Spaces:
Running
Running
File size: 2,394 Bytes
1fc3adb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
"""
Leaderboard Screen for TraceMind-AI
Displays evaluation runs with MCP-powered insights
"""
import pandas as pd
import gradio as gr
from typing import Optional, Tuple
def prepare_leaderboard_data(df: pd.DataFrame) -> pd.DataFrame:
"""
Prepare leaderboard dataframe for display
Args:
df: Raw leaderboard dataframe from HuggingFace
Returns:
Formatted dataframe for display
"""
if df.empty:
return pd.DataFrame()
# Select and reorder columns for display
display_columns = [
'model', 'agent_type', 'provider', 'success_rate',
'total_tests', 'avg_duration_ms', 'total_cost_usd',
'co2_emissions_g', 'gpu_utilization_avg', 'submitted_by', 'timestamp'
]
# Only include columns that exist
available_columns = [col for col in display_columns if col in df.columns]
display_df = df[available_columns].copy()
# Round numeric columns
if 'success_rate' in display_df.columns:
display_df['success_rate'] = display_df['success_rate'].round(1)
if 'avg_duration_ms' in display_df.columns:
display_df['avg_duration_ms'] = display_df['avg_duration_ms'].round(0)
if 'total_cost_usd' in display_df.columns:
display_df['total_cost_usd'] = display_df['total_cost_usd'].round(4)
if 'co2_emissions_g' in display_df.columns:
display_df['co2_emissions_g'] = display_df['co2_emissions_g'].round(2)
if 'gpu_utilization_avg' in display_df.columns:
display_df['gpu_utilization_avg'] = display_df['gpu_utilization_avg'].round(1)
# Sort by success rate descending by default
if 'success_rate' in display_df.columns:
display_df = display_df.sort_values('success_rate', ascending=False)
return display_df
def get_run_id_from_selection(
df: pd.DataFrame,
evt: gr.SelectData
) -> Optional[str]:
"""
Extract run_id from a selected row in the dataframe
Args:
df: Full leaderboard dataframe (with run_id column)
evt: Gradio SelectData event from dataframe click
Returns:
run_id string or None
"""
if df.empty or evt is None:
return None
try:
row_index = evt.index[0] # evt.index is (row, col)
if row_index < len(df):
return df.iloc[row_index]['run_id']
except (IndexError, KeyError, AttributeError):
return None
return None
|