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| from app.core.cache import cache_config | |
| from datetime import datetime | |
| from typing import List, Dict, Any | |
| import datasets | |
| from fastapi import HTTPException | |
| import logging | |
| from app.config.base import HF_ORGANIZATION | |
| from app.utils.logging import LogFormatter | |
| logger = logging.getLogger(__name__) | |
| class LeaderboardService: | |
| def __init__(self): | |
| pass | |
| async def fetch_raw_data(self) -> List[Dict[str, Any]]: | |
| """Fetch raw leaderboard data from HuggingFace dataset""" | |
| try: | |
| logger.info(LogFormatter.section("FETCHING LEADERBOARD DATA")) | |
| logger.info(LogFormatter.info(f"Loading dataset from {HF_ORGANIZATION}/contents")) | |
| dataset = datasets.load_dataset( | |
| f"{HF_ORGANIZATION}/contents", | |
| cache_dir=cache_config.get_cache_path("datasets") | |
| )["train"] | |
| df = dataset.to_pandas() | |
| data = df.to_dict('records') | |
| stats = { | |
| "Total_Entries": len(data), | |
| "Dataset_Size": f"{df.memory_usage(deep=True).sum() / 1024 / 1024:.1f}MB" | |
| } | |
| for line in LogFormatter.stats(stats, "Dataset Statistics"): | |
| logger.info(line) | |
| return data | |
| except Exception as e: | |
| logger.error(LogFormatter.error("Failed to fetch leaderboard data", e)) | |
| raise HTTPException(status_code=500, detail=str(e)) | |
| async def get_formatted_data(self) -> List[Dict[str, Any]]: | |
| """Get formatted leaderboard data""" | |
| try: | |
| logger.info(LogFormatter.section("FORMATTING LEADERBOARD DATA")) | |
| raw_data = await self.fetch_raw_data() | |
| formatted_data = [] | |
| type_counts = {} | |
| error_count = 0 | |
| # Initialize progress tracking | |
| total_items = len(raw_data) | |
| logger.info(LogFormatter.info(f"Processing {total_items:,} entries...")) | |
| for i, item in enumerate(raw_data, 1): | |
| try: | |
| formatted_item = await self.transform_data(item) | |
| formatted_data.append(formatted_item) | |
| # Count model types | |
| model_type = formatted_item["model"]["type"] | |
| type_counts[model_type] = type_counts.get(model_type, 0) + 1 | |
| except Exception as e: | |
| error_count += 1 | |
| logger.error(LogFormatter.error(f"Failed to format entry {i}/{total_items}", e)) | |
| continue | |
| # Log progress every 10% | |
| if i % max(1, total_items // 10) == 0: | |
| progress = (i / total_items) * 100 | |
| logger.info(LogFormatter.info(f"Progress: {LogFormatter.progress_bar(i, total_items)}")) | |
| # Log final statistics | |
| stats = { | |
| "Total_Processed": total_items, | |
| "Successful": len(formatted_data), | |
| "Failed": error_count | |
| } | |
| logger.info(LogFormatter.section("PROCESSING SUMMARY")) | |
| for line in LogFormatter.stats(stats, "Processing Statistics"): | |
| logger.info(line) | |
| # Log model type distribution | |
| type_stats = {f"Type_{k}": v for k, v in type_counts.items()} | |
| logger.info(LogFormatter.subsection("MODEL TYPE DISTRIBUTION")) | |
| for line in LogFormatter.stats(type_stats): | |
| logger.info(line) | |
| return formatted_data | |
| except Exception as e: | |
| logger.error(LogFormatter.error("Failed to format leaderboard data", e)) | |
| raise HTTPException(status_code=500, detail=str(e)) | |
| async def transform_data(self, data: Dict[str, Any]) -> Dict[str, Any]: | |
| """Transform raw data into the format expected by the frontend""" | |
| try: | |
| # Extract model name for logging | |
| model_name = data.get("fullname", "Unknown") | |
| logger.debug(LogFormatter.info(f"Transforming data for model: {model_name}")) | |
| # Create unique ID combining model name, precision, sha and chat template status | |
