Update app.py

app.py

@@ -5,71 +5,11 @@ from langchain_core.runnables import RunnableConfig
 from langchain_teddynote.messages import random_uuid
 from langchain_core.messages import BaseMessage, HumanMessage
 from pprint import pprint
+from graph import app
 
 def format_namespace(namespace):
     return namespace[-1].split(":")[0] if len(namespace) > 0 else "root graph"
 
-from langchain_openai import ChatOpenAI
-from langgraph.checkpoint.memory import MemorySaver
-from langgraph_supervisor import create_supervisor
-from langgraph.prebuilt import create_react_agent
-from langgraph.checkpoint.memory import MemorySaver, InMemorySaver
-from langgraph.store.memory import InMemoryStore
-
-checkpointer = InMemorySaver()
-store = InMemoryStore()
-
-model = ChatOpenAI(model="gpt-4o")
-
-# Create specialized agents
-
-def add(a: float, b: float) -> float:
-    """Add two numbers."""
-    return a + b
-
-def multiply(a: float, b: float) -> float:
-    """Multiply two numbers."""
-    return a * b
-
-def web_search(query: str) -> str:
-    """Search the web for information."""
-    return (
-        "Here are the headcounts for each of the FAANG companies in 2024:\n"
-        "1. **Facebook (Meta)**: 67,317 employees.\n"
-        "2. **Apple**: 164,000 employees.\n"
-        "3. **Amazon**: 1,551,000 employees.\n"
-        "4. **Netflix**: 14,000 employees.\n"
-        "5. **Google (Alphabet)**: 181,269 employees."
-    )
-
-math_agent = create_react_agent(
-    model=model,
-    tools=[add, multiply],
-    name="math_expert",
-    prompt="You are a math expert. Always use one tool at a time."
-)
-
-research_agent = create_react_agent(
-    model=model,
-    tools=[web_search],
-    name="research_expert",
-    prompt="You are a world class researcher with access to web search. Do not do any math."
-)
-
-# Create supervisor workflow
-workflow = create_supervisor(
-    [research_agent, math_agent],
-    model=model,
-    prompt=(
-        "You are a team supervisor managing a research expert and a math expert. "
-        "For current events, use research_agent. "
-        "For math problems, use math_agent."
-    )
-)
-
-# Compile and run
-app = workflow.compile()
-
 def generate_response(message, history):
     inputs = {
             "messages": [HumanMessage(content=message)],