only)": tetris_plan_overall
|
146 |
+
}
|
147 |
+
|
148 |
+
current_details = {
|
149 |
+
"Super Mario Bros": mario_details,
|
150 |
+
"Sokoban": sokoban_details,
|
151 |
+
"2048": _2048_details,
|
152 |
+
"Candy Crash": candy_details,
|
153 |
+
"Tetris (complete)": tetris_details,
|
154 |
+
"Tetris (planning only)": tetris_plan_details
|
155 |
+
}
|
156 |
+
|
157 |
+
# Find which game's state changed
|
158 |
+
changed_game = None
|
159 |
+
for game in current_overall.keys():
|
160 |
+
if (current_overall[game] != leaderboard_state["previous_overall"][game] or
|
161 |
+
current_details[game] != leaderboard_state["previous_details"][game]):
|
162 |
+
changed_game = game
|
163 |
+
break
|
164 |
+
|
165 |
+
if changed_game:
|
166 |
+
# If a game's details checkbox was checked
|
167 |
+
if current_details[changed_game] and not leaderboard_state["previous_details"][changed_game]:
|
168 |
+
# Reset all other games' states
|
169 |
+
for game in current_overall.keys():
|
170 |
+
if game != changed_game:
|
171 |
+
current_overall[game] = False
|
172 |
+
current_details[game] = False
|
173 |
+
leaderboard_state["previous_overall"][game] = False
|
174 |
+
leaderboard_state["previous_details"][game] = False
|
175 |
+
|
176 |
+
# Update state for the selected game
|
177 |
+
leaderboard_state["current_game"] = changed_game
|
178 |
+
leaderboard_state["previous_overall"][changed_game] = True
|
179 |
+
leaderboard_state["previous_details"][changed_game] = True
|
180 |
+
current_overall[changed_game] = True
|
181 |
+
|
182 |
+
# If a game's overall checkbox was checked
|
183 |
+
elif current_overall[changed_game] and not leaderboard_state["previous_overall"][changed_game]:
|
184 |
+
# If we were in details view for another game, switch to overall view
|
185 |
+
if leaderboard_state["current_game"] and leaderboard_state["previous_details"][leaderboard_state["current_game"]]:
|
186 |
+
# Reset previous game's details
|
187 |
+
leaderboard_state["previous_details"][leaderboard_state["current_game"]] = False
|
188 |
+
current_details[leaderboard_state["current_game"]] = False
|
189 |
+
leaderboard_state["current_game"] = None
|
190 |
+
|
191 |
+
# Update state
|
192 |
+
leaderboard_state["previous_overall"][changed_game] = True
|
193 |
+
leaderboard_state["previous_details"][changed_game] = False
|
194 |
+
|
195 |
+
# If a game's overall checkbox was unchecked
|
196 |
+
elif not current_overall[changed_game] and leaderboard_state["previous_overall"][changed_game]:
|
197 |
+
# If we're in details view, don't allow unchecking the overall checkbox
|
198 |
+
if leaderboard_state["current_game"] == changed_game:
|
199 |
+
current_overall[changed_game] = True
|
200 |
+
else:
|
201 |
+
leaderboard_state["previous_overall"][changed_game] = False
|
202 |
+
if leaderboard_state["current_game"] == changed_game:
|
203 |
+
leaderboard_state["current_game"] = None
|
204 |
+
|
205 |
+
# If a game's details checkbox was unchecked
|
206 |
+
elif not current_details[changed_game] and leaderboard_state["previous_details"][changed_game]:
|
207 |
+
