import os
from dotenv import load_dotenv
from strands import Agent
from strands.models.litellm import LiteLLMModel
from strands_tools import http_request

# Load environment variables from a .env file
load_dotenv()

WEATHER_SYSTEM_PROMPT = """You are a friendly and helpful weather assistant with HTTP capabilities.

Your primary function is to provide accurate weather forecasts for locations in the United States by using the National Weather Service API.

Follow these steps to fulfill a user's request:
1. First, if you don't have grid coordinates, use the points API endpoint to get them.
   - For latitude and longitude: https://api.weather.gov/points/{latitude},{longitude}
   - For a US zipcode: https://api.weather.gov/points/{zipcode}
2. The points API will return a `forecast` URL. Use this URL to make a second HTTP request to get the actual weather forecast.
3. Process the forecast data and present it to the user in a clear, easy-to-understand format.

When displaying your response:
- Highlight key information like temperature, precipitation, and any weather alerts.
- Explain technical terms in simple language.
- If you encounter an error, apologize and explain that you couldn't retrieve the weather information.
"""

def create_weather_agent() -> Agent:
    """
    Creates and configures a weather-focused agent.
    
    Returns:
        An Agent instance configured with a model, system prompt, and tools.
    """
    # Configure the language model (LLM) that will power the agent
    model = LiteLLMModel(
        client_args={
            "api_key": os.getenv("NEBIUS_API_KEY"),
        },
        model_id="nebius/deepseek-ai/DeepSeek-V3-0324",
        params={
            "max_tokens": 1500,
            "temperature": 0.7,
        },
    )
    
    # Create the agent instance
    weather_agent = Agent(
        system_prompt=WEATHER_SYSTEM_PROMPT,
        tools=[http_request],  # Grant the agent HTTP capabilities
        model=model,
    )
    return weather_agent

def main():
    """
    Main function to run the weather agent.
    """
    # Create the weather agent
    weather_agent = create_weather_agent()
    
    # Define a user query
    user_query = "Compare the temperature in New York, NY and Chicago, IL this weekend."
    
    # Invoke the agent with the query and get the response
    print(f"User Query: {user_query}\n")
    response = weather_agent(user_query)
    
    # Print the agent's final response
    print("Weather Agent Response:")
    print(response)

if __name__ == "__main__":
    main()

User Query: Compare the temperature in New York, NY and Chicago, IL this weekend.

Weather Agent Response:
This weekend, New York City is expected to have temperatures ranging from 
52°F to 61°F, while Chicago will see cooler weather with temperatures between 
45°F and 54°F. New York will be about 7-9 degrees warmer than Chicago.