import { transformers } from '@localmode/transformers';
import {
  classify,
  extractEntities,
  transcribe,
  classifyImage,
  captionImage,
} from '@localmode/core';

// Text Classification
const sentiment = await classify({
  model: transformers.classifier('Xenova/distilbert-base-uncased-finetuned-sst-2-english'),
  text: 'I love this product!',
});
console.log(sentiment.label); // 'POSITIVE'

// Named Entity Recognition
const entities = await extractEntities({
  model: transformers.ner('Xenova/bert-base-NER'),
  text: 'John works at Microsoft in Seattle',
});
console.log(entities.entities);
// [{ entity: 'John', type: 'PER', ... }, { entity: 'Microsoft', type: 'ORG', ... }, ...]

// Speech-to-Text
const transcription = await transcribe({
  model: transformers.speechToText('Xenova/whisper-tiny'),
  audio: audioBlob,
  returnTimestamps: true,
});
console.log(transcription.text);

// Image Classification
const classification = await classifyImage({
  model: transformers.imageClassifier('Xenova/vit-base-patch16-224'),
  image: imageBlob,
});
console.log(classification.predictions);

// Image Captioning
const caption = await captionImage({
  model: transformers.captioner('Xenova/blip-image-captioning-base'),
  image: imageBlob,
});
console.log(caption.caption);

import { translate } from '@localmode/core';
import { transformers } from '@localmode/transformers';

const { translatedText } = await translate({
  model: transformers.translator('Xenova/opus-mt-en-de'),
  text: 'Hello world!',
  targetLanguage: 'de',
});
console.log(translatedText); // "Hallo Welt!"

import { summarize } from '@localmode/core';
import { transformers } from '@localmode/transformers';

const { summary } = await summarize({
  model: transformers.summarizer('Xenova/bart-large-cnn'),
  text: longArticle,
  maxLength: 100,
});

import { segmentImage } from '@localmode/core';
import { transformers } from '@localmode/transformers';

const { masks } = await segmentImage({
  model: transformers.segmenter('Xenova/segformer-b0-finetuned-ade-512-512'),
  image: imageBlob,
});

for (const mask of masks) {
  console.log(mask.label, mask.score);
}

import { detectObjects } from '@localmode/core';
import { transformers } from '@localmode/transformers';

const { objects } = await detectObjects({
  model: transformers.objectDetector('Xenova/detr-resnet-50'),
  image: imageBlob,
  threshold: 0.5,
});

for (const obj of objects) {
  console.log(`${obj.label}: ${obj.box.x},${obj.box.y}`);
}

import { synthesizeSpeech } from '@localmode/core';
import { transformers } from '@localmode/transformers';

const { audio, sampleRate } = await synthesizeSpeech({
  model: transformers.textToSpeech('Xenova/speecht5_tts'),
  text: 'Hello, how are you?',
});

// Play with Web Audio API
const ctx = new AudioContext();
const buffer = ctx.createBuffer(1, audio.length, sampleRate);
buffer.getChannelData(0).set(audio);
const source = ctx.createBufferSource();
source.buffer = buffer;
source.connect(ctx.destination);
source.start();

import { extractText } from '@localmode/core';
import { transformers } from '@localmode/transformers';

const { text, regions } = await extractText({
  model: transformers.ocr('Xenova/trocr-base-printed'),
  image: documentImage,
});
console.log(text);

import { answerQuestion } from '@localmode/core';
import { transformers } from '@localmode/transformers';

const { answers } = await answerQuestion({
  model: transformers.questionAnswering('Xenova/distilbert-base-cased-distilled-squad'),
  question: 'What is the capital of France?',
  context: 'Paris is the capital and largest city of France.',
});
console.log(answers[0].answer); // "Paris"

const model = transformers.classifier('Xenova/distilbert-base-uncased-finetuned-sst-2-english', {
  quantized: true, // Use quantized model (smaller, faster)
  revision: 'main', // Model revision
});

const result = await classify({
  model: transformers.classifier('Xenova/model'),
  text: 'Hello world',
  providerOptions: {
    transformers: {
      // Any Transformers.js specific options
    },
  },
});

// Models are automatically cached after first load
const classifier = transformers.classifier(
  'Xenova/distilbert-base-uncased-finetuned-sst-2-english'
);

// First call downloads the model
const result = await classify({ model: classifier, text: 'Hello' });

// Subsequent calls are instant (loaded from cache)
const result2 = await classify({ model: classifier, text: 'World' });