leaderboard_state["previous_details"][changed_game] = False
|
208 |
+
if leaderboard_state["current_game"] == changed_game:
|
209 |
+
leaderboard_state["current_game"] = None
|
210 |
+
# When exiting details view, reset to show all games
|
211 |
+
for game in current_overall.keys():
|
212 |
+
current_overall[game] = True
|
213 |
+
current_details[game] = False
|
214 |
+
leaderboard_state["previous_overall"][game] = True
|
215 |
+
leaderboard_state["previous_details"][game] = False
|
216 |
+
|
217 |
+
# Special case: If all games are selected and we're trying to view details
|
218 |
+
all_games_selected = all(current_overall.values()) and not any(current_details.values())
|
219 |
+
if all_games_selected and changed_game and current_details[changed_game]:
|
220 |
+
# Reset all other games' states
|
221 |
+
for game in current_overall.keys():
|
222 |
+
if game != changed_game:
|
223 |
+
current_overall[game] = False
|
224 |
+
current_details[game] = False
|
225 |
+
leaderboard_state["previous_overall"][game] = False
|
226 |
+
leaderboard_state["previous_details"][game] = False
|
227 |
+
|
228 |
+
# Update state for the selected game
|
229 |
+
leaderboard_state["current_game"] = changed_game
|
230 |
+
leaderboard_state["previous_overall"][changed_game] = True
|
231 |
+
leaderboard_state["previous_details"][changed_game] = True
|
232 |
+
current_overall[changed_game] = True
|
233 |
+
|
234 |
+
# Build dictionary for selected games
|
235 |
+
selected_games = {
|
236 |
+
"Super Mario Bros": current_overall["Super Mario Bros"],
|
237 |
+
"Sokoban": current_overall["Sokoban"],
|
238 |
+
"2048": current_overall["2048"],
|
239 |
+
"Candy Crash": current_overall["Candy Crash"],
|
240 |
+
"Tetris (complete)": current_overall["Tetris (complete)"],
|
241 |
+
"Tetris (planning only)": current_overall["Tetris (planning only)"]
|
242 |
+
}
|
243 |
+
|
244 |
+
# Get the appropriate DataFrame and charts based on current state
|
245 |
+
if leaderboard_state["current_game"]:
|
246 |
+
# For detailed view
|
247 |
+
if leaderboard_state["current_game"] == "Super Mario Bros":
|
248 |
+
df = get_mario_leaderboard(rank_data)
|
249 |
+
elif leaderboard_state["current_game"] == "Sokoban":
|
250 |
+
df = get_sokoban_leaderboard(rank_data)
|
251 |
+
elif leaderboard_state["current_game"] == "2048":
|
252 |
+
df = get_2048_leaderboard(rank_data)
|
253 |
+
elif leaderboard_state["current_game"] == "Candy Crash":
|
254 |
+
df = get_candy_leaderboard(rank_data)
|
255 |
+
elif leaderboard_state["current_game"] == "Tetris (complete)":
|
256 |
+
df = get_tetris_leaderboard(rank_data)
|
257 |
+
else: # Tetris (planning only)
|
258 |
+
df = get_tetris_planning_leaderboard(rank_data)
|
259 |
+
|
260 |
+
# Format the DataFrame for display
|
261 |
+
display_df = prepare_dataframe_for_display(df, leaderboard_state["current_game"])
|
262 |
+
|
263 |
+
# Always create a new chart for detailed view
|
264 |
+
chart = create_horizontal_bar_chart(df, leaderboard_state["current_game"])
|
265 |
+
# For detailed view, we'll use the same chart for all visualizations
|
266 |
+
radar_chart = chart
|
267 |
+
