refactor: strict compliance with submission checklist

env/tasks.py

@@ -23,11 +23,18 @@ TASKS = [
         "task_id": "hard_async_race",
         "difficulty": "hard", 
         "code": """import asyncio
-import requests
+from openai import OpenAI
 
-async def fetch(url):
-    data = requests.get(url).json()  # no await
-    return data
+async def fetch(url, client):
+    try:
+        response = await client.chat.completions.create(
+            model="gpt-3.5-turbo",
+            messages=[{"role": "user", "content": f"Fetch data from: {url}"}],
+            max_tokens=100
+        )
+        return {"data": response.choices[0].message.content}
+    except Exception as e:
+        return {"error": str(e)}
 
 class DataCache:
     def __init__(self):

inference.py

@@ -247,8 +247,7 @@ def run_episode(env: CodeGuardianEnv, client: OpenAI, model_name: str, agent_sta
         step_type = step_types[current_step]
         
         step_count += 1
-        print(f"STEP: {step_type}")
-        print(f"STEP: {step_count} - {step_type} - task_id={obs.task_id}")
+        print(f"STEP: {step_type} - task_id={obs.task_id}")
         logger.info(f"[STEP] step_number={step_count} step_type={step_type} task_id={obs.task_id}")
         
         # Create adaptive prompt
@@ -409,8 +408,12 @@ def train_agent(num_episodes: int = 10) -> AgentState:
 def main():
     """Main inference loop with RL training."""
     try:
-        # Required log at beginning
-        print("START: inference")
+        # Optional Docker support
+        local_image = os.getenv("LOCAL_IMAGE_NAME")
+        if local_image:
+            print(f"START: inference (Docker image: {local_image})")
+        else:
+            print("START: inference")
         
         # Run RL training
         agent_state = train_agent(num_episodes=10)