Update root inference.py to match pre-submission format

inference.py

@@ -1,5 +1,23 @@
 import os
 import sys
+from typing import Any, Dict, List, Optional
+
+from openai import OpenAI
+
+
+# Required submission variables.
+# Defaults are provided only for API_BASE_URL and MODEL_NAME.
+API_BASE_URL = os.getenv("API_BASE_URL", "https://router.huggingface.co/v1")
+MODEL_NAME = os.getenv("MODEL_NAME", "Qwen/Qwen2.5-72B-Instruct")
+HF_TOKEN = os.getenv("HF_TOKEN")
+LOCAL_IMAGE_NAME = os.getenv("LOCAL_IMAGE_NAME")
+
+
+# Compatibility fallback. HF_TOKEN remains the primary required variable.
+API_KEY = HF_TOKEN or os.getenv("API_KEY") or os.getenv("OPENAI_API_KEY")
+
+TASK_NAME = os.getenv("TASK", "easy")
+BENCHMARK = os.getenv("BENCHMARK", "codeguard")
 
 
 def _bootstrap_path() -> None:
@@ -9,11 +27,90 @@ def _bootstrap_path() -> None:
         sys.path.insert(0, agentbox_root)
 
 
+def _fmt_bool(value: bool) -> str:
+    return "true" if value else "false"
+
+
+def _fmt_error(error: Optional[str]) -> str:
+    return "null" if error is None else str(error)
+
+
+def _clamp_score(value: float) -> float:
+    return max(0.0, min(1.0, value))
+
+
 def main() -> None:
     _bootstrap_path()
-    from inference import main as agentbox_main
+    from src.env import CodeGuardEnv
+
+    rewards: List[float] = []
+    steps: int = 0
+    score: float = 0.0
+    success: bool = False
+
+    print(f"[START] task={TASK_NAME} env={BENCHMARK} model={MODEL_NAME}")
+
+    env = None
+    try:
+        client = None
+        init_error: Optional[str] = None
+        if API_KEY:
+            try:
+                client = OpenAI(base_url=API_BASE_URL, api_key=API_KEY)
+            except Exception as exc:
+                init_error = str(exc)
+        else:
+            init_error = "Missing HF_TOKEN (or API_KEY)"
+
+        env = CodeGuardEnv()
+        state: Dict[str, Any] = env.reset()
+        done = False
+
+        while not done:
+            steps += 1
+
+            model_error: Optional[str] = None
+            if client is None:
+                action = ""
+                model_error = init_error
+            else:
+                try:
+                    response = client.chat.completions.create(
+                        model=MODEL_NAME,
+                        messages=[
+                            {"role": "system", "content": "You are a code-fixing agent."},
+                            {"role": "user", "content": str(state)},
+                        ],
+                        temperature=0.0,
+                    )
+                    action = (response.choices[0].message.content or "").strip()
+                except Exception as exc:
+                    action = ""
+                    model_error = str(exc)
+
+            next_state, reward, done, info = env.step(action)
+            rewards.append(float(reward))
+
+            step_error = model_error or info.get("error")
+            print(
+                f"[STEP] step={steps} action={action} reward={float(reward):.2f} "
+                f"done={_fmt_bool(done)} error={_fmt_error(step_error)}"
+            )
+
+            state = next_state
+            score = _clamp_score(float(state.get("score", 0.0)))
+            if done and float(reward) >= env.threshold:
+                success = True
 
-    agentbox_main()
+    except Exception:
+        success = False
+        score = 0.0
+    finally:
+        rewards_str = ",".join(f"{r:.2f}" for r in rewards) if rewards else "0.00"
+        print(
+            f"[END] success={_fmt_bool(success)} steps={steps} "
+            f"score={score:.2f} rewards={rewards_str}"
+        )
 
 
 if __name__ == "__main__":