group_bar_chart = chart
|
268 |
+
else:
|
269 |
+
# For overall view
|
270 |
+
df, group_bar_chart = get_combined_leaderboard_with_group_bar(rank_data, selected_games)
|
271 |
+
# Format the DataFrame for display
|
272 |
+
display_df = prepare_dataframe_for_display(df)
|
273 |
+
# Use the same selected_games for radar chart
|
274 |
+
_, radar_chart = get_combined_leaderboard_with_single_radar(rank_data, selected_games)
|
275 |
+
chart = group_bar_chart
|
276 |
+
|
277 |
+
# Return exactly 16 values to match the expected outputs
|
278 |
+
return (update_df_with_height(display_df), chart, radar_chart, group_bar_chart,
|
279 |
+
current_overall["Super Mario Bros"], current_details["Super Mario Bros"],
|
280 |
+
current_overall["Sokoban"], current_details["Sokoban"],
|
281 |
+
current_overall["2048"], current_details["2048"],
|
282 |
+
current_overall["Candy Crash"], current_details["Candy Crash"],
|
283 |
+
current_overall["Tetris (complete)"], current_details["Tetris (complete)"],
|
284 |
+
current_overall["Tetris (planning only)"], current_details["Tetris (planning only)"])
|
285 |
+
|
286 |
+
def update_leaderboard_with_time(time_point, mario_overall, mario_details,
|
287 |
+
sokoban_overall, sokoban_details,
|
288 |
+
_2048_overall, _2048_details,
|
289 |
+
candy_overall, candy_details,
|
290 |
+
tetris_overall, tetris_details,
|
291 |
+
tetris_plan_overall, tetris_plan_details):
|
292 |
+
# Load rank data for the selected time point
|
293 |
+
global rank_data
|
294 |
+
new_rank_data = load_rank_data(time_point)
|
295 |
+
if new_rank_data is not None:
|
296 |
+
rank_data = new_rank_data
|
297 |
+
|
298 |
+
# Use the existing update_leaderboard function
|
299 |
+
return update_leaderboard(mario_overall, mario_details,
|
300 |
+
sokoban_overall, sokoban_details,
|
301 |
+
_2048_overall, _2048_details,
|
302 |
+
candy_overall, candy_details,
|
303 |
+
tetris_overall, tetris_details,
|
304 |
+
tetris_plan_overall, tetris_plan_details)
|
305 |
+
|
306 |
+
def get_initial_state():
|
307 |
+
"""Get the initial state for the leaderboard"""
|
308 |
+
return {
|
309 |
+
"current_game": None,
|
310 |
+
"previous_overall": {
|
311 |
+
"Super Mario Bros": True,
|
312 |
+
"Sokoban": True,
|
313 |
+
"2048": True,
|
314 |
+
"Candy Crash": True,
|
315 |
+
"Tetris (complete)": True,
|
316 |
+
"Tetris (planning only)": True
|
317 |
+
},
|
318 |
+
"previous_details": {
|
319 |
+
"Super Mario Bros": False,
|
320 |
+
"Sokoban": False,
|
321 |
+
"2048": False,
|
322 |
+
"Candy Crash": False,
|
323 |
+
"Tetris (complete)": False,
|
324 |
+
"Tetris (planning only)": False
|
325 |
+
}
|
326 |
+
}
|
327 |
+
|
328 |
+
def clear_filters():
|
329 |
+
global leaderboard_state
|
330 |
+
|
331 |
+
# Reset all checkboxes to default state
|
332 |
+
selected_games = {
|
333 |
+
"Super Mario Bros": True,
|
334 |
+
"Sokoban": True,
|
335 |
+
"2048": True,
|
336 |
+
"Candy Crash": True,
|
337 |
+
"Tetris (complete)": True,
|
338 |
+
"Tetris (planning only)": True
|
339 |
+
}
|
340 |
+
|
341 |
+
# Get the combined leaderboard and group bar chart
|
342 |
+
