correct agent

app.py

@@ -1,22 +1,16 @@
 import gradio as gr
 import langgraph as lg
-from typing import TypedDict, Annotated
+from typing import TypedDict, Annotated, Sequence
 from huggingface_hub import InferenceClient, login, list_models
 from langgraph.prebuilt import ToolNode, tools_condition
 from langgraph.graph.message import add_messages
-from langgraph.tools import Tool
-from langgraph.retrievers import BM25Retriever
-from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFacePipeline
-from langgraph.graph.message import add_messages
-from langgraph.prebuilt import ToolNode, tools_condition
-from langgraph.graph import START, StateGraph
-from langgraph.tools.dsl import TextDocument
-from langgraph.tools.dsl.query import Query
-from langgraph.tools.dsl.answer import Answer
-from langgraph.tools.dsl.answer_format import TextAnswer
+from langchain.tools import Tool  # Updated import
+from langchain_core.messages import HumanMessage, AIMessage, BaseMessage  # Added message types
+from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
+from langgraph.graph import START,END, StateGraph
 import os
-from langgraph.graph import START, StateGraph
-from langchain.tools import Tool
+
+
 """
 For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
 """
@@ -57,13 +51,12 @@ def get_hub_stats(author: str) -> str:
         return f"Error fetching models for {author}: {str(e)}"
 
 # Initialize the tool
-hub_stats_tool = Tool(
-    name="get_hub_stats",
+hub_stats_tool = Tool.from_function(  # Use proper Tool initialization
     func=get_hub_stats,
-    description="Fetches the most downloaded model from a specific author on the Hugging Face Hub."
+    name="get_hub_stats",
+    description="Fetches popular models from Hugging Face Hub"
 )
 
-
 def predict(message, history):
     # Convert Gradio history to LangChain message format
     history_langchain_format = []
@@ -92,7 +85,7 @@ chat_with_tools = model.bind_tools(tools)
 
 # Generate the AgentState and Agent graph
 class AgentState(TypedDict):
-    messages: Annotated[list[AnyMessage], add_messages]
+    messages: Annotated[list[BaseMessage], add_messages] 
 
 def assistant(state: AgentState):
     return {
@@ -113,6 +106,7 @@ builder.add_conditional_edges(
     # If the latest message requires a tool, route to tools
     # Otherwise, provide a direct response
     tools_condition,
+    {"tools": "tools", "end": END}
 )
 builder.add_edge("tools", "assistant")
 alfred = builder.compile()