Update app.py

app.py

@@ -1,33 +1,74 @@
+import os
 import gradio as gr
-import random
-from smolagents import GradioUI, CodeAgent, HfApiModel
 
-# Import our custom tools from their modules
-from tools import DuckDuckGoSearchTool, WeatherInfoTool, HubStatsTool
-from retriever import load_guest_dataset
+from typing import TypedDict, Annotated
+from langgraph.graph.message import add_messages
+from langchain_core.messages import AnyMessage, AIMessage, HumanMessage
+from langgraph.prebuilt import ToolNode
+from langgraph.graph import START, StateGraph
+from langgraph.prebuilt import tools_condition
+from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
 
-# Initialize the Hugging Face model
-model = HfApiModel()
+from tools import DuckDuckGoSearchRun, weather_info_tool, hub_stats_tool
+from retriever import guest_info_tool
 
-# Initialize the web search tool
-search_tool = DuckDuckGoSearchTool()
+hf_token = os.getenv("HF_TOKEN")
 
-# Initialize the weather tool
-weather_info_tool = WeatherInfoTool()
+# 初始化网络搜索工具
+search_tool = DuckDuckGoSearchRun()
 
-# Initialize the Hub stats tool
-hub_stats_tool = HubStatsTool()
+# 生成包含工具的聊天接口
+llm = HuggingFaceEndpoint(
+    repo_id="Qwen/Qwen2.5-Coder-32B-Instruct",
+    huggingfacehub_api_token=hf_token,
+)
+
+chat = ChatHuggingFace(llm=llm, verbose=True)
+tools = [guest_info_tool, search_tool, weather_info_tool, hub_stats_tool]
+chat_with_tools = chat.bind_tools(tools)
+
+# 生成 AgentState 和 Agent 图
+class AgentState(TypedDict):
+    messages: Annotated[list[AnyMessage], add_messages]
 
-# Load the guest dataset and initialize the guest info tool
-guest_info_tool = load_guest_dataset()
+def assistant(state: AgentState):
+    return {
+        "messages": [chat_with_tools.invoke(state["messages"])],
+    }
 
-# Create Alfred with all the tools
-alfred = CodeAgent(
-    tools=[guest_info_tool, weather_info_tool, hub_stats_tool, search_tool], 
-    model=model,
-    add_base_tools=True,  # Add any additional base tools
-    planning_interval=3   # Enable planning every 3 steps
+##  构建流程图
+builder = StateGraph(AgentState)
+
+# 定义节点：执行具体工作
+builder.add_node("assistant", assistant)
+builder.add_node("tools", ToolNode(tools))
+
+# 定义边：控制流程走向
+builder.add_edge(START, "assistant")
+builder.add_conditional_edges(
+    "assistant",
+    # 如果最新消息需要工具调用，则路由到 tools 节点
+    # 否则直接响应
+    tools_condition,
 )
+builder.add_edge("tools", "assistant")
+alfred = builder.compile()
+
+def predict(message, history):
+    history_langchain_format = []
+    for msg in history:
+        if msg['role'] == "user":
+            history_langchain_format.append(HumanMessage(content=msg['content']))
+        elif msg['role'] == "assistant":
+            history_langchain_format.append(AIMessage(content=msg['content']))
+    history_langchain_format.append(HumanMessage(content=message))
+    gpt_response = alfred.invoke({"messages": history_langchain_format})
+    return gpt_response["messages"][-1].content
 
 if __name__ == "__main__":
-    GradioUI(alfred).launch()
+    demo = gr.ChatInterface(
+        predict,
+        type="messages"
+    )
+    
+    demo.launch()