import json
from openai import OpenAI # Using the OpenAI SDK as a common example

# --- Step 1: Define the actual function in your code ---
def get_current_weather(location: str, unit: str = "fahrenheit", detailed: bool = False) -> dict:
    """
    Gets the current weather for a given location.
    In a real app, this would call an external weather API.
    """
    print(f"-> Calling get_current_weather for {location} ({unit}), detailed={detailed}")
    # Placeholder: mock data for demonstration
    if location == "San Francisco":
        base_data = {"temperature": "60", "unit": unit, "condition": "Cloudy"}
    else:
        base_data = {"temperature": "72", "unit": unit, "condition": "Sunny"}

    # Add detailed information if requested
    if detailed:
        base_data.update({
            "humidity": "70%",
            "wind_speed": "10 mph",
            "forecast": "Partly cloudy in the evening"
        })

    return base_data

# --- Step 2: Define the function schema for the LLM ---
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA"
                    },
                    "detailed": {
                        "type": "boolean",
                        "description": "Whether to provide detailed weather information including humidity, wind speed, and forecast"
                    }
                },
                "required": ["location"],
            },
        },
    }
]

model="" # Or another function calling model

# --- Step 3: Main logic to interact with the LLM ---
def run_conversation():
    # Initialize your LLM client (e.g., OpenAI, Gemini, Mistral)
    client = OpenAI(
    base_url="http://127.0.0.1:8080/v1",
    api_key=""
    ) # Replace with your actual client setup

    # Initial user message
    messages = [{"role": "user", "content": "What's the detailed weather like in San Francisco today?"}]

    # First request: Ask the model if it needs to call a function
    response = client.chat.completions.create(
        model=model, # Or another function calling model
        messages=messages,
        tools=tools,
        tool_choice="auto",
    )
    
    response_message = response.choices[0].message
    tool_calls = response_message.tool_calls

    # Check if the model decided to call a function
    if tool_calls:
        print("Tools called!")
        
        # Step 4: Execute the function in your application
        messages.append(response_message) # Extend conversation with the function call request
        
        for tool_call in tool_calls:
            function_name = tool_call.function.name
            function_args = json.loads(tool_call.function.arguments)
            
            # Call the local function dynamically
            available_functions = {"get_current_weather": get_current_weather}
            function_to_call = available_functions[function_name]
            function_response = function_to_call(**function_args)
            
            # Step 5: Send the function output back to the model
            messages.append(
                {
                    "tool_call_id": tool_call.id,
                    "role": "tool",
                    "name": function_name,
                    "content": json.dumps(function_response),
                }
            )
        
        # Second request: Get the final answer from the model
        final_response = client.chat.completions.create(
            model=model,
            messages=messages,
        )
        return final_response.choices[0].message.content
    else:
        # Model returned a text response directly
        return response_message.content

# --- Run the example ---
if __name__ == "__main__":
    weather_report = run_conversation()
    print("\n--- Final Answer ---")
    print(weather_report)