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 {
#[arg(short, long)]
pub name: Option<String>,
#[arg(short, long)]
pub directory: Option<PathBuf>,
#[arg(short, long, default_value = "basic")]
pub template: String,
#[arg(long)]
pub git: bool,
#[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));
tokio::fs::create_dir_all(&project_dir).await?;
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<()> {
let src_dir = dir.join("src");
tokio::fs::create_dir_all(&src_dir).await?;
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(())
}
"#
}
};
let main_rs = src_dir.join("main.rs");
tokio::fs::write(&main_rs, main_content).await?;
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(())
}