feat(leaderboard): upload leaderboard data from spectrumbench v1.0

Author: little1d

README.md

@@ -35,7 +35,7 @@ uv pip install -e .
 
    Benchmark data is hosted on Hugging Face. Please download it from the following link:
 
-   [https://huggingface.co/datasets/SpectrumWorld/spectrumbench_v_1.0](https://huggingface.co/datasets/SpectrumWorld/spectrumbench_v_1.0)
+   [https://huggingface.co/datasets/SpectrumWorld/spectrumbench_v_1.0](https://huggingface.co/SpectrumWorld/spectrumbench_v_1.0/tree/main)
 
    After downloading, extract the data to the `data` directory in the project root.
 

leaderboard/batch_import_models.py

@@ -0,0 +1,50 @@
+#!/usr/bin/env python3
+"""
+Batch import models to leaderboard
+Usage: python batch_import_models.py models_data.json
+"""
+
+import json
+import sys
+from pathlib import Path
+
+# Add the leaderboard directory to Python path
+sys.path.insert(0, str(Path(__file__).parent))
+
+from manage_leaderboard import LeaderboardManager
+
+
+def batch_import(models_file: str):
+    """Batch import models from JSON file"""
+
+    with open(models_file, "r", encoding="utf-8") as f:
+        models_data = json.load(f)
+
+    manager = LeaderboardManager()
+
+    # Clear existing models if specified
+    if models_data.get("clear_existing", False):
+        print("🗑️ Clearing existing models...")
+        manager.data["models"] = []
+
+    # Import models
+    imported_count = 0
+    for model_data in models_data["models"]:
+        model_info = model_data["model_info"]
+        subcategory_scores = model_data["scores"]
+
+        success = manager.add_model(model_info, subcategory_scores)
+        if success:
+            imported_count += 1
+
+    print(
+        f"\n✅ Successfully imported {imported_count}/{len(models_data['models'])} models"
+    )
+
+
+if __name__ == "__main__":
+    if len(sys.argv) != 2:
+        print("Usage: python batch_import_models.py models_data.json")
+        sys.exit(1)
+
+    batch_import(sys.argv[1])