//! Example demonstrating MCP-based chat interaction.
//!
//! This example shows the recommended way to interact with LLMs via the MCP architecture.
//! It uses `RemoteMcpClient` which communicates with the MCP LLM server.
//!
//! Prerequisites:
//! - Build the MCP LLM server: `cargo build --release -p owlen-mcp-llm-server`
//! - Ensure Ollama is running with a model available

use owlen_core::{
    Provider,
    mcp::remote_client::RemoteMcpClient,
    types::{ChatParameters, ChatRequest, Message, Role},
};
use std::sync::Arc;

#[tokio::main]
async fn main() -> Result<(), anyhow::Error> {
    println!("🦉 Owlen MCP Chat Example\n");

    // Create MCP client - this will spawn/connect to the MCP LLM server
    println!("Connecting to MCP LLM server...");
    let client = Arc::new(RemoteMcpClient::new()?);
    println!("✓ Connected\n");

    // List available models
    println!("Fetching available models...");
    let models = client.list_models().await?;
    println!("Available models:");
    for model in &models {
        println!("  - {} ({})", model.name, model.provider);
    }
    println!();

    // Select first available model or default
    let model_name = models
        .first()
        .map(|m| m.id.clone())
        .unwrap_or_else(|| "llama3.2:latest".to_string());
    println!("Using model: {}\n", model_name);

    // Create a simple chat request
    let user_message = "What is the capital of France? Please be concise.";
    println!("User: {}", user_message);

    let request = ChatRequest {
        model: model_name,
        messages: vec![Message::new(Role::User, user_message.to_string())],
        parameters: ChatParameters {
            temperature: Some(0.7),
            max_tokens: Some(100),
            stream: false,
            extra: std::collections::HashMap::new(),
        },
        tools: None,
    };

    // Send request and get response
    println!("\nAssistant: ");
    let response = client.send_prompt(request).await?;
    println!("{}", response.message.content);

    if let Some(usage) = response.usage {
        println!(
            "\n📊 Tokens: {} prompt + {} completion = {} total",
            usage.prompt_tokens, usage.completion_tokens, usage.total_tokens
        );
    }

    Ok(())
}