Update src/streamlit_app.py

src/streamlit_app.py

@@ -34,6 +34,16 @@ st.set_page_config(page_title="Cybersecurity AI Assistant (Hugging Face RAG & Ba
 st.title("🛡️ Meta-Llama-3-8B-Instruct with FAISS RAG & Batch Analysis (Inference Client)")
 st.markdown("已啟用：**IndexFlatIP** + **L2 正規化** + **Hugging Face Inference Client (API)**。上傳 JSON 執行批量分析，上傳其他檔案作為 RAG 參考庫。")
 
+
+if 'execute_batch_analysis' not in st.session_state:
+    st.session_state.execute_batch_analysis = False
+if 'batch_results' not in st.session_state:
+    st.session_state.batch_results = None
+if 'rag_current_file_key' not in st.session_state:
+    st.session_state.rag_current_file_key = None
+if 'json_current_file_key' not in st.session_state:
+    st.session_state.json_current_file_key = None
+    
 # 設定模型 ID (替換為您指定的模型)
 MODEL_ID = "ZySec-AI/SecurityLLM" 
 WINDOW_SIZE = 8
@@ -289,16 +299,7 @@ Based on the provided LOG SEQUENCE and REFERENCE CONTEXT, you must analyze the *
         # 如果模型呼叫失敗，回傳詳細錯誤訊息
         return f"Hugging Face Model Error: {str(e)}", context_text
 
-# === 檔案處理和主執行邏輯 (保持結構，替換 LLM 呼叫) ===
-# 初始化 Session State (保持不變)
-if 'execute_batch_analysis' not in st.session_state:
-    st.session_state.execute_batch_analysis = False
-if 'batch_results' not in st.session_state:
-    st.session_state.batch_results = None
 
-# --- 1. 處理 RAG 知識庫檔案 (rag_uploaded_file) --- (保持不變)
-if 'rag_current_file_key' not in st.session_state:
-    st.session_state.rag_current_file_key = None
     
 if rag_uploaded_file:
     file_key = f"vs_{rag_uploaded_file.name}_{rag_uploaded_file.size}"
@@ -319,9 +320,6 @@ elif 'vector_store' in st.session_state:
     del st.session_state.rag_current_file_key
     st.info("RAG 檔案已移除，已清除相關知識庫。")
 
-# --- 2. 處理 JSON 批量分析檔案 (json_uploaded_file) --- (保持不變)
-if 'json_current_file_key' not in st.session_state:
-    st.session_state.json_current_file_key = None
     
 if json_uploaded_file:
     json_file_key = f"json_{json_uploaded_file.name}_{json_uploaded_file.size}"