# /// script
# requires-python = ">=3.10"
# dependencies = [
#     "agent-framework-foundry",
#     "azure-monitor-opentelemetry",
# ]
# ///
# Run with any PEP 723 compatible runner, e.g.:
#   uv run python/samples/02-agents/observability/foundry_tracing.py

# Copyright (c) Microsoft. All rights reserved.

import asyncio
import logging
import os
from random import randint
from typing import Annotated

from agent_framework import Agent, tool
from agent_framework.foundry import FoundryChatClient
from agent_framework.observability import get_tracer
from azure.identity import AzureCliCredential
from dotenv import load_dotenv
from opentelemetry.trace import SpanKind
from opentelemetry.trace.span import format_trace_id
from pydantic import Field

"""
This sample shows how to setup telemetry in Microsoft Foundry for a custom agent
using ``FoundryChatClient.configure_azure_monitor()``.

First ensure you have a Foundry workspace with Application Insights enabled.
And use the Operate tab to Register an Agent.
Set the OpenTelemetry agent ID to the value used below in the Agent creation: ``weather-agent``
(or change both).

Environment variables:
    FOUNDRY_PROJECT_ENDPOINT — Microsoft Foundry project endpoint
    FOUNDRY_MODEL            — Model deployment name (e.g. gpt-4o)
"""

load_dotenv()

logger = logging.getLogger(__name__)


# NOTE: approval_mode="never_require" is for sample brevity.
@tool(approval_mode="never_require")
async def get_weather(
    location: Annotated[str, Field(description="The location to get the weather for.")],
) -> str:
    """Get the weather for a given location."""
    await asyncio.sleep(randint(0, 10) / 10.0)  # Simulate a network call
    conditions = ["sunny", "cloudy", "rainy", "stormy"]
    return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."


async def main():
    client = FoundryChatClient(
        project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
        model=os.environ["FOUNDRY_MODEL"],
        credential=AzureCliCredential(),
    )

    # configure_azure_monitor() retrieves the Application Insights connection string
    # from the project client and sets up tracing automatically.
    await client.configure_azure_monitor(
        enable_sensitive_data=True,
        enable_live_metrics=True,
    )
    print("Observability is set up. Starting Weather Agent...")

    questions = ["What's the weather in Amsterdam?", "and in Paris, and which is better?", "Why is the sky blue?"]

    with get_tracer().start_as_current_span("Weather Agent Chat", kind=SpanKind.CLIENT) as current_span:
        print(f"Trace ID: {format_trace_id(current_span.get_span_context().trace_id)}")

        agent = Agent(
            client=client,
            tools=[get_weather],
            name="WeatherAgent",
            instructions="You are a weather assistant.",
            id="weather-agent",
        )
        session = agent.create_session()
        for question in questions:
            print(f"\nUser: {question}")
            print(f"{agent.name}: ", end="")
            async for update in agent.run(question, session=session, stream=True):
                if update.text:
                    print(update.text, end="")


if __name__ == "__main__":
    asyncio.run(main())
