# Web AI Toolkit

The Web AI Toolkit simplifies the integration of AI features, such as OCR, speech-to-text, text summarization and more into your application. It ensures data privacy and offline capability by running all AI workloads locally, leveraging WebNN when available, with a fallback to WebGPU.

## Installation

To install the Web AI Toolkit, run:

```sh
npm install web-ai-toolkit
```

## Available Functions

*Note: Supported hardware is listed in priority of device selection. For example, for transcribing an audio file,
the code will attempt to choose the GPU first and then the CPU otherwise.*

| Function Name         | Parameter      | Type                   | Default Value | Supported Hardware |
|-----------------------|----------------|------------------------|---------------|--------------------|
| transcribeAudioFile   | audioFile      | Blob                   | -             | GPU / CPU               |
|                       | model          | string                 | "Xenova/whisper-tiny"|                    |
|                       | timestamps     | boolean                | false         |                    |
|                       | language       | string                 | "en-US"       |                    |
| textToSpeech          | text           | string                 | -             | GPU / CPU               |
|                       | model          | string                 | "Xenova/mms-tts-eng"|                    |
| summarize             | text           | string                 | -             | GPU / CPU               |
|                       | model          | string                 | "Xenova/distilbart-cnn-6-6"|                |
| ocr                   | image          | Blob                   | -             | GPU / CPU               |
|                       | model          | string                 | "Xenova/trocr-small-printed"|                 |
| classifyImage         | image          | Blob                   | -             | NPU / GPU / CPU               |
|                       | model          | string                 | "Xenova/resnet-50"|                 |
| doRAGSearch           | texts          | Array<string>          | []            | GPU
|                       | query          | string                 | ""            |                      |

## Usage

Here are examples of how to use each function:

### RAG (Retrieval-Augmented Generation)

```javascript
import { doRAGSearch } from 'web-ai-toolkit';

window.showOpenFilePicker().then(async (file) => {
    const fileBlob = await file[0].getFile();
    const text = await fileBlob.text();

    // text can be derived from anything
    // this sample is just meant to be extremely simple
    // for example, your text could be an array of text that you have OCR'ed
    // from some photos

    const query = "My Search Query";
    const ragQuery = await doRAGSearch([text], query);
    console.log(ragQuery);
});
```

### Transcribe Audio File

```javascript
import { transcribeAudioFile } from 'web-ai-toolkit';

const audioFile = ...; // Your audio file Blob
const transcription = await transcribeAudioFile(audioFile, "Xenova/whisper-tiny", true, "en-US");
console.log(transcription);
```

### Text to Speech

```javascript
import { textToSpeech } from 'web-ai-toolkit';

const text = "Hello, world!";
const audio = await textToSpeech(text);
console.log(audio);
```

### Summarize Text

```javascript
import { summarize } from 'web-ai-toolkit';

const text = "Long text to be summarized...";
const summary = await summarize(text);
console.log(summary);
```

### Optical Character Recognition (OCR)

```javascript
import { ocr } from 'web-ai-toolkit';

const image = ...; // Your image Blob
const text = await ocr(image);
console.log(text);
```

### Image Classification

```javascript
import { classifyImage } from 'web-ai-toolkit';

const image = ...; // Your image Blob
const text = await classifyImage(image);
console.log(text);
```

## Technical Details

The Web AI Toolkit utilizes the [transformers.js project](https://huggingface.co/docs/transformers.js/index) to run AI workloads. All AI processing is performed locally on the device, ensuring data privacy and reducing latency. AI workloads are run using the [WebNN API](https://learn.microsoft.com/en-us/windows/ai/directml/webnn-overview) when available, otherwise falling back to the WebGPU API, or even to the CPU with WebAssembly. Choosing the correct hardware to target is handled by the library.

## Contribution

We welcome contributions to the Web AI Toolkit. Please fork the repository and submit a pull request with your changes. For major changes, please open an issue first to discuss what you would like to change.

## License

The Web AI Toolkit is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.

## Contact

For questions or support, please open an issue here on GitHub

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Thank you for using the Web AI Toolkit! We hope it makes integrating AI into your applications easier and more efficient.