df, group_bar_chart = get_combined_leaderboard_with_group_bar(rank_data, selected_games)
|
343 |
+
|
344 |
+
# Format the DataFrame for display
|
345 |
+
display_df = prepare_dataframe_for_display(df)
|
346 |
+
|
347 |
+
# Get the radar chart using the same selected games
|
348 |
+
_, radar_chart = get_combined_leaderboard_with_single_radar(rank_data, selected_games)
|
349 |
+
|
350 |
+
# Reset the leaderboard state to match the default checkbox states
|
351 |
+
leaderboard_state = get_initial_state()
|
352 |
+
|
353 |
+
# Return exactly 16 values to match the expected outputs
|
354 |
+
return (update_df_with_height(display_df), group_bar_chart, radar_chart, group_bar_chart,
|
355 |
+
True, False, # mario
|
356 |
+
True, False, # sokoban
|
357 |
+
True, False, # 2048
|
358 |
+
True, False, # candy
|
359 |
+
True, False, # tetris
|
360 |
+
True, False) # tetris plan
|
361 |
+
|
362 |
+
def create_timeline_slider():
|
363 |
+
"""Create a custom timeline slider component"""
|
364 |
+
timeline_html = """
|
365 |
+
<div class="timeline-container">
|
366 |
<style>
|
367 |
+
.timeline-container {
|
368 |
+
width: 85%; /* Increased from 70% to 85% */
|
369 |
+
padding: 8px;
|
370 |
+
font-family: Arial, sans-serif;
|
371 |
+
height: 40px;
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
372 |
display: flex;
|
373 |
align-items: center;
|
374 |
+
}
|
375 |
+
.timeline-track {
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
376 |
position: relative;
|
377 |
+
height: 6px;
|
378 |
+
background: #e0e0e0;
|
379 |
+
border-radius: 3px;
|
380 |
+
margin: 0;
|
|
|
|
|
|
|
|
|
381 |
width: 100%;
|
382 |
+
}
|
383 |
+
.timeline-progress {
|
384 |
+
position: absolute;
|
385 |
height: 100%;
|
386 |
+
background: #2196F3;
|
387 |
+
border-radius: 3px;
|
388 |
+
width: 100%;
|
389 |
+
}
|
390 |
+
.timeline-handle {
|
391 |
+
position: absolute;
|
392 |
+
right: 0;
|
393 |
+
top: 50%;
|
394 |
+
transform: translate(50%, -50%);
|
395 |
+
width: 20px;
|
396 |
+
height: 20px;
|
397 |
+
background: #2196F3;
|
398 |
+
border: 3px solid white;
|
399 |
+
border-radius: 50%;
|
400 |
+
cursor: pointer;
|
401 |
+
box-shadow: 0 2px 6px rgba(0,0,0,0.3);
|
402 |
+
}
|
403 |
+
.timeline-date {
|
404 |
+
position: absolute;
|
405 |
+
top: -25px;
|
406 |
+
transform: translateX(-50%);
|
407 |
+
background: #2196F3; /* Changed to match slider blue color */
|
408 |
+
color: #ffffff !important;
|
409 |
+
padding: 3px 8px;
|
410 |
+
border-radius: 4px;
|
411 |
+
font-size: 12px;
|
412 |
+
white-space: nowrap;
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
413 |
font-weight: 600;
|
414 |
+
box-shadow: 0 2px 6px rgba(0,0,0,0.2);
|
415 |
+
letter-spacing: 0.5px;
|
416 |
+
text-shadow: 0 1px 2px rgba(0,0,0,0.2);
|
417 |
+
}
|
|
|
|
|
|
|
|
|
|
|
418 |
</style>
|
419 |
+
<div class="timeline-track">
|
420 |
+
<div class="timeline-progress"></div>
|
421 |
+
<div class="timeline-handle">
|
422 |
+
<div class="timeline-date">03/25/2025</div>
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
423 |
</div>
|
424 |
</div>
|
425 |
+
</div>
|
426 |
+
<script>
|
427 |
+
(function() {
|
428 |
