app.py

import os
import streamlit as st
from dotenv import load_dotenv
from langchain_core.messages import HumanMessage
from langchain_core.tools import tool
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

load_dotenv()
api_key = os.getenv("OPENROUTER_API_KEY")

@tool
def calculator(a: float, b: float) -> str:
    """Useful for calculating the sum of two numbers (a + b)."""
    return f"The sum of {a} and {b} is {a + b}"

@tool
def say_hello(name: str) -> str:
    """Useful for greeting a user"""
    return f"Hello {name}, I hope you are well today"

model = ChatOpenAI(
    model="mistralai/mistral-7b-instruct:free",
    api_key=api_key,
    base_url="https://openrouter.ai/api/v1",
    temperature=0,
)

tools = [calculator, say_hello]
agent_executor = create_react_agent(model, tools)


# Streamlit UI
#st.set_page_config(page_title="Tool-Using AI Assistant", page_icon="🤖")
#st.title("🧠 Mistral Agent with LangGraph")
st.title("🤖 Mistral Agent with LangGraph")

st.markdown("""
This AI assistant can use custom tools!

### 🛠 Available Tools:
- `calculator(a, b)` – Calculates the sum of two numbers.
  - Example: "What is the sum of 12 and 30?"
- `say_hello(name)` – Greets the person by name.
  - Example: "Say hello to Alex."

You can ask the assistant naturally — it will decide when to use a tool.
""")

user_input = st.text_input("Ask me anything (type 'quit' to exit):")

if user_input and user_input.lower() != "quit":
    with st.spinner("Thinking..."):
        chunks = []
        for chunk in agent_executor.stream({"messages": [HumanMessage(content=user_input)]}):
            if "agent" in chunk and "messages" in chunk["agent"]:
                for message in chunk["agent"]["messages"]:
                    chunks.append(message.content)
        st.success(" ".join(chunks))