| unique_id = f"{data.get('fullname', 'Unknown')}_{data.get('Precision', 'Unknown')}_{data.get('Model sha', 'Unknown')}_{str(data.get('Chat Template', False))}" | |
| evaluations = { | |
| "ifeval": { | |
| "name": "IFEval", | |
| "value": data.get("IFEval Raw", 0), | |
| "normalized_score": data.get("IFEval", 0) | |
| }, | |
| "bbh": { | |
| "name": "BBH", | |
| "value": data.get("BBH Raw", 0), | |
| "normalized_score": data.get("BBH", 0) | |
| }, | |
| "math": { | |
| "name": "MATH Level 5", | |
| "value": data.get("MATH Lvl 5 Raw", 0), | |
| "normalized_score": data.get("MATH Lvl 5", 0) | |
| }, | |
| "gpqa": { | |
| "name": "GPQA", | |
| "value": data.get("GPQA Raw", 0), | |
| "normalized_score": data.get("GPQA", 0) | |
| }, | |
| "musr": { | |
| "name": "MUSR", | |
| "value": data.get("MUSR Raw", 0), | |
| "normalized_score": data.get("MUSR", 0) | |
| }, | |
| "mmlu_pro": { | |
| "name": "MMLU-PRO", | |
| "value": data.get("MMLU-PRO Raw", 0), | |
| "normalized_score": data.get("MMLU-PRO", 0) | |
| } | |
| } | |
| features = { | |
| "is_not_available_on_hub": data.get("Available on the hub", False), | |
| "is_merged": data.get("Merged", False), | |
| "is_moe": data.get("MoE", False), | |
| "is_flagged": data.get("Flagged", False), | |
| "is_highlighted_by_maintainer": data.get("Official Providers", False) | |
| } | |
| metadata = { | |
| "upload_date": data.get("Upload To Hub Date"), | |
| "submission_date": data.get("Submission Date"), | |
| "generation": data.get("Generation"), | |
| "base_model": data.get("Base Model"), | |
| "hub_license": data.get("Hub License"), | |
| "hub_hearts": data.get("Hub ❤️"), | |
| "params_billions": data.get("#Params (B)"), | |
| "co2_cost": data.get("CO₂ cost (kg)", 0) | |
| } | |
| # Clean model type by removing emojis if present | |
| original_type = data.get("Type", "") | |
| model_type = original_type.lower().strip() | |
| # Remove emojis and parentheses | |
| if "(" in model_type: | |
| model_type = model_type.split("(")[0].strip() | |
| model_type = ''.join(c for c in model_type if not c in '🔶🟢🟩💬🤝🌸 ') | |
| # Map old model types to new ones | |
| model_type_mapping = { | |
| "fine-tuned": "fined-tuned-on-domain-specific-dataset", | |
| "fine tuned": "fined-tuned-on-domain-specific-dataset", | |
| "finetuned": "fined-tuned-on-domain-specific-dataset", | |
| "fine_tuned": "fined-tuned-on-domain-specific-dataset", | |
| "ft": "fined-tuned-on-domain-specific-dataset", | |
| "finetuning": "fined-tuned-on-domain-specific-dataset", | |
| "fine tuning": "fined-tuned-on-domain-specific-dataset", | |
| "fine-tuning": "fined-tuned-on-domain-specific-dataset" | |
| } | |
| mapped_type = model_type_mapping.get(model_type.lower().strip(), model_type) | |
| if mapped_type != model_type: | |
| logger.debug(LogFormatter.info(f"Model type mapped: {original_type} -> {mapped_type}")) | |
| transformed_data = { | |
| "id": unique_id, | |
| "model": { | |
| "name": data.get("fullname"), | |
| "sha": data.get("Model sha"), | |
| "precision": data.get("Precision"), | |
| "type": mapped_type, | |
| "weight_type": data.get("Weight type"), | |
| "architecture": data.get("Architecture"), | |
| "average_score": data.get("Average ⬆️"), | |
| "has_chat_template": data.get("Chat Template", False) | |
| }, | |
| "evaluations": evaluations, | |
| "features": features, | |
| "metadata": metadata | |
| } | |
| logger.debug(LogFormatter.success(f"Successfully transformed data for {model_name}")) | |
| return transformed_data | |
| except Exception as e: | |
| logger.error(LogFormatter.error(f"Failed to transform data for {data.get('fullname', 'Unknown')}", e)) | |
| raise |