Upload example_submission.py

example_submission.py

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+"""
+WM Bench — Example Submission Script
+=====================================
+Any world model can participate in WM Bench using this template.
+No 3D environment needed — text input/output only.
+
+Usage:
+  python example_submission.py --api_url YOUR_MODEL_API --api_key YOUR_KEY --model YOUR_MODEL_NAME
+"""
+
+import json
+import argparse
+import requests
+import time
+from pathlib import Path
+
+# ── 설정 ────────────────────────────────────────────────────────
+DATASET_PATH = Path(__file__).parent.parent / "data" / "wm_bench_dataset.json"
+
+SYSTEM_PROMPT = """You are a world model. Given scene_context as JSON, respond in exactly 2 lines:
+Line 1: PREDICT: left=<safe|danger>(<reason>), right=<safe|danger>(<reason>), fwd=<safe|danger>(<reason>), back=<safe|danger>(<reason>)
+Line 2: MOTION: <describe the character's physical motion and emotional state in one sentence>
+Respond ONLY these 2 lines. No explanation."""
+
+# ── 메인 평가 함수 ───────────────────────────────────────────────
+def run_evaluation(api_url: str, api_key: str, model: str, output_path: str = "my_submission.json"):
+    """
+    WM Bench 평가를 실행하고 제출 파일을 생성합니다.
+    
+    Parameters:
+        api_url: OpenAI 호환 API URL (예: https://api.openai.com/v1/chat/completions)
+        api_key: API 키
+        model: 모델 이름
+        output_path: 제출 파일 경로
+    """
+    # 데이터셋 로드
+    with open(DATASET_PATH, "r", encoding="utf-8") as f:
+        dataset = json.load(f)
+    
+    scenarios = dataset["scenarios"]
+    print(f"✅ 데이터셋 로드: {len(scenarios)}개 시나리오")
+    print(f"🤖 모델: {model}")
+    print(f"🔗 API: {api_url}\n")
+
+    results = []
+    errors = 0
+
+    for i, scenario in enumerate(scenarios):
+        sc_id = scenario["id"]
+        cat = scenario["cat"]
+        scene = scenario["scene_context"]
+
+        # API 호출
+        t0 = time.time()
+        response_text, latency_ms = call_api(api_url, api_key, model, scene)
+        
+        if response_text is None:
+            errors += 1
+            print(f"  ❌ {sc_id} ({cat}): API 오류")
+            results.append({
+                "id": sc_id,
+                "cat": cat,
+                "response": None,
+                "latency_ms": latency_ms,
+                "error": True
+            })
+        else:
+            results.append({
+                "id": sc_id,
+                "cat": cat,
+                "response": response_text,
+                "latency_ms": round(latency_ms, 1),
+                "error": False
+            })
+            
+            if (i + 1) % 10 == 0:
+                print(f"  ✓ {i+1}/100 완료 ({cat})")
+
+    # 제출 파일 생성
+    submission = {
+        "model": model,
+        "api_url": api_url,
+        "track": "A",  # Text-Only Track
+        "submitted_at": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
+        "total_scenarios": len(scenarios),
+        "errors": errors,
+        "results": results
+    }
+
+    with open(output_path, "w", encoding="utf-8") as f:
+        json.dump(submission, f, ensure_ascii=False, indent=2)
+
+    print(f"\n✅ 제출 파일 생성: {output_path}")
+    print(f"   총 시나리오: {len(scenarios)}, 오류: {errors}")
+    print(f"\n📤 다음 단계: WM Bench Space에 제출 파일 업로드")
+    print(f"   https://huggingface.co/spaces/FINAL-Bench/worldmodel-bench")
+    return output_path
+
+
+def call_api(api_url: str, api_key: str, model: str, scene_context: dict):
+    """OpenAI 호환 API 호출"""
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {api_key}"
+    }
+    payload = {
+        "model": model,
+        "max_tokens": 200,
+        "temperature": 0.0,
+        "messages": [
+            {"role": "system", "content": SYSTEM_PROMPT},
+            {"role": "user", "content": f"scene_context: {json.dumps(scene_context)}"}
+        ]
+    }
+    t0 = time.time()
+    try:
+        r = requests.post(api_url, headers=headers, json=payload, timeout=30)
+        r.raise_for_status()
+        text = r.json()["choices"][0]["message"]["content"]
+        return text, (time.time() - t0) * 1000
+    except Exception as e:
+        return None, (time.time() - t0) * 1000
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="WM Bench Submission Script")
+    parser.add_argument("--api_url", required=True, help="OpenAI-compatible API URL")
+    parser.add_argument("--api_key", required=True, help="API Key")
+    parser.add_argument("--model", required=True, help="Model name")
+    parser.add_argument("--output", default="my_submission.json", help="Output file path")
+    args = parser.parse_args()
+    
+    run_evaluation(args.api_url, args.api_key, args.model, args.output)