from oci.addons.adk import Agent, AgentClient
from compartments import Compartments
from get_config import get_config, auth_type, profile, region, agent_endpoint_id
from oci_ai_text_generation import chat
from oci_ai_models import models
import oci

config=get_config()
generative_ai_client = oci.generative_ai.GenerativeAiClient(config)

def main():
    client = AgentClient(
        auth_type=auth_type,
        profile=profile,
        region=region,
    )
    Instructions ="""
    Route all requests to tool calling 
     """
    # Create a local agent object with the client, instructions, and tools.
    # You also need the agent endpoint id. To obtain the OCID, follow Step 1.
    agent = Agent(
        client=client,
        agent_endpoint_id=agent_endpoint_id,
        instructions=Instructions,
        tools=[Compartments(), models(), chat()]
    )

    agent.setup()

    # Run the agent. You can embed this method in your webapp, slack bot, etc.
    # You invoke the run() when you need to handle your user's request.

    # Do not change this input
    input = """
    Fetch the tenancy id using the get tenancy function.
    """
    response = agent.run(input)
    response.pretty_print()

    # Do not change this input
    input = """
    Analyze if the user input is text generation, summarization, (question and answer) or code. then set the model name as 'meta.llama-3.3-70b-instruct' and 
     If the user input is regarding coding then set the model name as 'meta.llama-4-maverick-17b-128e-instruct-fp8'. 
     Pass the model name, compartment id and fetch the model id using the model_id function.
    """
    response = agent.run(input, session_id=response.session_id)
    response.pretty_print()

    # Change this input alone as per your requirement.
    input = """
    Write an essay about cloud technology in not more than 200 words
    """
    response = agent.run(input, session_id=response.session_id)
    response.pretty_print()



if __name__ == "__main__":
    main()