"""
Simple weather assistant agent using PydanticAI.

This agent demonstrates structured outputs by returning weather information
in a consistent format using Pydantic models.
"""

from pydantic import BaseModel
from pydantic_ai import Agent, RunContext

# Use the OpenAI Responses API with the smallest current GPT-5 reasoning model.
# The `openai-responses:` prefix selects the Responses API explicitly (the bare
# `openai:` prefix also resolves there in PydanticAI v2.0+).
DEFAULT_MODEL = "openai-responses:gpt-5.4-nano"


class WeatherResponse(BaseModel):
    """Structured weather response"""

    location: str
    temperature: str
    description: str


def get_weather(ctx: RunContext, location: str) -> dict:
    """Get weather data for a location (mock implementation for demo)"""
    # Simple mock weather data for demonstration
    mock_weather = {
        "london": {"temp": "18°C", "desc": "Cloudy"},
        "new york": {"temp": "22°C", "desc": "Sunny"},
        "tokyo": {"temp": "16°C", "desc": "Rainy"},
    }

    location_lower = location.lower()
    for city, weather in mock_weather.items():
        if city in location_lower:
            return {
                "location": location,
                "temperature": weather["temp"],
                "description": weather["desc"],
            }

    # Default response for unknown locations
    return {"location": location, "temperature": "21°C", "description": "Clear"}


def get_weather_agent(model: str = DEFAULT_MODEL) -> Agent:
    """Create a weather agent with structured output"""
    agent = Agent(
        model,
        output_type=WeatherResponse,
        system_prompt=(
            "You are a helpful weather assistant. "
            "Use the get_weather tool to fetch weather data for locations. "
            "Always return responses in the required structured format."
        ),
    )

    agent.tool(get_weather)
    return agent


async def run_weather_agent(query: str, model: str = DEFAULT_MODEL) -> WeatherResponse:
    """Run the weather agent with a query"""
    try:
        agent = get_weather_agent(model)
        result = await agent.run(query)
        return result.output
    except Exception as e:
        return WeatherResponse(
            location="Unknown", temperature="N/A", description=f"Error: {str(e)}"
        )


if __name__ == "__main__":
    import asyncio

    async def test_agent():
        queries = ["What's the weather like in London?"]
        for query in queries:
            result = await run_weather_agent(query)
            print(f"{query} -> {result.model_dump_json()}")

    asyncio.run(test_agent())