Update app.py

app.py

@@ -5,12 +5,10 @@ import pandas as pd
 from typing import TypedDict, Annotated, Sequence
 import operator
 from langchain_core.messages import BaseMessage, HumanMessage
-from langchain.agents import AgentExecutor
 from langchain_community.tools import DuckDuckGoSearchRun
-from langchain_huggingface import HuggingFaceEndpoint
+from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
 from langgraph.graph import StateGraph, END
 from langgraph.prebuilt import ToolNode, tools_condition
-from langchain_core.prompts import ChatPromptTemplate
 
 # --- Main Application Logic ---
 
@@ -22,30 +20,31 @@ class AgentState(TypedDict):
 def create_langgraph_agent():
     print("Initializing LangGraph Agent...")
     
-    # 1. Set up the LLM (The "Brain") using the DeepSeek Coder model
-    llm = HuggingFaceEndpoint(
+    # 1. Set up the LLM Endpoint connection
+    llm_endpoint = HuggingFaceEndpoint(
         repo_id="deepseek-ai/deepseek-coder-6.7b-instruct",
         task="conversational",
-        max_new_tokens=1024, # Increased tokens for better reasoning
+        max_new_tokens=1024,
         do_sample=False,
     )
     
-    # We use a wrapper to make the model compatible with LangChain's tool calling
-    from langchain_huggingface.chat_models import HuggingFaceChat
-    llm_with_tools = HuggingFaceChat(endpoint=llm).bind_tools([DuckDuckGoSearchRun()])
+    # 2. Wrap the endpoint in the ChatHuggingFace class to make it a chat model
+    # and bind the tools to it.
+    tools = [DuckDuckGoSearchRun()]
+    chat_model = ChatHuggingFace(llm=llm_endpoint)
+    chat_model_with_tools = chat_model.bind_tools(tools)
     print("LLM and tools initialized.")
 
-    # 2. Define the agent's logic (the "agent" node)
-    # We define the graph nodes and edges for the agent's reasoning process
+    # 3. Define the agent's logic (the "agent" node)
     def agent_node(state):
         print("Calling agent node...")
-        response = llm_with_tools.invoke(state["messages"])
+        response = chat_model_with_tools.invoke(state["messages"])
         return {"messages": [response]}
     
-    tool_node = ToolNode([DuckDuckGoSearchRun()])
+    tool_node = ToolNode(tools)
     print("Graph nodes defined.")
 
-    # 3. Define the Graph
+    # 4. Define the Graph
     graph = StateGraph(AgentState)
     graph.add_node("agent", agent_node)
     graph.add_node("tools", tool_node)
@@ -54,7 +53,7 @@ def create_langgraph_agent():
     graph.add_conditional_edges("agent", tools_condition)
     graph.add_edge("tools", "agent")
 
-    # 4. Compile the graph into a runnable app
+    # 5. Compile the graph into a runnable app
     app = graph.compile()
     print("LangGraph agent compiled and ready.")
     return app
@@ -120,7 +119,7 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
 
 # Gradio Interface
 with gr.Blocks() as demo:
-    gr.Markdown("# Agent Evaluation Runner (DeepSeek + DuckDuckGo)")
+    gr.Markdown("# Agent Evaluation Runner (DeepSeek + LangGraph)")
     gr.LoginButton()
     run_button = gr.Button("Run Evaluation & Submit All Answers")
     status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)