diff --git a/webui.py b/webui.py
index 18a6a3e..f041da6 100644
--- a/webui.py
+++ b/webui.py
@@ -31,9 +31,12 @@ def upload_file(file):
 
 
 def get_answer(query, vs_path, history):
-    resp, history = local_doc_qa.get_knowledge_based_answer(
-        query=query, vs_path=vs_path, chat_history=history)
-    return history, history
+    if vs_path:
+        resp, history = local_doc_qa.get_knowledge_based_answer(
+            query=query, vs_path=vs_path, chat_history=history)
+    else:
+        history = history + [[None, "请先加载文件后，再进行提问。"]]
+    return history
 
 
 def update_status(history, status):
@@ -50,26 +53,30 @@ def init_model():
         return """模型未成功加载，请重新选择后点击"加载模型"按钮"""
 
 
-def reinit_model(llm_model, embedding_model, llm_history_len, top_k):
+def reinit_model(llm_model, embedding_model, llm_history_len, top_k, history):
     try:
         local_doc_qa.init_cfg(llm_model=llm_model,
                               embedding_model=embedding_model,
                               llm_history_len=llm_history_len,
                               top_k=top_k)
-        return """模型已成功重新加载，请选择文件后点击"加载文件"按钮"""
+        model_status = """模型已成功重新加载，请选择文件后点击"加载文件"按钮"""
     except:
-        return """模型未成功重新加载，请重新选择后点击"加载模型"按钮"""
+        model_status = """模型未成功重新加载，请重新选择后点击"加载模型"按钮"""
+    return history + [[None, model_status]]
 
 
 
-def get_vector_store(filepath):
-    vs_path = local_doc_qa.init_knowledge_vector_store(["content/" + filepath])
-    if vs_path:
-        file_status = "文件已成功加载，请开始提问"
+def get_vector_store(filepath, history):
+    if local_doc_qa.llm and local_doc_qa.llm:
+        vs_path = local_doc_qa.init_knowledge_vector_store(["content/" + filepath])
+        if vs_path:
+            file_status = "文件已成功加载，请开始提问"
+        else:
+            file_status = "文件未成功加载，请重新上传文件"
     else:
-        file_status = "文件未成功加载，请重新上传文件"
-    print(file_status)
-    return vs_path, file_status
+        file_status = "模型未完成加载，请先在加载模型后再导入文件"
+        vs_path = None
+    return vs_path, history + [[None, file_status]]
 
 
 block_css = """.importantButton {
@@ -98,7 +105,7 @@ init_message = """欢迎使用 langchain-ChatGLM Web UI，开始提问前，请
 model_status = init_model()
 
 with gr.Blocks(css=block_css) as demo:
-    vs_path, history, file_status, model_status = gr.State(""), gr.State([]), gr.State(""), gr.State(model_status)
+    vs_path, file_status, model_status = gr.State(""), gr.State(""), gr.State(model_status)
     gr.Markdown(webui_title)
     with gr.Row():
         with gr.Column(scale=2):
@@ -106,9 +113,9 @@ with gr.Blocks(css=block_css) as demo:
                                  elem_id="chat-box",
                                  show_label=False).style(height=750)
             query = gr.Textbox(show_label=False,
-                               placeholder="请提问",
-                               lines=1,
-                               value="用200字总结一下"
+                               placeholder="请输入提问内容，按回车进行提交",
+                               # lines=1,
+                               # value="用200字总结一下"
                                ).style(container=False)
 
         with gr.Column(scale=1):
@@ -144,26 +151,24 @@ with gr.Blocks(css=block_css) as demo:
                 file = gr.File(label="content file",
                                file_types=['.txt', '.md', '.docx', '.pdf']
                                )  # .style(height=100)
-            load_file_button = gr.Button("重新加载文件")
+            load_file_button = gr.Button("加载文件")
     load_model_button.click(reinit_model,
                             show_progress=True,
-                            inputs=[llm_model, embedding_model, llm_history_len, top_k],
-                            outputs=model_status
-                            ).then(update_status, [chatbot, model_status], chatbot)
+                            inputs=[llm_model, embedding_model, llm_history_len, top_k, chatbot],
+                            outputs=chatbot
+                            )
     # 将上传的文件保存到content文件夹下,并更新下拉框
     file.upload(upload_file,
                 inputs=file,
                 outputs=selectFile)
     load_file_button.click(get_vector_store,
                            show_progress=True,
-                           inputs=selectFile,
-                           outputs=[vs_path, file_status],
-                           ).then(
-        update_status, [chatbot, file_status], chatbot
-    )
+                           inputs=[selectFile, chatbot],
+                           outputs=[vs_path, chatbot],
+                           )
     query.submit(get_answer,
                  [query, vs_path, chatbot],
-                 [chatbot, history],
+                 [chatbot],
                  )
 
 demo.queue(concurrency_count=3).launch(