f_agent = Agent(name="Flight Assistant agent",
              instructions=f"""
Always answer in given language. Return all the given flight information

departure_id : Parameter defines the departure airport code or location kgmid. An airport code is an uppercase 3-letter code. For example, CDG is Paris Charles de Gaulle Airport and AUS is Austin-Bergstrom International Airport.

arrival_id : Parameter defines the arrival airport code or location kgmid. An airport code is an uppercase 3-letter code. You can search for it on Google Flights or IATA. For example, CDG is Paris Charles de Gaulle Airport and AUS is Austin-Bergstrom International Airport.

outbound_date : Parameter defines the outbound date. The format is YYYY-MM-DD. e.g. 2025-04-09

return_date : Parameter defines the return date. The format is YYYY-MM-DD. e.g. 2025-04-15
Today : {datetime.now().strftime("%Y-%m-%d")}
""",
              model="gpt-4o-mini",
              # model = 'gpt-4o',
              model_settings=ModelSettings(temperature= 0.0,
                                #  max_tokens = 4096*8
                                 ),
            tools= [flight_search_2]
              )
              
h_agent = Agent(
    name = "Hotels Assistant agent",
    instructions= """Returns google hotels information.     
    q : Location
    gl : Country""",
    model = 'gpt-4o-mini',
    tools=[hotels_search2],
    model_settings=ModelSettings(temperature= 0.0,
                                #  max_tokens = 4096*2
                                 ),
)



y_agent = Agent(
    name = "Yelp Search information",
    instructions= """Returns Yelp search results.     
    search_term: str, 
    location: str
    """.strip(),
    model = 'gpt-4o-mini',
    tools=[yelp_search2],
    model_settings=ModelSettings(temperature= 0.0,
                                #  max_tokens = 4096*2
                                 ),
)              


# agents are described above with tools from serp API. 

import os
import sys
import uuid
import datetime
from dotenv import load_dotenv
from pydantic import BaseModel
from typing import List, Optional
from openai import OpenAI
import pandas as pd
import numpy as np


script_dir = os.path.dirname(os.path.abspath(__file__))
parent_dir = os.path.join(script_dir, "..")
sys.path.append(parent_dir)

from agents import Agent, Runner, trace
from helpers.travel_agents import f_agent, h_agent, y_agent
from helpers.helper_functions import assemble_conversation
from agents.extensions.handoff_prompt import RECOMMENDED_PROMPT_PREFIX 
load_dotenv()

myuuid = str(uuid.uuid4())
print(f"Session UUID: {myuuid}")

main_agent = Agent(
    name="Main Agent",

    model="gpt-4.1", 
    instructions=f"""{RECOMMENDED_PROMPT_PREFIX}
You are a helpful travel planning assistant. Guide the user through planning their trip.
If the user asks about flights or flight search, handoff to the Flight Assistant agent.
If the user asks about accommodation, hotels, or places to stay, handoff to the Hotels Assistant agent.
If the user asks about restaurants, food, or places to eat, handoff to the Yelp Assistant agent.
For general conversation or other requests, respond yourself.
Remember to be friendly and helpful!
""", 
    handoffs=[f_agent, h_agent, y_agent],
    handoff_description=(
        "Handoff to the relevant assistant (Flights, Hotels, Yelp) for specific search or booking queries."
    ),
)

if __name__ == "__main__":
    print("Hello! I'm your travel planning assistant.")
    print("What can I help you plan today?")
    print("Type 'quit' or 'exit' to end the conversation.")

    last_response = None


    while True:
        user_input = input("> ")

        # Check for the quit condition
        if user_input.lower() in ['quit', 'exit']:
            print("Okay, goodbye! Happy travels!")
            break # Exit the loop

        # If the user didn't quit, process the input
        print("\nProcessing your request...")
        current_turn_input = assemble_conversation(last_response, user_input) if last_response else user_input

        try:
            with trace(myuuid):
                res = Runner.run_sync(main_agent, current_turn_input)

            print("\n--- Assistant Response ---")
            print(res.final_output)
            print("------------------------")

            last_response = res

        except Exception as e:
            print(f"\nAn error occurred during agent execution: {e}")
            last_response = None
        print("\nWhat else can I help with? (Type 'quit' or 'exit' to end)")

help_agent =Agent(
    name= 'help desk agent', 
    instructions=f"""You are an experienced and kind help desk agent. Help users in terms of their needs, if the question about travel. 
    You may need to call multiple agents to get all answers.
    Today : {datetime.now().strftime("%Y-%m-%d")}""",
    model='gpt-4.1',
    tools=[
        f_agent.as_tool(
            tool_name="flight_information_agent", 
            tool_description="Search the flight information"
        ),
        h_agent.as_tool(
            tool_name="hotel_information_agent", 
            tool_description="Search the hotel information"
        ),
        y_agent.as_tool(
            tool_name="food_information_agent",  
            tool_description="Search the restaurants and place for food information"
        ),
        # recommender_agent.as_tool(
        #     tool_name="travel_recommendation_agent",  
        #     tool_description="gives recommendation about the travel if necessary"
        # ),
        # information_gathering_agent.as_tool(
        #     tool_name="info_gathering_agent", 
        #     tool_description="Kindly asks for travel details"
        # )
    ]
)

question : I'm considering a trip to Rome (FCO) sometime in late September or early October.     I'm pretty flexible with the exact dates, maybe around a week-long trip.     I'd like to get an idea of flight ticket prices and some well-located hotels.     I'm also a big foodie, so any recommendations for great local restaurants would be fantastic!