import os

# Import necessary modules and classes
from langchain_arcade import ArcadeToolManager
from langchain_core.messages import HumanMessage
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

"""
Example showing how to use pre-auth'd tokens for tools
this will not wait for the user to authorize the tool
if the tool is not authorized, it will return an error

to have the user authorize the tool, you can see the
example in langgraph_with_user_auth.py
"""


arcade_api_key = os.environ["ARCADE_API_KEY"]
openai_api_key = os.environ["OPENAI_API_KEY"]

# Initialize the tool manager that fetches
# tools from arcade and wraps them as langgraph tools
tool_manager = ArcadeToolManager(api_key=arcade_api_key)
tools = tool_manager.get_tools()

# Create an instance of the AI language model
model = ChatOpenAI(model="gpt-4o", api_key=openai_api_key)

# Init a prebuilt agent that can use tools
# in a REACT style langgraph
graph = create_react_agent(model, tools=tools)

# Define the initial input message from the user
inputs = {
    "messages": [HumanMessage(content="Check and see if I have any important emails in my inbox")],
}

# Configuration parameters for the agent and tools
config = {
    "configurable": {
        "thread_id": "2",
        "user_id": "user@example.com",
    }
}

# Stream the assistant's responses by executing the graph
for chunk in graph.stream(inputs, stream_mode="values", config=config):
    # Access the latest message from the conversation
    last_message = chunk["messages"][-1]
    # Print the assistant's message content
    if last_message.content:
        print(last_message.content)