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from typing import Optional, Dict
import pandas as pd
from functools import lru_cache
from huggingface_hub import snapshot_download
import logging
from config import CONFIG
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class DataManager:
def __init__(self):
self._leaderboard_data: Optional[pd.DataFrame] = None
self._responses_data: Optional[pd.DataFrame] = None
self._section_results_data: Optional[pd.DataFrame] = None
@lru_cache(maxsize=1)
def _load_dataset(self, path: str) -> pd.DataFrame:
"""Load dataset with caching."""
try:
return pd.read_parquet(path)
except Exception as e:
logger.error(f"Error loading dataset from {path}: {e}")
raise RuntimeError(f"Failed to load dataset: {e}")
def refresh_datasets(self) -> None:
"""Refresh all datasets from source."""
try:
snapshot_download(
repo_id="alibayram",
repo_type="dataset",
local_dir=CONFIG["dataset"].cache_dir
)
# Clear cache to force reload
self._load_dataset.cache_clear()
logger.info("Datasets refreshed successfully")
except Exception as e:
logger.error(f"Error refreshing datasets: {e}")
@property
def leaderboard_data(self) -> pd.DataFrame:
if self._leaderboard_data is None:
self._leaderboard_data = self._load_dataset(CONFIG["dataset"].leaderboard_path)
return self._leaderboard_data
@property
def responses_data(self) -> pd.DataFrame:
if self._responses_data is None:
self._responses_data = self._load_dataset(CONFIG["dataset"].responses_path)
return self._responses_data
@property
def section_results_data(self) -> pd.DataFrame:
if self._section_results_data is None:
self._section_results_data = self._load_dataset(CONFIG["dataset"].section_results_path)
return self._section_results_data
# Global instance
data_manager = DataManager() |