update codes

app.py

@@ -10,45 +10,32 @@ from smolagents import CodeAgent, LiteLLMModel, DuckDuckGoSearchTool, VisitWebpa
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- RPM Guard: 프리 티어 버스트 방어형 커스텀 모델 어댑터 ---
-class PacedGeminiModel(LiteLLMModel):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.last_call_time = 0.0
-        self.min_interval = 10  # 15 RPM 제한을 안전하게 회피하기 위한 최소 초 단위 간격 고정
-
-    def __call__(self, *args, **kwargs):
-        # 마지막 API 호출 시점으로부터 경과한 시간을 계산하여 정밀 페이싱 락(Lock) 가동
-        now = time.time()
-        elapsed = now - self.last_call_time
-        if elapsed < self.min_interval:
-            sleep_needed = self.min_interval - elapsed
-            time.sleep(sleep_needed)
-            
-        try:
-            response = super().__call__(*args, **kwargs)
-            return response
-        finally:
-            # 실패하든 성공하든 타임스탬프를 갱신하여 연속 리트라이로 인한 버스트 폭증 차단
-            self.last_call_time = time.time()
-
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
     def __init__(self):
         gemini_key = os.getenv("GEMINI_API_KEY") or os.getenv("GOOGLE_API_KEY")
         
-        # 기본 LiteLLMModel 대신 정밀 감속기가 내장된 PacedGeminiModel로 바인딩
-        self.model = PacedGeminiModel(
+        # 1. 기존에 정상 구동이 확인된 검증된 모델 식별자로 원상 복구
+        base_model = LiteLLMModel(
             model_id="gemini/gemini-2.5-flash",
-            api_key=gemini_key,
-            requests_per_minute=6
+            api_key=gemini_key
         )
+        
+        # 2. 라이브러리 충돌을 막기 위해 모델 호출 자체를 인터셉트하여 10초 페이싱(Pacing) 강제 주입
+        original_call = base_model.__call__
+        def paced_call(*args, **kwargs):
+            print("⏳ [RPM Guard] Free tier burst protection sleep (10s)...")
+            time.sleep(10.0)
+            return original_call(*args, **kwargs)
+        
+        base_model.__call__ = paced_call
+        self.model = base_model
                 
         self.search_tool = DuckDuckGoSearchTool()
         self.visit_tool = VisitWebpageTool()
                 
-        # 계획 주기 가동 및 툴 가방 바인딩
+        # 45점 베이스라인 아키텍처 규격 고정
         self.alfred = CodeAgent(
             tools=[self.search_tool, self.visit_tool], 
             model=self.model,
@@ -58,7 +45,7 @@ class BasicAgent:
 
     def __call__(self, question: str) -> str:
         try:
-            # 연속된 문제 풀이 사이에도 프리 티어 게이트웨이가 휴식할 수 있도록 5초 전처리 대기
+            # 문제와 문제 사이의 완전한 세션 초기화를 위해 5초 추가 대기
             time.sleep(5.0)
             result = self.alfred.run(question)
             if result is None:
@@ -73,8 +60,8 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     Fetches all questions, runs the BasicAgent on them, submits all answers,
     and displays the results.
     """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
+    space_id = os.getenv("SPACE_ID") 
+
     if profile:
         username= f"{profile.username}"
         print(f"User logged in: {username}")
@@ -86,17 +73,15 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
-    # 1. Instantiate Agent ( modify this part to create your agent)
     try:
         agent = BasicAgent()
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
-    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
+        
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
     print(agent_code)
 
-    # 2. Fetch Questions
     print(f"Fetching questions from: {questions_url}")
     try:
         response = requests.get(questions_url, timeout=15)
@@ -117,12 +102,10 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         print(f"An unexpected error occurred fetching questions: {e}")
         return f"An unexpected error occurred fetching questions: {e}", None
 
-    # 3. Run your Agent
     results_log = []
     answers_payload = []
     print(f"Running agent on {len(questions_data)} questions...")
     for item in questions_data:
-        time.sleep(5.0)
         task_id = item.get("task_id")
         question_text = item.get("question")
         if not task_id or question_text is None:
@@ -140,12 +123,10 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         print("Agent did not produce any answers to submit.")
         return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
-    # 4. Prepare Submission 
     submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
     status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
     print(status_update)
 
-    # 5. Submit
     print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
     try:
         response = requests.post(submit_url, json=submission_data, timeout=60)