fix

examples/gradio_test.py

@@ -0,0 +1,19 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+import gradio as gr
+
+def change_tab():
+    return gr.Tabs.update(selected=1)
+
+with gr.Blocks() as demo:
+    with gr.Tabs() as tabs:
+        with gr.TabItem("Train", id=0):
+            t = gr.Textbox()
+        with gr.TabItem("Inference", id=1):
+            i = gr.Image()
+    
+    btn = gr.Button()
+    btn.click(change_tab, None, tabs)
+
+demo.launch()

pilot/configs/model_config.py

@@ -28,8 +28,7 @@ VECTOR_SEARCH_TOP_K = 3
 LLM_MODEL = "vicuna-13b"
 LIMIT_MODEL_CONCURRENCY = 5
 MAX_POSITION_EMBEDDINGS = 4096 
-VICUNA_MODEL_SERVER = "http://192.168.31.114:8000"
+VICUNA_MODEL_SERVER = "http://47.97.125.199:8000"
-
 
 # Load model config
 ISLOAD_8BIT = True

pilot/server/webserver.py

@@ -45,6 +45,7 @@ enable_moderation = False
 models = []
 dbs = []
 vs_list = ["新建知识库"] + get_vector_storelist()
+autogpt = False
 
 priority = {
     "vicuna-13b": "aaa"
@@ -58,8 +59,6 @@ def get_simlar(q):
     contents = [dc.page_content for dc, _ in docs]
     return "\n".join(contents)
     
-    
-
 def gen_sqlgen_conversation(dbname):
     mo = MySQLOperator(
         **DB_SETTINGS
@@ -118,6 +117,8 @@ def regenerate(state, request: gr.Request):
     return (state, state.to_gradio_chatbot(), "") + (disable_btn,) * 5
 
 def clear_history(request: gr.Request):
+
+    
     logger.info(f"clear_history. ip: {request.client.host}")
     state = None
     return (state, [], "") + (disable_btn,) * 5
@@ -135,7 +136,7 @@ def add_text(state, text, request: gr.Request):
             return (state, state.to_gradio_chatbot(), moderation_msg) + (
                 no_change_btn,) * 5
 
-    text = text[:1536]  # Hard cut-off
+    text = text[:4000]  # Hard cut-off
     state.append_message(state.roles[0], text)
     state.append_message(state.roles[1], None)
     state.skip_next = False
@@ -152,6 +153,8 @@ def post_process_code(code):
     return code
 
 def http_bot(state, mode, db_selector, temperature, max_new_tokens, request: gr.Request):
+
+    print("是否是AUTO-GPT模式.", autogpt)
     start_tstamp = time.time()
     model_name = LLM_MODEL
 
@@ -163,6 +166,7 @@ def http_bot(state, mode, db_selector, temperature, max_new_tokens, request: gr.
         return
 
 
+    # TODO when tab mode is AUTO_GPT, Prompt need to rebuild.
     if len(state.messages) == state.offset + 2:
         # 第一轮对话需要加入提示Prompt 
         
@@ -264,15 +268,14 @@ pre {
             """
 )
 
-def change_tab(tab):
-    pass
-
 def change_mode(mode):
     if mode in ["默认知识库对话", "LLM原生对话"]:
         return gr.update(visible=False)
     else:
         return gr.update(visible=True)
 
+def change_tab():
+    autogpt = True 
  
 def build_single_model_ui():
    
@@ -309,7 +312,8 @@ def build_single_model_ui():
         )
     tabs= gr.Tabs()
     with tabs:
-        with gr.TabItem("SQL生成与诊断", elem_id="SQL"):
+        tab_sql = gr.TabItem("SQL生成与诊断", elem_id="SQL")
+        with tab_sql:
             # TODO A selector to choose database
             with gr.Row(elem_id="db_selector"):
                 db_selector = gr.Dropdown(
@@ -318,9 +322,12 @@ def build_single_model_ui():
                     value=dbs[0] if len(models) > 0 else "",
                     interactive=True,
                     show_label=True).style(container=False) 
+        tab_auto = gr.TabItem("AUTO-GPT", elem_id="auto")
+        with tab_auto:
+            gr.Markdown("自动执行模式下, DB-GPT可以具备执行SQL、从网络读取知识自动化存储学习的能力")
 
-        with gr.TabItem("知识问答", elem_id="QA"):
+        tab_qa = gr.TabItem("知识问答", elem_id="QA")
-            
+        with tab_qa:
             mode = gr.Radio(["LLM原生对话", "默认知识库对话", "新增知识库对话"], show_label=False, value="LLM原生对话")
             vs_setting = gr.Accordion("配置知识库", open=False)
             mode.change(fn=change_mode, inputs=mode, outputs=vs_setting)
@@ -360,9 +367,7 @@ def build_single_model_ui():
         regenerate_btn = gr.Button(value="重新生成", interactive=False)
         clear_btn = gr.Button(value="清理", interactive=False)
 
-
     gr.Markdown(learn_more_markdown)
-
     btn_list = [regenerate_btn, clear_btn]
     regenerate_btn.click(regenerate, state, [state, chatbot, textbox] + btn_list).then(
         http_bot,