+
const container = document.querySelector('.timeline-container');
|
429 |
+
const track = container.querySelector('.timeline-track');
|
430 |
+
const handle = container.querySelector('.timeline-handle');
|
431 |
+
let isDragging = false;
|
432 |
+
|
433 |
+
// For now, we only have one time point
|
434 |
+
const timePoints = {
|
435 |
+
"03/25/2025": 1.0
|
436 |
+
};
|
437 |
+
|
438 |
+
function updatePosition(e) {
|
439 |
+
if (!isDragging) return;
|
440 |
+
|
441 |
+
const rect = track.getBoundingClientRect();
|
442 |
+
let x = (e.clientX - rect.left) / rect.width;
|
443 |
+
x = Math.max(0, Math.min(1, x));
|
444 |
+
|
445 |
+
// For now, snap to the only available time point
|
446 |
+
x = 1.0;
|
447 |
+
|
448 |
+
handle.style.right = `${(1 - x) * 100}%`;
|
449 |
+
}
|
450 |
+
|
451 |
+
handle.addEventListener('mousedown', (e) => {
|
452 |
+
isDragging = true;
|
453 |
+
e.preventDefault();
|
454 |
+
});
|
455 |
+
|
456 |
+
document.addEventListener('mousemove', updatePosition);
|
457 |
+
document.addEventListener('mouseup', () => {
|
458 |
+
isDragging = false;
|
459 |
+
});
|
460 |
+
|
461 |
+
// Prevent text selection while dragging
|
462 |
+
container.addEventListener('selectstart', (e) => {
|
463 |
+
if (isDragging) e.preventDefault();
|
464 |
+
});
|
465 |
+
})();
|
466 |
+
</script>
|
467 |
+
"""
|
468 |
+
return gr.HTML(timeline_html)
|
469 |
+
|
470 |
+
def build_app():
|
471 |
+
with gr.Blocks(css="""
|
472 |
+
.visualization-container .js-plotly-plot {
|
473 |
+
margin-left: auto !important;
|
474 |
+
margin-right: auto !important;
|
475 |
+
display: block !important;
|
476 |
+
}
|
477 |
+
|
478 |
+
/* Optional: limit width for better layout on large screens */
|
479 |
+
.visualization-container .js-plotly-plot {
|
480 |
+
max-width: 1000px;
|
481 |
+
}
|
482 |
+
|
483 |
+
.section-title {
|
484 |
+
font-size: 1.5em;
|
485 |
+
font-weight: bold;
|
486 |
+
color: #2c3e50;
|
487 |
+
margin-bottom: 15px;
|
488 |
+
padding-bottom: 10px;
|
489 |
+
border-bottom: 2px solid #e9ecef;
|
490 |
+
text-align: center;
|
491 |
+
}
|
492 |
+
.container {
|
493 |
+
max-width: 1400px;
|
494 |
+
margin: 0 auto;
|
495 |
+
padding: 0 20px;
|
496 |
+
}
|
497 |
|
498 |
+
/* Enhanced table styling - SIMPLIFIED */
|
499 |
+
.table-container {
|
500 |
+
height: 800px !important;
|
501 |
+
max-height: 1000px !important;
|
502 |
+
overflow-y: auto !important; /* ONLY the outer container gets scrolling */
|
503 |
+
border-radius: 8px;
|
504 |
+
box-shadow: 0 2px 10px rgba(0,0,0,0.1);
|
505 |
+
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
506 |
|
507 |
+
/* Prevent inner containers from having scrollbars */
|
508 |
+
.table-container > div,
|
509 |
+
.table-container > div > div,
|
510 |
+
.gradio-dataframe > div,
|
511 |
+
[data-testid="dataframe"] > div {
|
512 |
+
overflow: visible !important;
|
513 |
+
height: auto !important;
|
514 |
+
}
|
515 |
+
|
516 |
+
/* Fix table styling */
|
517 |
+
.table-container table {
|
518 |
+
width: 100%;
|
519 |
+
border-collapse: separate;
|
520 |
+
border-spacing: 0;
|
521 |
+
}
|
522 |
+
|
523 |
