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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
OSM_SCORES = pd.read_json(file_path)
# Reset index so model names become a column and transpose for (year, name) pairs as rows
OSM_SCORES = OSM_SCORES.T.reset_index()
# Rename the first column as 'Model' to keep model names visible
OSM_SCORES.rename(columns={'index': 'Model'}, inplace=True)
# Filter columns that contain 'Egzaminy Gimnazjalne' in the name
filtered_columns = ['Model'] + [col for col in OSM_SCORES.columns if "Egzaminy Ósmoklasisty" in col]
OSM_SCORES = OSM_SCORES[filtered_columns]
OSM_SCORES["Model"] = OSM_SCORES["Model"].apply(
lambda name: f"[{name.replace('__','/')}](https://huggingface.co/{name.replace('__','/')})"
)
# Round numeric values to 2 decimal places
numeric_columns = OSM_SCORES.columns[1:] # Get all year columns
OSM_SCORES[numeric_columns] = OSM_SCORES[numeric_columns].apply(pd.to_numeric, errors='coerce') * 100
OSM_SCORES[numeric_columns] = OSM_SCORES[numeric_columns].round(2)
# Convert year part in column names to strings for Gradio compatibility
OSM_SCORES.columns = [col.split(',')[0][1:] if col != 'Model' else col for col in OSM_SCORES.columns]
year_columns = OSM_SCORES.columns[1:]
sorted_year_columns = sorted(year_columns.astype(str).tolist()) # Sort the year columns as strings
sorted_columns = ['Model'] + sorted_year_columns
OSM_SCORES = OSM_SCORES[sorted_columns]
# Sort alphabetically by model name
OSM_SCORES = OSM_SCORES.sort_values(by='Model')
return OSM_SCORES
# Define file path
file_path = str(abs_path / "leaderboards/all_types_years.json")
OSM_SCORES = load_json_data(file_path)
OSM_SCORES = OSM_SCORES.style.highlight_max(
color = highlight_color,
subset=OSM_SCORES.columns[-5:]).format(precision=2)
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