Update app.py

app.py

@@ -16,20 +16,27 @@ from datetime import datetime
 # Load environment variables
 load_dotenv()
 
-# Read Gemini API key from environment (.env or platform secrets)
-gemini_api_key = os.getenv("GOOGLE_API_KEY", "").strip()
-if gemini_api_key:
-    os.environ["GOOGLE_API_KEY"] = gemini_api_key
-else:
-    print("警告：未找到 GOOGLE_API_KEY，請在環境變數或 .env 設定。")
+# 延後讀取 API 金鑰：提供工具函式，實際需要時才讀取
+def _get_api_key() -> str:
+    candidate_keys = [
+        "GOOGLE_API_KEY",
+        "GEMINI_API_KEY",
+        "GOOGLE_GENAI_API_KEY",
+        "GENAI_API_KEY",
+    ]
+    for key_name in candidate_keys:
+        value = os.getenv(key_name, "").strip()
+        if value:
+            # 同步一份到 GOOGLE_API_KEY 以相容底層套件
+            os.environ["GOOGLE_API_KEY"] = value
+            return value
+    return ""
 
 class PDFChatBot:
     def __init__(self):
         self.vector_store = None
-        self.embeddings = GoogleGenerativeAIEmbeddings(
-            model="models/text-embedding-004",
-            google_api_key=gemini_api_key
-        )
+        # 嵌入模型延後初始化，直到真的需要（處理或載入向量庫）
+        self.embeddings = None
         self.processed_files = []
         self.chat_history = []  # 儲存聊天歷史
 
@@ -82,6 +89,14 @@ class PDFChatBot:
     def create_vector_store(self, chunks):
         """從文字區塊創建FAISS向量存儲"""
         try:
+            if self.embeddings is None:
+                api_key = _get_api_key()
+                if not api_key:
+                    return False
+                self.embeddings = GoogleGenerativeAIEmbeddings(
+                    model="models/text-embedding-004",
+                    google_api_key=api_key
+                )
             self.vector_store = FAISS.from_texts(chunks, self.embeddings)
             self.vector_store.save_local("faiss_index")
             return True
@@ -93,6 +108,14 @@ class PDFChatBot:
         """載入已存在的向量存儲"""
         try:
             if os.path.exists("faiss_index"):
+                if self.embeddings is None:
+                    api_key = _get_api_key()
+                    if not api_key:
+                        return False
+                    self.embeddings = GoogleGenerativeAIEmbeddings(
+                        model="models/text-embedding-004",
+                        google_api_key=api_key
+                    )
                 self.vector_store = FAISS.load_local(
                     "faiss_index",
                     embeddings=self.embeddings,
@@ -118,10 +141,14 @@ class PDFChatBot:
         回答:
         """
 
-        # Using Flash 2.0 model
+        # Using Flash 2.0 model（延後讀取 API Key）
+        api_key = _get_api_key()
+        if not api_key:
+            raise RuntimeError("尚未設定 API 金鑰，請於部署後設定 GOOGLE_API_KEY 再重試。")
+
         model = ChatGoogleGenerativeAI(
             model="gemini-2.0-flash-exp",
-            google_api_key=gemini_api_key,
+            google_api_key=api_key,
             temperature=temperature,
             max_tokens=max_tokens,
             top_p=0.8,