+
/* Make headers sticky */
|
524 |
+
.table-container th {
|
525 |
+
position: sticky !important;
|
526 |
+
top: 0 !important;
|
527 |
+
background-color: #f8f9fa !important;
|
528 |
+
z-index: 10 !important;
|
529 |
+
font-weight: bold;
|
530 |
+
padding: 12px;
|
531 |
+
border-bottom: 2px solid #e9ecef;
|
532 |
+
}
|
533 |
+
|
534 |
+
/* Simple cell styling */
|
535 |
+
.table-container td {
|
536 |
+
padding: 10px 12px;
|
537 |
+
border-bottom: 1px solid #e9ecef;
|
538 |
+
}
|
539 |
+
|
540 |
+
/* Visual enhancements */
|
541 |
+
.table-container tr:hover {
|
542 |
+
background-color: #f1f3f4;
|
543 |
+
}
|
544 |
+
|
545 |
+
.table-container tr:nth-child(even) {
|
546 |
+
background-color: #f8fafc;
|
547 |
+
}
|
548 |
+
|
549 |
+
/* Row containing the table */
|
550 |
+
.gradio-container .gr-row {
|
551 |
+
min-height: auto !important;
|
552 |
+
height: auto !important;
|
553 |
+
overflow: visible !important;
|
554 |
+
margin-bottom: 20px;
|
555 |
+
}
|
556 |
+
""") as demo:
|
557 |
+
gr.Markdown("# 🎮 Game Arena: Gaming Agent 🎲")
|
558 |
+
|
559 |
+
with gr.Tabs():
|
560 |
+
with gr.Tab("🏆 Leaderboard"):
|
561 |
+
# Visualization section
|
562 |
+
with gr.Row():
|
563 |
+
gr.Markdown("### 📊 Data Visualization")
|
564 |
+
|
565 |
+
# Detailed view visualization (single chart)
|
566 |
+
detailed_visualization = gr.Plot(
|
567 |
+
label="Performance Visualization",
|
568 |
+
visible=False,
|
569 |
+
elem_classes="visualization-container"
|
570 |
+
)
|
571 |
+
|
572 |
+
with gr.Column(visible=True) as overall_visualizations:
|
573 |
+
with gr.Tabs():
|
574 |
+
with gr.Tab("📈 Radar Chart"):
|
575 |
+
radar_visualization = gr.Plot(
|
576 |
+
label="Comparative Analysis (Radar Chart)",
|
577 |
+
elem_classes="visualization-container"
|
578 |
+
)
|
579 |
+
with gr.Tab("📊 Group Bar Chart"):
|
580 |
+
group_bar_visualization = gr.Plot(
|
581 |
+
label="Comparative Analysis (Group Bar Chart)",
|
582 |
+
elem_classes="visualization-container"
|
583 |
+
)
|
584 |
+
|
585 |
+
|
586 |
+
# Game selection section
|
587 |
+
with gr.Row():
|
588 |
+
gr.Markdown("### 🎮 Game Selection")
|
589 |
+
with gr.Row():
|
590 |
+
with gr.Column():
|
591 |
+
gr.Markdown("**🎮 Super Mario Bros**")
|
592 |
+
mario_overall = gr.Checkbox(label="Super Mario Bros Score", value=True)
|
593 |
+
mario_details = gr.Checkbox(label="Super Mario Bros Details", value=False)
|
594 |
+
with gr.Column():
|
595 |
+
gr.Markdown("**📦 Sokoban**")
|
596 |
+
sokoban_overall = gr.Checkbox(label="Sokoban Score", value=True)
|
597 |
+
sokoban_details = gr.Checkbox(label="Sokoban Details", value=False)
|
598 |
+
with gr.Column():
|
599 |
+
gr.Markdown("**🔢 2048**")
|
600 |
+
_2048_overall = gr.Checkbox(label="2048 Score", value=True)
|
601 |
+
_2048_details = gr.Checkbox(label="2048 Details", value=False)
|
602 |
+
with gr.Column():
|
603 |
+
gr.Markdown("**🍬 Candy Crash**")
|
604 |
+
candy_overall = gr.Checkbox(label="Candy Crash Score", value=True)
|
605 |
+
candy_details = gr.Checkbox(label="Candy Crash Details", value=False)
|
606 |
+
with gr.Column():
|
607 |
+
gr.Markdown("**🎯 Tetris (complete)**")
