Spaces:
Running
Running
File size: 2,009 Bytes
c5afbf5 fc9fd5b c5afbf5 e88ceea c5afbf5 fc9fd5b c5afbf5 |
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 |
import pandas as pd
from pathlib import Path
from ..styles import highlight_color
# Define the absolute path to the file
abs_path = Path(__file__).parent.parent.parent
def load_json_data(file_path):
# Load the JSON data
ZAW_SCORES = pd.read_json(file_path)
# Reset index so model names become a column and transpose for (year, name) pairs as rows
ZAW_SCORES = ZAW_SCORES.T.reset_index()
# Rename the first column as 'Model' to keep model names visible
ZAW_SCORES.rename(columns={'index': 'Model'}, inplace=True)
# Filter columns that contain 'Egzaminy Gimnazjalne' in the name
filtered_columns = ['Model'] + [col for col in ZAW_SCORES.columns if "Egzaminy Zawodowe" in col]
ZAW_SCORES = ZAW_SCORES[filtered_columns]
ZAW_SCORES["Model"] = ZAW_SCORES["Model"].apply(
lambda name: f"[{name.replace('__','/')}](https://huggingface.co/{name.replace('__','/')})"
)
# Round numeric values to 2 decimal places
numeric_columns = ZAW_SCORES.columns[1:] # Get all year columns
ZAW_SCORES[numeric_columns] = ZAW_SCORES[numeric_columns].apply(pd.to_numeric, errors='coerce') * 100
ZAW_SCORES[numeric_columns] = ZAW_SCORES[numeric_columns].round(2)
# Convert year part in column names to strings for Gradio compatibility
ZAW_SCORES.columns = [col.split(',')[0][1:] if col != 'Model' else col for col in ZAW_SCORES.columns]
year_columns = ZAW_SCORES.columns[1:]
sorted_year_columns = sorted(year_columns.astype(str).tolist()) # Sort the year columns as strings
sorted_columns = ['Model'] + sorted_year_columns
ZAW_SCORES = ZAW_SCORES[sorted_columns]
# Sort alphabetically by model name
ZAW_SCORES = ZAW_SCORES.sort_values(by='Model')
return ZAW_SCORES
# Define file path
file_path = str(abs_path / "leaderboards/all_types_years.json")
ZAW_SCORES = load_json_data(file_path)
ZAW_SCORES = ZAW_SCORES.style.highlight_max(
color = highlight_color,
subset=ZAW_SCORES.columns[-12:]).format(precision=2)
|