mermaid-cli 0.3.10

use anyhow::Result;
use std::path::PathBuf;

use crate::{
    app::{load_config, persist_last_model, Config},
    cli::{handle_command, Cli},
    models::ModelFactory,
    ollama::{ensure_model as ensure_ollama_model, require_any_model},
    session::{select_conversation, ConversationManager},
    tui::{run_ui, App},
    utils::{check_ollama_available, log_error, log_info, log_progress, log_warn},
};

/// Main runtime orchestrator
pub struct Orchestrator {
    cli: Cli,
    config: Config,
}

impl Orchestrator {
    /// Create a new orchestrator from CLI args
    pub fn new(cli: Cli) -> Result<Self> {
        // Load configuration (single file + defaults)
        let config = match load_config() {
            Ok(cfg) => cfg,
            Err(e) => {
                log_warn("CONFIG", format!("Config load failed: {:#}. Using defaults.", e));
                Config::default()
            },
        };

        Ok(Self {
            cli,
            config,
        })
    }

    /// Run the orchestrator
    pub async fn run(self) -> Result<()> {
        // Progress tracking for startup
        let total_steps = 6; // Total startup steps
        let mut current_step = 0;

        // Handle subcommands
        current_step += 1;
        log_progress(current_step, total_steps, "Processing commands");
        if let Some(command) = &self.cli.command {
            if handle_command(command).await? {
                return Ok(()); // Command handled, exit
            }
            // Continue to chat for Commands::Chat
        }

        // Determine model to use (CLI arg > last_used > default_model)
        current_step += 1;
        log_progress(current_step, total_steps, "Configuring model");

        let cli_model_provided = self.cli.model.is_some();
        let model_id = if let Some(model) = &self.cli.model {
            // CLI flag takes precedence
            model.clone()
        } else if let Some(last_model) = &self.config.last_used_model {
            // Use last used model from config
            last_model.clone()
        } else if !self.config.default_model.provider.is_empty()
            && !self.config.default_model.name.is_empty()
        {
            // Fall back to default_model if set
            format!(
                "{}/{}",
                self.config.default_model.provider, self.config.default_model.name
            )
        } else {
            // No model configured - check if any models are available
            let available = require_any_model().await?;
            // Use first available model
            format!("ollama/{}", available[0])
        };

        log_info(
            "MERMAID",
            format!("Starting Mermaid with model: {}", model_id),
        );

        // Check Ollama availability for local models
        current_step += 1;
        if is_local_model(&model_id) {
            log_progress(current_step, total_steps, "Checking Ollama availability");
            let ollama_check = check_ollama_available(
                &self.config.ollama.host,
                self.config.ollama.port,
            ).await;

            if !ollama_check.available {
                log_error("OLLAMA", &ollama_check.message);
                std::process::exit(1);
            }
        } else {
            log_progress(current_step, total_steps, "Using API provider");
        }

        // Validate model exists
        current_step += 1;
        log_progress(current_step, total_steps, "Checking model availability");
        ensure_ollama_model(&model_id, true).await?;

        // Persist model if CLI flag was used
        if cli_model_provided {
            if let Err(e) = persist_last_model(&model_id) {
                log_warn("CONFIG", format!("Failed to persist model choice: {}", e));
            }
        }

        // Create model instance with config for authentication and optional backend override
        current_step += 1;
        log_progress(current_step, total_steps, "Initializing model");
        // Use config.behavior.backend ("auto" means None)
        let backend = if self.config.behavior.backend == "auto" {
            None
        } else {
            Some(self.config.behavior.backend.as_str())
        };
        let model = match ModelFactory::create_with_backend(
            &model_id,
            Some(&self.config),
            backend,
        )
        .await
        {
            Ok(m) => m,
            Err(e) => {
                log_error("ERROR", format!("Failed to initialize model: {}", e));
                log_error(
                    "",
                    "Make sure the model is available and properly configured.",
                );
                std::process::exit(1);
            },
        };

        // Set up project path for session management
        let project_path = self.cli.path.clone().unwrap_or_else(|| PathBuf::from("."));

        // Start UI - LLM explores codebase via tools, no context injection
        current_step += 1;
        log_progress(current_step, total_steps, "Starting UI");
        let mut app = App::new(model, model_id.clone());

        // Handle session loading
        // Default: start fresh (no history)
        // --continue: resume last conversation
        // --sessions: show picker to choose a previous conversation
        if self.cli.continue_session || self.cli.sessions {
            let conversation_manager = ConversationManager::new(&project_path)?;

            if self.cli.sessions {
                // Show selection UI for choosing a conversation
                let conversations = conversation_manager.list_conversations()?;
                if !conversations.is_empty() {
                    if let Some(selected) = select_conversation(conversations)? {
                        log_info(
                            "RESUME",
                            format!("Resuming conversation: {}", selected.title),
                        );
                        app.load_conversation(selected);
                    }
                } else {
                    log_info("INFO", "No previous conversations found in this directory");
                }
            } else {
                // --continue: resume last conversation
                if let Some(last_conv) = conversation_manager.load_last_conversation()? {
                    log_info(
                        "RESUME",
                        format!("Resuming: {}", last_conv.title),
                    );
                    app.load_conversation(last_conv);
                } else {
                    log_info("INFO", "No previous conversation to continue");
                }
            }
        }

        // Run the TUI
        run_ui(app).await
    }
}

/// Check if a model uses local Ollama inference
///
/// All models go through Ollama:
/// - `ollama/` prefix - explicit Ollama models
/// - `:cloud` suffix - Ollama cloud routing (e.g., kimi-k2:cloud)
/// - Models without prefix - auto-discovered on Ollama
fn is_local_model(_model_id: &str) -> bool {
    // All models use Ollama (local or cloud routing)
    true
}