import { pipeline } from "@huggingface/transformers";

function loadingBar(total: number, current: number, barLength = 20) {
  const filled = Math.round((current / total) * barLength);
  const empty = barLength - filled;

  const bar = `[${"#".repeat(filled)}${" ".repeat(empty)}] ${Math.round(
    (current / total) * 100
  )}%`;
  process.stdout.write("\r" + bar);
}

process.stdout.write("\n"); // Move

async function run() {
  // Load a pre-trained model and tokenizer
  const pipe = await pipeline(
    "text-generation",
    "HuggingFaceTB/SmolLM2-135M-Instruct",
    // "HuggingFaceTB/SmolLM2-360M-Instruct",
    // "HuggingFaceTB/SmolLM2-1.7B-Instruct",
    {
      cache_dir: "./cache",
      progress_callback: (progress: any) => {
        loadingBar(progress.total, progress.loaded);
      },
    }
  );

  // Perform sentiment analysis
  const result = await pipe(
    [
      {
        role: "user",
        content: `Recomend a song for me based on these songs: 'Dheere Dheere Aap Mere', 'Zara Zara - Deep House Mix', 'Hey Minnale (From "Amaran") (Tamil)', 'Apna Bana Le (From "Bhediya")`,
      },
    ],
    {
      max_new_tokens: 100,
    }
  );

  console.log(result);
}

run().catch(console.error);