import { HfInference } from '@huggingface/inference'

const hf = new HfInference('your access token')

// Natural Language

await hf.fillMask({
  model: 'bert-base-uncased',
  inputs: '[MASK] world!'
})

await hf.summarization({
  model: 'facebook/bart-large-cnn',
  inputs:
    'The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930.',
  parameters: {
    max_length: 100
  }
})

await hf.questionAnswering({
  model: 'deepset/roberta-base-squad2',
  inputs: {
    question: 'What is the capital of France?',
    context: 'The capital of France is Paris.'
  }
})

await hf.tableQuestionAnswering({
  model: 'google/tapas-base-finetuned-wtq',
  inputs: {
    query: 'How many stars does the transformers repository have?',
    table: {
      Repository: ['Transformers', 'Datasets', 'Tokenizers'],
      Stars: ['36542', '4512', '3934'],
      Contributors: ['651', '77', '34'],
      'Programming language': ['Python', 'Python', 'Rust, Python and NodeJS']
    }
  }
})

await hf.textClassification({
  model: 'distilbert-base-uncased-finetuned-sst-2-english',
  inputs: 'I like you. I love you.'
})

await hf.textGeneration({
  model: 'gpt2',
  inputs: 'The answer to the universe is'
})

for await (const output of hf.textGenerationStream({
  model: "google/flan-t5-xxl",
  inputs: 'repeat "one two three four"',
  parameters: { max_new_tokens: 250 }
})) {
  console.log(output.token.text, output.generated_text);
}

await hf.tokenClassification({
  model: 'dbmdz/bert-large-cased-finetuned-conll03-english',
  inputs: 'My name is Sarah Jessica Parker but you can call me Jessica'
})

await hf.translation({
  model: 't5-base',
  inputs: 'My name is Wolfgang and I live in Berlin'
})

await hf.zeroShotClassification({
  model: 'facebook/bart-large-mnli',
  inputs: [
    'Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!'
  ],
  parameters: { candidate_labels: ['refund', 'legal', 'faq'] }
})

await hf.conversational({
  model: 'microsoft/DialoGPT-large',
  inputs: {
    past_user_inputs: ['Which movie is the best ?'],
    generated_responses: ['It is Die Hard for sure.'],
    text: 'Can you explain why ?'
  }
})

await hf.sentenceSimilarity({
  model: 'sentence-transformers/paraphrase-xlm-r-multilingual-v1',
  inputs: {
    source_sentence: 'That is a happy person',
    sentences: [
      'That is a happy dog',
      'That is a very happy person',
      'Today is a sunny day'
    ]
  }
})

await hf.featureExtraction({
  model: "sentence-transformers/distilbert-base-nli-mean-tokens",
  inputs: "That is a happy person",
});

// Audio

await hf.automaticSpeechRecognition({
  model: 'facebook/wav2vec2-large-960h-lv60-self',
  data: readFileSync('test/sample1.flac')
})

await hf.audioClassification({
  model: 'superb/hubert-large-superb-er',
  data: readFileSync('test/sample1.flac')
})

// Computer Vision

await hf.imageClassification({
  data: readFileSync('test/cheetah.png'),
  model: 'google/vit-base-patch16-224'
})

await hf.objectDetection({
  data: readFileSync('test/cats.png'),
  model: 'facebook/detr-resnet-50'
})

await hf.imageSegmentation({
  data: readFileSync('test/cats.png'),
  model: 'facebook/detr-resnet-50-panoptic'
})

await hf.textToImage({
  inputs: 'award winning high resolution photo of a giant tortoise/((ladybird)) hybrid, [trending on artstation]',
  model: 'stabilityai/stable-diffusion-2',
  parameters: {
    negative_prompt: 'blurry',
  }
})

await hf.imageToText({
  data: readFileSync('test/cats.png'),
  model: 'nlpconnect/vit-gpt2-image-captioning'
})

// Multimodal

await hf.visualQuestionAnswering({
  model: 'dandelin/vilt-b32-finetuned-vqa',
  inputs: {
    question: 'How many cats are lying down?',
    image: await (await fetch('https://placekitten.com/300/300')).blob()
  }
})

await hf.documentQuestionAnswering({
  model: 'impira/layoutlm-document-qa',
  inputs: {
    question: 'Invoice number?',
    image: await (await fetch('https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png')).blob(),
  }
})

// Custom call, for models with custom parameters / outputs
await hf.request({
  model: 'my-custom-model',
  inputs: 'hello world',
  parameters: {
    custom_param: 'some magic',
  }
})

// Custom streaming call, for models with custom parameters / outputs
for await (const output of hf.streamingRequest({
  model: 'my-custom-model',
  inputs: 'hello world',
  parameters: {
    custom_param: 'some magic',
  }
})) {
  ...
}

// Using your own inference endpoint: https://hf.co/docs/inference-endpoints/
const gpt2 = hf.endpoint('https://xyz.eu-west-1.aws.endpoints.huggingface.cloud/gpt2');
const { generated_text } = await gpt2.textGeneration({inputs: 'The answer to the universe is'});

import {textGeneration} from "@huggingface/inference";

await textGeneration({
  accessToken: "hf_...",
  model: "model_or_endpoint",
  inputs: ...,
  parameters: ...
})