torsh_cli/commands/

init.rs

//! Project initialization commands

use anyhow::Result;
use clap::Args;
use std::path::{Path, PathBuf};

use crate::config::Config;
use crate::utils::output;

#[derive(Debug, Args)]
pub struct InitCommand {
    /// Project name
    #[arg(short, long)]
    pub name: Option<String>,

    /// Project directory
    #[arg(short, long)]
    pub directory: Option<PathBuf>,

    /// Project template (basic, vision, nlp, custom)
    #[arg(short, long, default_value = "basic")]
    pub template: String,

    /// Enable Git repository initialization
    #[arg(long)]
    pub git: bool,

    /// Use interactive mode
    #[arg(short, long)]
    pub interactive: bool,
}

pub async fn execute(args: InitCommand, _config: &Config, _output_format: &str) -> Result<()> {
    output::print_info("Initializing new ToRSh project...");

    let project_name = args.name.unwrap_or_else(|| "torsh-project".to_string());
    let project_dir = args
        .directory
        .unwrap_or_else(|| PathBuf::from(&project_name));

    // Create project directory
    tokio::fs::create_dir_all(&project_dir).await?;

    // Create basic project structure
    create_project_structure(&project_dir, &args.template).await?;

    output::print_success(&format!(
        "Project '{}' initialized successfully!",
        project_name
    ));
    output::print_info(&format!("Location: {}", project_dir.display()));

    Ok(())
}

async fn create_project_structure(dir: &Path, template: &str) -> Result<()> {
    // Create basic directories
    let src_dir = dir.join("src");
    tokio::fs::create_dir_all(&src_dir).await?;

    // Create main.rs with appropriate template
    let main_content = match template {
        "vision" => {
            r#"//! Vision project template
//!
//! This template provides a starting point for computer vision projects using ToRSh.
//! Uncomment and modify the code below to train your vision model.

use anyhow::Result;
// Uncomment these imports when you're ready to use them:
// use torsh::prelude::*;
// use torsh_models::vision::*;
// use torsh_optim::Adam;

fn main() -> Result<()> {
    println!("ToRSh Vision Project");

    // Step 1: Create or load a vision model
    // Example - ResNet for image classification:
    // let config = ResNetConfig {
    //     variant: ResNetVariant::ResNet18,
    //     num_classes: 10,  // e.g., CIFAR-10
    //     ..Default::default()
    // };
    // let mut model = ResNet::new(config)?;

    // Step 2: Prepare your dataset
    // let train_loader = create_image_dataloader("path/to/train", batch_size: 32)?;
    // let val_loader = create_image_dataloader("path/to/val", batch_size: 32)?;

    // Step 3: Setup optimizer and loss function
    // let mut optimizer = Adam::new(model.parameters(), 0.001)?;
    // let loss_fn = CrossEntropyLoss::new();

    // Step 4: Training loop
    // for epoch in 1..=10 {
    //     model.train();
    //     for (images, labels) in train_loader.iter() {
    //         let predictions = model.forward(&images)?;
    //         let loss = loss_fn.forward(&predictions, &labels)?;
    //         loss.backward()?;
    //         optimizer.step()?;
    //         optimizer.zero_grad()?;
    //     }
    //
    //     // Validation
    //     model.eval();
    //     let accuracy = evaluate(&model, &val_loader)?;
    //     println!("Epoch {}: Validation Accuracy = {:.2}%", epoch, accuracy * 100.0);
    // }

    println!("Tip: Check torsh-models documentation for available vision models!");
    println!("Available models: ResNet, VisionTransformer, EfficientNet (v0.2.0+)");

    Ok(())
}
"#
        }
        "nlp" => {
            r#"//! NLP project template
//!
//! This template provides a starting point for natural language processing projects using ToRSh.
//! Uncomment and modify the code below to train your NLP model.

use anyhow::Result;
// Uncomment these imports when you're ready to use them:
// use torsh::prelude::*;
// use torsh_models::nlp::*;
// use torsh_optim::AdamW;

fn main() -> Result<()> {
    println!("ToRSh NLP Project");

    // Step 1: Create or load an NLP model
    // Example - RoBERTa for text classification:
    // let config = RobertaConfig {
    //     vocab_size: 50265,
    //     hidden_size: 768,
    //     num_hidden_layers: 12,
    //     num_attention_heads: 12,
    //     ..Default::default()
    // };
    // let mut model = RobertaForSequenceClassification::new(config, num_labels: 2)?;

