Refactor authentication and session management

- Removed old llm_login and session scripts, replacing them with a new structure for handling SSO login and session management.
- Introduced a new prompt system for collecting SSO redirect URLs, ensuring compliance with security protocols.
- Implemented a robust backend notification system for tracking scan initiation.
- Enhanced browser profile configuration and resource management for improved session handling.
- Added utility functions for environment variable checks and logging.
- Updated the overall architecture to improve maintainability and readability.

lib/llm/__init__.py

@@ -0,0 +1,25 @@
+from langchain.callbacks.base import BaseCallbackHandler
+from langchain_google_genai import ChatGoogleGenerativeAI
+
+class QuotaExhaustedHandler(BaseCallbackHandler):
+    def on_llm_error(self, error, **kwargs):
+        if "ResourceExhausted" in str(error) or "429" in str(error):
+            print("⚠️ API 쿼터가 소진되었습니다. 재시도 로직에 위임합니다...")
+            # backoff handled in scan_one_url
+
+def CreateChatGoogleGenerativeAI(model: str):
+    """재시도 로직이 포함된 LLM 생성"""
+    if model == "fallback":
+        print("⚠️ Fallback 모델을 사용합니다. Envorinment 변수를 확인하세요.")
+        print("⚠️ Model Gemini-2.0-flash-lite를 사용합니다.")
+        model = "gemini-2.0-flash-lite"
+    return ChatGoogleGenerativeAI(
+        model=model,
+        max_retries=10,  # 최대 재시도 횟수 증가
+        model_kwargs={
+            "request_timeout": 120,  # 타임아웃 시간 증가 (2분)
+        },
+        callbacks=[QuotaExhaustedHandler()],
+        # API 호출 간격 조정
+        temperature=0.1,
+    )