Update app.py

app.py

@@ -1327,43 +1327,45 @@ def build_langgraph_gpt_like():
     return graph.compile()
 
 # 用於 Gradio Tab 6 的主入口函數
-def langgraph_tab6_main(query: str, files=None) -> str:
+def langgraph_tab6_main(query: str, file=None) -> str:
     try:
-        if not files:
-            return "Please upload at least one document file."
+        if not file:
+            # fallback to General Agent only when no file
+            result = general_agent.kickoff(inputs={"query": query})
+            return result.output
 
+        # ⬇️ 保證 file 是 list（支援多檔）
+        files = file if isinstance(file, list) else [file]
         all_docs = []
         file_names = []
 
-        for file in files:
-            file_path = get_file_path(file)
-            if not file_path:
+        for f in files:
+            path = get_file_path(f)
+            if not path:
                 continue
-            file_names.append(os.path.basename(file_path))
-            if file_path.lower().endswith(".pdf"):
-                loader = PyPDFLoader(file_path)
-            elif file_path.lower().endswith(".docx"):
-                loader = UnstructuredWordDocumentLoader(file_path)
+            file_names.append(os.path.basename(path))
+
+            if path.lower().endswith(".pdf"):
+                loader = PyPDFLoader(path)
+            elif path.lower().endswith(".docx"):
+                loader = UnstructuredWordDocumentLoader(path)
             else:
-                loader = TextLoader(file_path)
+                loader = TextLoader(path)
+
             docs = loader.load()
             all_docs.extend(docs)
 
         if not all_docs:
-            return "No valid documents loaded."
+            return "Uploaded files are empty or unreadable."
 
         chunks = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50).split_documents(all_docs)
         db = FAISS.from_documents(chunks, embeddings)
         retriever = db.as_retriever()
 
-        # 建構 LangGraph QA Chain
+        # 🧠 建構 LangGraph QA Chain
         graph = build_langgraph_doc_qa_chain(llm=llm_gpt4, retriever=retriever, memory=None, prompt=custom_prompt)
-        result = graph({
-            "query": query,
-            "file_names": file_names  # Pass all file names for intent detection
-        })
+        result = graph(query)
         return result.get("answer", "No answer generated.")
-
     except Exception as e:
         return f"[Tab6 Error] {e}"