    // Step 2: Prepare your text dataset
    // let tokenizer = load_tokenizer("roberta-base")?;
    // let train_loader = create_text_dataloader("path/to/train.csv", tokenizer, batch_size: 16)?;
    // let val_loader = create_text_dataloader("path/to/val.csv", tokenizer, batch_size: 16)?;

    // Step 3: Setup optimizer and loss function
    // let mut optimizer = AdamW::new(model.parameters(), learning_rate: 2e-5)?;
    // let loss_fn = CrossEntropyLoss::new();

    // Step 4: Training loop
    // for epoch in 1..=5 {
    //     model.train();
    //     for (input_ids, attention_mask, labels) in train_loader.iter() {
    //         let logits = model.forward(&input_ids)?;
    //         let loss = loss_fn.forward(&logits, &labels)?;
    //         loss.backward()?;
    //         optimizer.step()?;
    //         optimizer.zero_grad()?;
    //     }
    //
    //     // Validation
    //     model.eval();
    //     let accuracy = evaluate(&model, &val_loader)?;
    //     println!("Epoch {}: Validation Accuracy = {:.2}%", epoch, accuracy * 100.0);
    // }

    println!("Tip: Check torsh-models documentation for available NLP models!");
    println!("Available models: RoBERTa, BERT (v0.2.0+), GPT-2 (v0.2.0+)");

    Ok(())
}
"#
        }
        _ => {
            r#"//! Basic ToRSh project template
//!
//! This template provides a minimal starting point for machine learning projects using ToRSh.
//! Uncomment and modify the code below to build your model.

use anyhow::Result;
// Uncomment these imports when you're ready to use them:
// use torsh::prelude::*;
// use torsh_nn::{Linear, Module, Sequential};
// use torsh_optim::SGD;

fn main() -> Result<()> {
    println!("ToRSh Basic Project - Getting Started");

    // Step 1: Create a simple neural network
    // Example - Basic feedforward network:
    // let model = Sequential::new(vec![
    //     Linear::new(input_dim: 784, output_dim: 128, bias: true)?,
    //     ReLU::new(),
    //     Linear::new(128, 10, true)?,
    // ]);

    // Step 2: Prepare your dataset
    // let train_data = load_dataset("path/to/train.csv")?;
    // let val_data = load_dataset("path/to/val.csv")?;

    // Step 3: Setup optimizer and loss function
    // let mut optimizer = SGD::new(model.parameters(), learning_rate: 0.01)?;
    // let loss_fn = MSELoss::new();

    // Step 4: Training loop
    // let epochs = 10;
    // for epoch in 1..=epochs {
    //     model.train();
    //     let predictions = model.forward(&train_data.inputs)?;
    //     let loss = loss_fn.forward(&predictions, &train_data.targets)?;
    //
    //     loss.backward()?;
    //     optimizer.step()?;
    //     optimizer.zero_grad()?;
    //
    //     println!("Epoch {}/{}: Loss = {:.4}", epoch, epochs, loss.item());
    // }

    println!("\nNext steps:");
    println!("1. Add torsh dependencies to Cargo.toml");
    println!("2. Import necessary modules (torsh::prelude::*, torsh_nn::*, torsh_optim::*)");
    println!("3. Define your model architecture");
    println!("4. Load your dataset");
    println!("5. Train and evaluate your model");
    println!("\nFor more examples, see: https://github.com/cool-japan/torsh/tree/main/examples");

    Ok(())
}
"#
        }
    };

    // Write the template content to main.rs
    let main_rs = src_dir.join("main.rs");
    tokio::fs::write(&main_rs, main_content).await?;

    // Create Cargo.toml with correct version
    let cargo_toml = dir.join("Cargo.toml");
    let cargo_content = r#"[package]
name = "torsh-project"
version = "0.1.0"
edition = "2021"

[dependencies]
torsh = "0.1.0"
anyhow = "1.0"

# Uncomment these dependencies as needed:
# torsh-models = "0.1.0"
# torsh-optim = "0.1.0"
# torsh-data = "0.1.0"
"#;
    tokio::fs::write(&cargo_toml, cargo_content).await?;

    Ok(())
}