#[cfg(feature = "mkl")]
extern crate intel_mkl_src;

#[cfg(feature = "accelerate")]
extern crate accelerate_src;

use candle::{IndexOp, D};
use clap::Parser;

#[derive(Parser)]
struct Args {
    #[arg(long)]
    image: String,

    #[arg(long)]
    model: Option<String>,
}

pub fn main() -> anyhow::Result<()> {
    let args = Args::parse();
    let image = candle_examples::imagenet::load_image224(args.image)?;

    println!("loaded image {image:?}");

    let model = match args.model {
        Some(model) => std::path::PathBuf::from(model),
        None => hf_hub::api::sync::Api::new()?
            .model("lmz/candle-onnx".into())
            .get("squeezenet1.1-7.onnx")?,
    };

    let model = candle_onnx::read_file(model)?;
    let graph = model.graph.as_ref().unwrap();
    let mut inputs = std::collections::HashMap::new();
    inputs.insert(graph.input[0].name.to_string(), image.unsqueeze(0)?);
    let mut outputs = candle_onnx::simple_eval(&model, inputs)?;
    let logits = outputs.remove(&graph.output[0].name).unwrap();
    let prs = candle_nn::ops::softmax(&logits, D::Minus1)?
        .i(0)?
        .to_vec1::<f32>()?;

    // Sort the predictions and take the top 5
    let mut top: Vec<_> = prs.iter().enumerate().collect();
    top.sort_by(|a, b| b.1.partial_cmp(a.1).unwrap());
    let top = top.into_iter().take(5).collect::<Vec<_>>();

    // Print the top predictions
    for &(i, p) in &top {
        println!(
            "{:50}: {:.2}%",
            candle_examples::imagenet::CLASSES[i],
            p * 100.0
        );
    }

    Ok(())
}