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https://github.com/danvk/localturk

Mechanical Turk on your own machine.
https://github.com/danvk/localturk

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Mechanical Turk on your own machine.

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# localturk



Local Turk implements Amazon's Mechanical Turk API on your own machine.



It's handy if you want to:



1. Develop a Mechanical Turk template

2. Do some repetitive tasks on your own, without involving Turkers.



You could use it, for instance, to generate test and training data for a Machine Learning algorithm.



## Quick Start



Install:



    npm install -g localturk



Run:



    cd localturk/sample

    localturk transcribe.html tasks.csv outputs.csv



Then visit http://localhost:4321/ to start Turking.



## Templates and Tasks



Using Local Turk is just like using Amazon's Mechanical Turk. You create:



1. An HTML template file with a <form>

2. A CSV file of tasks



For example, say you wanted to record whether some images contained a red ball. You would make a CSV file containing the URLs for each image:



    image_url

    http://example.com/image_with_red_ball.png

    http://example.com/image_without_red_ball.png



Then you'd make an HTML template for the task:



```html



 Has a red ball


 Does not have a red ball


```



Finally, you'd start up the Local Turk server:



    $ localturk path/to/template.html path/to/tasks.csv path/to/output.csv



Now you can visit http://localhost:4321/ to complete each task. When you're done, the output.csv file will contain



    image_url,has_button

    http://example.com/image_with_red_ball.png,yes

    http://example.com/image_without_red_ball.png,no



## Image Classification



The use case described above (classifying images) is an extremely common one.



To expedite this, localturk provides a separate script for doing image

classification. The example above could be written as:



    classify-images --labels 'Has a red ball,Does not have a red ball' *.png



This will bring up a web server with a UI for assigning one of those two labels

to each image on your local file system. The results will go in `output.csv`.



Alternatively, you can also pass in a text file containing the paths to images.

In this case, the images can be URLs, rather than paths to local files.



For more details, run `classify-images --help`.



## Tips & Tricks



It can be hard to remember the exact format for template files. localturk can help! Run it with

the `--write-template` argument to generate a template file for your input that you can edit:



    localturk --write-template tasks.csv > template.html



When you're going through many tasks, keyboard shortcuts can speed things up tremendously.

localturk supports these via the `data-key` attribute on form elements. For example, make yourer

submit button look like this:



    



Now, when you press `d`, it'll automatically click the "Good" button for you. _Note that this

feature is not available on mechanical turk itself!_



If you'd like to reference sensitive data that you'd prefer not to commit to your repo

(an API key, say), you can pass it via a command-line parameter:



    localturk --var API_KEY=123456abcd



Then you can reference this as `${API_KEY}` in your template file.



## Development



To make changes to localturk, clone it and set it up using `yarn`:



    yarn



You can run `localturk.ts` or `classify-images.ts` directly using `ts-node`:



    ts-node localturk.ts path/to/template.html path/to/tasks.csv path/to/output.csv



To type check and run the tests:



    yarn tsc

    yarn test



To publish a new version on npm, run:



    yarn tsc

    yarn publish