> ## Documentation Index
> Fetch the complete documentation index at: https://landinglens.docs.landing.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Developer Tools

export const vp = 'Visual Prompting';

export const smartLabel = 'Smart Labeling';

export const productLL = 'LandingLens';

export const mi = 'Mobile Inference';

export const llsf = 'LandingLens on Snowflake';

export const companyName = 'LandingAI';

*This article applies to these versions of LandingLens:*

<table>
  <thead>
    <tr>
      <th>LandingLens</th>
      <th>LandingLens on Snowflake</th>
    </tr>
  </thead>

  <tbody>
    <tr>
      <td><span class="check-icon">✓</span></td>
      <td><span class="check-icon">✓</span> (see exceptions below)</td>
    </tr>
  </tbody>
</table>

{companyName} offers these developer tools to accelerate your deployment process and heighten your creativity

* [REST APIs](https://landing-ai.github.io/public-rest-api/fastapi/#/operations/create_image_v1_projects__project_id__images_post) (not supported for {llsf})
* [Python library](https://github.com/landing-ai/landingai-python)
* [JavaScript library](https://github.com/landing-ai/landingai-js) (not supported for {llsf})

The APIs and libraries include practical examples of how to run inference using models you've developed in LandingLens. The {companyName} libraries are available in [GitHub](https://github.com/landing-ai/).

<Info>We plan to add a C# library in the future.</Info>

## REST APIs

Use the [REST APIs](https://landing-ai.github.io/public-rest-api/fastapi/#/operations/create_image_v1_projects__project_id__images_post) to perform many tasks, including:

* Upload images to LandingLens.
* Create projects.
* Create classes.
* Assign split keys (Dev, Train, Test) to images.
* Train models.
* Deploy models.

<Info>The REST APIs don't support {llsf}.</Info>

## Python Library

Use the [Python library](https://github.com/landing-ai/landingai-python) to:

* Upload labeled and unlabeled images to LandingLens.
* Capture images from various sources (video files, webcams, RTSP streams, etc.).
* Assign metadata values and split keys (Dev, Train, Test) to images.
* Get prediction results from your deployed model.
* Post-process your prediction results into other formats.
* Visualize your prediction results.
* Chain multiple model inference and post-processing operations together.

To learn more, check out these resources:

* [Python repository](https://github.com/landing-ai/landingai-python)
* [Python documentation](https://landing-ai.github.io/landingai-python)

### Using the Python Library with {llsf}

The Python library offers limited support for {llsf}. The Python library can be used to [run inference](https://landing-ai.github.io/landingai-python/inferences/getting-started/) and perform [image operations](https://landing-ai.github.io/landingai-python/image-operations/) like cropping and resizing images. However, it doesn't support other functions for interacting with data on {llsf}, like uploading images and assigning splits.

## JavaScript Library

Use the JavaScript library to:

* Get prediction results from your deployed model.
* Visualize your prediction results.
* Upload unlabeled images from your app.

To learn more, check out the [JavaScript repository](https://github.com/landing-ai/landingai-js).

<Info>The JavaScript library doesn't support {llsf}.</Info>