import asyncio
from operator import truediv
import os
from typing import Annotated
from pydantic import Field
from dotenv import load_dotenv
load_dotenv()
from azure.identity import DefaultAzureCredential
from agent_framework.azure import AzureOpenAIChatClient
from agent_framework import (
   HandoffBuilder,
   WorkflowEvent,
   WorkflowOutputEvent,
   ChatAgent,
)
import logging
import sys

logging.basicConfig(
    level=logging.DEBUG,
    format='[%(asctime)s - %(name)s - %(filename)s:%(lineno)d - %(levelname)s] %(message)s',
    handlers=[
        logging.StreamHandler(sys.stdout),
        logging.FileHandler('debug.log', mode='w', encoding='utf-8')
    ]
)

# 設置特定模組的日誌等級
logging.getLogger('agent_framework').setLevel(logging.DEBUG)
logging.getLogger('agent_framework_devui').setLevel(logging.DEBUG)
logging.getLogger('agent_framework._workflows').setLevel(logging.DEBUG)
# ---- Minimal mock tools for specialists ----
def mk_travel_plan(location: Annotated[str, Field(description="Location")]) -> str:
   print(f"mk_travel_plan tool executed")
   return f"[TravelPlanner] Plan for {location}: Day 1/2/3..."

def mk_route(location: Annotated[str, Field(description="Location")]) -> str:
   print(f"mk_route tool executed")
   return f"[RouteMaster] Best route adjustments for {location}."

def mk_weather(location: Annotated[str, Field(description="Location")]) -> str:
   print(f"mk_weather tool executed")
   return f"[WeatherAdvisor] Forecast summary for {location}: sunny."

def mk_hotel(location: Annotated[str, Field(description="Location")]) -> str:
   print(f"mk_hotel tool executed")
   return f"[HotelFinder] Recommended hotel in {location}: The Test Hotel."

def mk_budget() -> str:
   print(f"mk_budget tool executed")
   return "[TripBudget] Estimated total cost: 1234 TWD."

def mk_html() -> str:
   print(f"mk_html tool executed")
   return "[TravelStoryHTMLnImage] HTML generated with 1 image and map embeds."

async def main() -> None:
   chat_client = AzureOpenAIChatClient(
       credential=DefaultAzureCredential(),
       project_endpoint=os.environ["AIPROJECT_ENDPOINT"],
       deployment_name=os.environ["AZURE_OPENAI_DEPLOYMENT"],
   )
   # ---- Create agents directly with client (no Team helper) ----
   coordinator: ChatAgent = chat_client.create_agent(
       name="TaskPlanner",
       description="Coordinator that always delegates using handoff tools.",
       instructions=(
           "You are the coordinator. Read the latest user message and ALWAYS delegate the next step "
           "by calling exactly one handoff tool to the most suitable specialist. "
           "Specialists available: TravelPlanner, WeatherAdvisor, HotelFinder, RouteMaster, TripBudget, TravelStoryHTMLnImage. "
           "Examples of tools you may call: handoff_to_TravelPlanner, handoff_to_WeatherAdvisor, handoff_to_HotelFinder, "
           "handoff_to_RouteMaster, handoff_to_TripBudget, handoff_to_TravelStoryHTMLnImage. "
           "Do not produce final results yourself."
       ),
   )
   travel_planner: ChatAgent = chat_client.create_agent(
       name="TravelPlanner",
       description="Creates a simple itinerary.",
       instructions="Reply with a concise itinerary using your tool.",
    #   tools=[mk_travel_plan],
   )
   route_master: ChatAgent = chat_client.create_agent(
       name="RouteMaster",
       description="Optimizes routes.",
       instructions="Reply with route improvements using your tool.",
       tools=[mk_route],
   )
   weather_advisor: ChatAgent = chat_client.create_agent(
       name="WeatherAdvisor",
       description="Provides a short forecast.",
       instructions="Reply with a forecast using your tool.",
       tools=[mk_weather],
   )
   hotel_finder: ChatAgent = chat_client.create_agent(
       name="HotelFinder",
       description="Finds one suitable hotel.",
       instructions="Reply with one hotel using your tool.",
       tools=[mk_hotel],
   )
   trip_budget: ChatAgent = chat_client.create_agent(
       name="TripBudget",
       description="Outputs a simple budget number.",
       instructions="Reply with a budget using your tool.",
       tools=[mk_budget],
   )
   finalizer: ChatAgent = chat_client.create_agent(
       name="TravelStoryHTMLnImage",
       description="Generates a simple HTML summary.",
       instructions="Reply by generating HTML using your tool.",
       tools=[mk_html],
   )
   # ---- Build handoff workflow ----
   def terminate_when_finalizer(conversation) -> bool:
       return any(m.author_name == travel_planner.name for m in reversed(conversation))
   workflow = (
       HandoffBuilder(
           name="minimal_handoff",
           participants=[
               coordinator,
              travel_planner,
            #    route_master,
            #    weather_advisor,
            #    hotel_finder,
            #    trip_budget,
            #    finalizer,
           ],
       )
       .set_coordinator(coordinator)
       .add_handoff(coordinator, travel_planner, tool_description="Delegate to TravelPlanner for initial itinerary.")
    #   .add_handoff(coordinator, travel_planner, tool_description="Delegate to TravelPlanner for initial itinerary.")
    #    .add_handoff(coordinator, weather_advisor, tool_description="Delegate to WeatherAdvisor for forecast adjustments.")
    #    .add_handoff(coordinator, hotel_finder, tool_description="Delegate to HotelFinder for one hotel suggestion.")
    #    .add_handoff(coordinator, route_master, tool_description="Delegate to RouteMaster for route optimization.")
    #    .add_handoff(coordinator, trip_budget, tool_description="Delegate to TripBudget for a quick budget.")
    #    .add_handoff(coordinator, finalizer, tool_description="Delegate to TravelStoryHTMLnImage to generate HTML.")
       .with_termination_condition(
            lambda conv: sum(1 for msg in conv if msg.role.value == "user") >= 5
            or any("goodbye" in msg.text.lower() for msg in conv[-2:])
        )
       .build()
   )



   return workflow

if __name__ == "__main__":
   # Setup workflow in async context
   workflow = asyncio.run(main())
   from agent_framework.devui import serve   
   serve(entities=[workflow], port=8093, auto_open=True, tracing_enabled=True)