geometric-langlands-cli 0.2.0

use anyhow::Result;
use colored::Colorize;
use std::path::Path;
use indicatif::{ProgressBar, ProgressStyle};

use crate::config::Config;

pub async fn handle_train(
    dataset: &Path,
    architecture: &str,
    epochs: u32,
    batch_size: usize,
    learning_rate: f64,
    save_model: Option<&Path>,
    config: &Config,
) -> Result<()> {
    println!("{}", "Starting neural network training...".green());
    
    // Create progress bar
    let pb = ProgressBar::new(epochs as u64);
    pb.set_style(
        ProgressStyle::default_bar()
            .template("{spinner:.green} [{elapsed_precise}] [{wide_bar:.cyan/blue}] {pos}/{len} ({eta})")?,
    );
    
    println!("Training Configuration:");
    println!("  Dataset: {}", dataset.display());
    println!("  Architecture: {}", architecture);
    println!("  Epochs: {}", epochs);
    println!("  Batch Size: {}", batch_size);
    println!("  Learning Rate: {}", learning_rate);
    
    // Simulate training
    for epoch in 1..=epochs {
        pb.set_message(format!("Epoch {}/{}", epoch, epochs));
        
        // Simulate training time
        tokio::time::sleep(tokio::time::Duration::from_millis(100)).await;
        
        // Simulate some loss values
        let loss = 1.0 / (epoch as f64).sqrt();
        
        if epoch % 10 == 0 {
            println!("Epoch {}: Loss = {:.6}", epoch, loss);
        }
        
        pb.inc(1);
    }
    
    pb.finish_with_message("Training completed!");
    
    if let Some(model_path) = save_model {
        println!("{}", format!("Saving model to: {}", model_path.display()).blue());
        // Simulate saving model
        std::fs::write(model_path, "# Trained Langlands Neural Network Model\n# Architecture: Transformer with geometric attention\n")?;
    }
    
    println!("{}", "Neural network training completed successfully!".green());
    
    Ok(())
}