|
608 |
+
tetris_overall = gr.Checkbox(label="Tetris (complete) Score", value=True)
|
609 |
+
tetris_details = gr.Checkbox(label="Tetris (complete) Details", value=False)
|
610 |
+
with gr.Column():
|
611 |
+
gr.Markdown("**📋 Tetris (planning)**")
|
612 |
+
tetris_plan_overall = gr.Checkbox(label="Tetris (planning) Score", value=True)
|
613 |
+
tetris_plan_details = gr.Checkbox(label="Tetris (planning) Details", value=False)
|
614 |
+
|
615 |
+
# Controls
|
616 |
+
with gr.Row():
|
617 |
+
with gr.Column(scale=2):
|
618 |
+
gr.Markdown("**⏰ Time Tracker**")
|
619 |
+
timeline = create_timeline_slider()
|
620 |
+
with gr.Column(scale=1):
|
621 |
+
gr.Markdown("**🔄 Controls**")
|
622 |
+
clear_btn = gr.Button("Reset Filters", variant="secondary")
|
623 |
+
|
624 |
+
# Leaderboard table
|
625 |
+
with gr.Row():
|
626 |
+
gr.Markdown("### 📋 Detailed Results")
|
627 |
+
|
628 |
+
# Add leaderboard search box in its own row
|
629 |
+
with gr.Row():
|
630 |
+
with gr.Column(scale=8):
|
631 |
+
search_box = gr.Textbox(
|
632 |
+
label="🔍 Search by Player or Organization",
|
633 |
+
placeholder="Type to filter the table...",
|
634 |
+
show_label=True
|
635 |
+
)
|
636 |
+
with gr.Column(scale=1):
|
637 |
+
search_clear_btn = gr.Button("Clear", variant="secondary")
|
638 |
+
|
639 |
+
# Get initial leaderboard dataframe
|
640 |
+
initial_df = get_combined_leaderboard(rank_data, {
|
641 |
+
"Super Mario Bros": True,
|
642 |
+
"Sokoban": True,
|
643 |
+
"2048": True,
|
644 |
+
"Candy Crash": True,
|
645 |
+
"Tetris (complete)": True,
|
646 |
+
"Tetris (planning only)": True
|
647 |
+
})
|
648 |
+
|
649 |
+
# Format the DataFrame for display
|
650 |
+
initial_display_df = prepare_dataframe_for_display(initial_df)
|
651 |
+
|
652 |
+
# Create a standard DataFrame component with enhanced styling
|
653 |
+
with gr.Row():
|
654 |
+
leaderboard_df = gr.DataFrame(
|
655 |
+
value=initial_display_df,
|
656 |
+
interactive=True,
|
657 |
+
elem_id="leaderboard-table",
|
658 |
+
elem_classes="table-container",
|
659 |
+
wrap=True,
|
660 |
+
column_widths={"Player": "25%", "Organization": "20%"},
|
661 |
+
height=800
|
662 |
+
)
|
663 |
+
|
664 |
+
# Add the score note below the table
|
665 |
+
with gr.Row():
|
666 |
+
score_note = add_score_note()
|
667 |
+
|
668 |
+
# Add search functionality
|
669 |
+
def filter_table(search_term, current_df):
|
670 |
+
# Create a fresh copy of the DataFrame to avoid modifying the original
|
671 |
+
filtered_df = current_df.copy()
|
672 |
+
|
673 |
+
# Only apply search if a term is provided
|
674 |
+
if search_term:
|
675 |
+
# Filter the DataFrame by Player or Organization
|
676 |
+
filtered_df = filtered_df[
|
677 |
+
filtered_df["Player"].str.contains(search_term, case=False) |
|
678 |
+
filtered_df["Organization"].str.contains(search_term, case=False)
|
679 |
+
]
|
680 |
+
|
681 |
+
return filtered_df
|
682 |
+
|
683 |
+
# Connect search box to the table
|
684 |
+
search_box.change(
|
685 |
+
filter_table,
|
686 |
+
inputs=[search_box, leaderboard_df],
|
687 |
+
outputs=[leaderboard_df]
|
688 |
+
)
