deployment_and_packaging.rs

use pyo3::prelude::*;
use pyo3::wrap_pyfunction;
use std::env;
use std::fs::File;
use std::io::Write;
use std::path::Path;
use tch::{nn, Device, Tensor};
use rand::Rng;

// Function to load a pre-trained PyTorch model
fn load_model(model_path: &str) -> Result<nn::Sequential, Box<dyn std::error::Error>> {
    // Determine the device to use (CUDA if available, otherwise CPU)
    let device = Device::cuda_if_available();
    
    // Load the pre-trained model from the specified path
    let model = nn::Sequential::load(model_path)?;

    // Set the model to evaluation mode
    model.set_eval();
    
    // Move the model to the selected device
    model.to_device(device);
    
    // Return the loaded model
    Ok(model)
}

// Function to generate audio samples using a pre-trained PyTorch model
fn generate_audio_samples(model: &nn::Sequential, num_samples: usize) -> Result<Vec<f32>, Box<dyn std::error::Error>> {
    // Generate random input data for the model
    let mut rng = rand::thread_rng();
    let input_data: Vec<f32> = (0..num_samples).map(|_| rng.gen()).collect();

    // Convert the input data to a PyTorch tensor
    let input_tensor = Tensor::of_slice(&input_data).view([-1, 1, num_samples as i64]);

    // Use the pre-trained model to generate audio samples
    let output_tensor = model.forward(&input_tensor).squeeze();
    
    // Extract the output data as a vector of f32
    let output_data = output_tensor.data().as_slice::<f32>()?.to_vec();

    // Return the generated audio samples
    Ok(output_data)
}

// Function to package the application and its dependencies
fn package_application(output_dir: &str) -> Result<(), Box<dyn std::error::Error>> {
    // Create the output directory if it doesn't exist
    if !Path::new(output_dir).exists() {
        std::fs::create_dir(output_dir)?;
    }

    // Copy the pre-trained PyTorch model to the output directory
    let model_path = "path/to/pretrained/model.pt";
    let model_file_name = Path::new(model_path).file_name().unwrap().to_str().unwrap();
    let output_model_path = format!("{}/{}", output_dir, model_file_name);
    std::fs::copy(model_path, output_model_path)?;

    // Create a requirements file with the necessary dependencies
    let requirements_path = format!("{}/requirements.txt", output_dir);
    let mut requirements_file = File::create(requirements_path)?;
    requirements_file.write_all(b"torch\ntqdm\nnumpy\n")?;

    // Copy the Rust executable to the output directory
    let executable_path = env::current_exe()?;
    let executable_file_name = executable_path.file_name().unwrap().to_str().unwrap();
    let output_executable_path = format!("{}/{}", output_dir, executable_file_name);
    std::fs::copy(executable_path, output_executable_path)?;

    // Return Ok if all operations succeed
    Ok(())
}

// Python module to expose the audio generator functionality
#[pymodule]
fn deployment_and_packaging(_py: Python, m: &PyModule) -> PyResult<()> {
    // Define a Python function to generate audio samples
    #[pyfn(m)]
    #[pyo3(name = "generate_audio")]
    fn generate_audio_py(model_path: &str, num_samples: usize) -> PyResult<Vec<f32>> {
        // Load the pre-trained PyTorch model
        let model = load_model(model_path)
            .map_err(|e| PyErr::new::<pyo3::exceptions::PyIOError, _>(format!("Failed to load model: {}", e)))?;

        // Generate audio samples using the pre-trained model
        let samples = generate_audio_samples(&model, num_samples)
            .map_err(|e| PyErr::new::<pyo3::exceptions::PyRuntimeError, _>(format!("Failed to generate audio: {}", e)))?;

        Ok(samples)
    }

    // Define a Python function to package the application
    #[pyfn(m)]
    #[pyo3(name = "package_application")]
    fn package_application_py(output_dir: &str) -> PyResult<()> {
        package_application(output_dir)
            .map_err(|e| PyErr::new::<pyo3::exceptions::PyIOError, _>(format!("Failed to package application: {}", e)))?;
        Ok(())
    }

    Ok(())
}