🛠️ Dynamic Travel Planner with OpenAI Function Calling + Real-Time Insights via Dappier

import os
from getpass import getpass

# Prompt for the Dappier API key securely
dappier_api_key = getpass('Enter your API key: ')
os.environ["DAPPIER_API_KEY"] = dappier_api_key

# Prompt for the API key securely
openai_api_key = getpass('Enter your API key: ')
os.environ["OPENAI_API_KEY"] = openai_api_key

from openai import OpenAI
from dappier import Dappier

# Initialize clients
openai_client = OpenAI()
dappier_client = Dappier()

def dappier_real_time_search(query: str) -> str:
    response = dappier_client.search_real_time_data(
        query=query,
        ai_model_id="am_01j06ytn18ejftedz6dyhz2b15"
    )
    return response.message if response else "No data found."

user_prompt = """
Generate a 2-day travel itinerary for New York City, tailored to real-time weather and current news. Follow these steps:

1. Fetch Current News:
Retrieve the latest news in New York City to include relevant events or updates.

2. Fetch Weather Data:
Retrieve the weather forecast for the upcoming weekend dates.

3. Fetch Hotel Deals:
Retrieve the cheapest hotel deals in NYC.

4. Design the Itinerary:
Use both news and weather insights to create a dynamic itinerary. Include news in the beginning of the itinerary and include weather for each day in the itinerary.
"""

tools = [{
    "type": "function",
    "function": {
        "name": "dappier_real_time_search",
        "description": "Accesses real-time information including news, weather, and more.",
        "parameters": {
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "Search query (e.g., 'Latest news in NYC')"
                }
            },
            "required": ["query"]
        }
    }
}]

import json

def run_conversation(user_prompt: str):
    messages = [{"role": "user", "content": user_prompt}]

    # Step 1: Let OpenAI decide on needed functions
    response = openai_client.chat.completions.create(
        model="gpt-4o-mini",
        messages=messages,
        tools=tools,
        tool_choice="auto",
        temperature=0
    )

    response_message = response.choices[0].message

    # Step 2: Call Dappier for each function the model requested
    if tool_calls := response_message.tool_calls:
        messages.append(response_message)

        for tool_call in tool_calls:
            function_args = json.loads(tool_call.function.arguments)
            tool_output = dappier_real_time_search(query=function_args["query"])

            messages.append({
                "tool_call_id": tool_call.id,
                "role": "tool",
                "name": tool_call.function.name,
                "content": tool_output,
            })

    # Step 3: Final call to OpenAI with all gathered data
    final_response = openai_client.chat.completions.create(
        model="gpt-4o-mini",
        messages=messages,
        temperature=0,
        stream=True
    )

    return final_response

if __name__ == "__main__":
    print("Processing travel planner...\n")

    response = run_conversation(user_prompt)

    print("\n🗓️ Final Itinerary:\n")
    for chunk in response:
        print(chunk.choices[0].delta.content or "", end='', flush=True)