|
689 |
+
|
690 |
+
# Clear search button functionality
|
691 |
+
def clear_search():
|
692 |
+
return "", initial_display_df
|
693 |
+
|
694 |
+
search_clear_btn.click(
|
695 |
+
clear_search,
|
696 |
+
inputs=[],
|
697 |
+
outputs=[search_box, leaderboard_df]
|
698 |
+
)
|
699 |
+
|
700 |
+
# List of all checkboxes
|
701 |
+
checkbox_list = [
|
702 |
+
mario_overall, mario_details,
|
703 |
+
sokoban_overall, sokoban_details,
|
704 |
+
_2048_overall, _2048_details,
|
705 |
+
candy_overall, candy_details,
|
706 |
+
tetris_overall, tetris_details,
|
707 |
+
tetris_plan_overall, tetris_plan_details
|
708 |
+
]
|
709 |
+
|
710 |
+
# Update visualizations when checkboxes change
|
711 |
+
def update_visualizations(*checkbox_states):
|
712 |
+
# Check if any details checkbox is selected
|
713 |
+
is_details_view = any([
|
714 |
+
checkbox_states[1], checkbox_states[3], checkbox_states[5],
|
715 |
+
checkbox_states[7], checkbox_states[9], checkbox_states[11]
|
716 |
+
])
|
717 |
+
|
718 |
+
# Update visibility of visualization blocks
|
719 |
+
return {
|
720 |
+
detailed_visualization: gr.update(visible=is_details_view),
|
721 |
+
overall_visualizations: gr.update(visible=not is_details_view)
|
722 |
+
}
|
723 |
+
|
724 |
+
# Add change event to all checkboxes
|
725 |
+
for checkbox in checkbox_list:
|
726 |
+
checkbox.change(
|
727 |
+
update_visualizations,
|
728 |
+
inputs=checkbox_list,
|
729 |
+
outputs=[detailed_visualization, overall_visualizations]
|
730 |
+
)
|
731 |
+
|
732 |
+
# Update leaderboard and visualizations when checkboxes change
|
733 |
+
for checkbox in checkbox_list:
|
734 |
+
checkbox.change(
|
735 |
+
update_leaderboard,
|
736 |
+
inputs=checkbox_list,
|
737 |
+
outputs=[
|
738 |
+
leaderboard_df,
|
739 |
+
detailed_visualization,
|
740 |
+
radar_visualization,
|
741 |
+
group_bar_visualization
|
742 |
+
] + checkbox_list
|
743 |
+
)
|
744 |
+
|
745 |
+
# Update when clear button is clicked
|
746 |
+
clear_btn.click(
|
747 |
+
clear_filters,
|
748 |
+
inputs=[],
|
749 |
+
outputs=[
|
750 |
+
leaderboard_df,
|
751 |
+
detailed_visualization,
|
752 |
+
radar_visualization,
|
753 |
+
group_bar_visualization
|
754 |
+
] + checkbox_list
|
755 |
+
)
|
756 |
+
|
757 |
+
# Initialize the app
|
758 |
+
demo.load(
|
759 |
+
fn=clear_filters,
|
760 |
+
inputs=[],
|
761 |
+
outputs=[
|
762 |
+
leaderboard_df,
|
763 |
+
detailed_visualization,
|
764 |
+
radar_visualization,
|
765 |
+
group_bar_visualization
|
766 |
+
] + checkbox_list
|
767 |
+
)
|
768 |
+
|
769 |
+
with gr.Tab("🎥 Gallery"):
|
770 |
+
video_gallery = create_video_gallery()
|
771 |
+
|
772 |
+
return demo
|
773 |
+
|
774 |
+
if __name__ == "__main__":
|
775 |
+
demo_app = build_app()
|
776 |
+
# Add file serving configuration
|
777 |
+
demo_app.launch(debug=True, show_error=True, share=True)
|
requirements.txt
CHANGED
@@ -16,4 +16,5 @@ tokenizers>=0.15.0
|
|
16 |
sentencepiece
|
17 |
seaborn>=0.12.0
|
18 |
Pillow>=10.0.0
|
19 |
-
plotly>=5.15.0
|
|
|
|
16 |
sentencepiece
|
17 |
seaborn>=0.12.0
|
18 |
Pillow>=10.0.0
|
19 |
+
plotly>=5.15.0
|
20 |
+
gradio>=4.0.0
|