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        "description": "# Python wrapper for the machinelearningforkids.co.uk API\n\nThis library was designed for use with [machinelearningforkids.co.uk](https://machinelearningforkids.co.uk).\n\n## Installation\n\nUse pip to install the `ml4k` package:\n\n    sudo pip install ml4k\n\n## Usage\n\nIn order to use your machine learning model, create a `Model` object\nwith your project's API key:\n\n    import ml4k\n\n    API_KEY = \"PASTE-API-KEY-HERE\"\n    model = ml4k.Model(API_KEY)\n\n\nThe following methods are available:\n\n### `model.classify(data)`\n\nClassifies the given data and returns a dictionary with the result. The\nresult dictionary contains the `\"label\"` and `\"confidence\"` values.\n\n\n### `model.add_training_data(label, data)`\n\nAdds training data to the given label \"bucket\". You can pass text,\nimages, or a list of numbers.\n\n\n## Examples\n\n### Recognizing Text\n\n    # Get input from user and pass it to our model\n    command = input(\"Type a command: \")\n    result = model.classify(\"Turn on the lamp\")\n\n    if result[\"label\"] == \"lamp_on\":\n        print(\"Turning on the lamp\")\n    elif result[\"label\"] == \"lamp_off\":\n        print(\"Turning off the lamp\")\n\n\n### Recognizing Images\n\nRecognizing images is similar, but you need to pass binary image data.\nThere are many was to get image data in python, such as opening and\nreading a file, or capturing from the webcam using a the\n[SimpleCV](http://simplecv.org/) library. \n\nImages will be automatically downsized before sending to the API. \n\n    # Assuming you have image data stored as a binary string...\n    result = model.classify(image)\n    if result[\"label\"] == \"dog\":\n        print(\"That's a dog\")\n    elif result[\"label\"] == \"cat\":\n        print(\"That's a cat\")",
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