First Chatbot Project - Build a Working Chatbot

import streamlit as st
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, AIMessage
from langchain_core.tools import tool
from langgraph.prebuilt import create_react_agent

# --- Tools ---

@tool
def web_search(query: str) -> str:
    """Search the web for information"""
    from duckduckgo_search import DDGS
    with DDGS() as ddgs:
        results = list(ddgs.text(query, max_results=3))
    return "\n".join([r["body"] for r in results])

@tool
def calculate(expression: str) -> str:
    """Calculate a math expression"""
    return str(eval(expression))

# --- Page Config ---

st.set_page_config(page_title="My AI Chatbot", page_icon="🤖")
st.title("🤖 My First AI Chatbot")

# --- Session State ---

if "messages" not in st.session_state:
    st.session_state.messages = []

# --- Display Old Messages ---

for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

# --- Get User Input ---

if prompt := st.chat_input("Ask me anything"):
    
    st.session_state.messages.append({"role": "user", "content": prompt})
    with st.chat_message("user"):
        st.markdown(prompt)
    
    if "agent" not in st.session_state:
        llm = ChatOpenAI(model="gpt-4o", temperature=0)
        st.session_state.agent = create_react_agent(
            model=llm,
            tools=[web_search, calculate],
        )
    
    with st.chat_message("assistant"):
        with st.spinner("Thinking..."):
            history = []
            for msg in st.session_state.messages[:-1]:
                if msg["role"] == "user":
                    history.append(HumanMessage(content=msg["content"]))
                else:
                    history.append(AIMessage(content=msg["content"]))
            history.append(HumanMessage(content=prompt))
            
            result = st.session_state.agent.invoke({"messages": history})
            response = result["messages"][-1].content
            st.markdown(response)
    
    st.session_state.messages.append({"role": "assistant", "content": response})

@tool
def get_stock_price(symbol: str) -> str:
    """Get current stock price"""
    return f"{symbol} is trading at $150.25"

agent = create_react_agent(
    model=llm,
    tools=[web_search, calculate, get_stock_price],
)