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NextBrain AI

Convenient access to the NextBrain AI API from python

Installation

pip install nextbrain

If you want to use the async version you need to install asyncio and aiohttp:

pip install asyncio aiohttp

Normal usage

All steps in one.

from nextbrain import NextBrain
from typing import Any, List

def main():
    nb = NextBrain('<YOUR-ACCESS-TOKEN-HERE>')

    # You can create your custom table and predict table by your own from any source
    # It is a list of list, where the first row contains the header
    # Example:
    # [
    #   [ Column1, Column2, Column3 ],
    #   [       1,       2,       3 ],
    #   [       4,       5,       6 ]
    # ]
    table: List[List[Any]] = nb.load_csv('<PATH-TO-YOUR-TRAINING-CSV>')
    predict_table: List[List[Any]] = nb.load_csv('<PATH-TO-YOUR-PREDICTING-CSV>')

    model_id, response = nb.upload_and_predict(table, predict_table, '<YOUR-TARGET-COLUMN>')
    # model_id is also returned in order to predict multiple times against same model
    print(response)

if __name__ == '__main__':
    main()

Step by step

from nextbrain import NextBrain
from typing import Any, List

def main():
    nb = NextBrain('<YOUR-ACCESS-TOKEN-HERE>')

    # You can create your custom table and predict table by your own from any source
    table: List[List[Any]] = nb.load_csv('<PATH-TO-YOUR-TRAINING-CSV>')
    # Upload the model to NextBrain service
    model_id: str = nb.upload_model(table)
    # Train the model
    # You can re-train a previous model
    nb.train_model(model_id, '<YOUR-TARGET-COLUMN>')

    predict_table: List[List[Any]] = nb.load_csv('<PATH-TO-YOUR-PREDICTING-CSV>')
    # You can predict multiple using the same model (don't need to create a new model each time)
    response = nb.predict_model(model_id, predict_table)
    print(response)

if __name__ == '__main__':
    main()

Async usage

All steps in one.

from nextbrain import AsyncNextBrain
from typing import Any, List

async def main():
    nb = AsyncNextBrain('<YOUR-ACCESS-TOKEN-HERE>')

    # You can create your custom table and predict table by your own from any source
    table: List[List[Any]] = nb.load_csv('<PATH-TO-YOUR-TRAINING-CSV>')
    predict_table: List[List[Any]] = nb.load_csv('<PATH-TO-YOUR-PREDICTING-CSV>')

    model_id, response = await nb.upload_and_predict(table, predict_table, '<YOUR-TARGET-COLUMN>')
    # model_id is also returned in order to predict multiple times against same model
    print(response)

if __name__ == '__main__':
    import asyncio
    asyncio.run(main())

Step by step

from nextbrain import AsyncNextBrain
from typing import Any, List

async def main():
    nb = AsyncNextBrain('<YOUR-ACCESS-TOKEN-HERE>')

    # You can create your custom table and predict table by your own from any source
    table: List[List[Any]] = nb.load_csv('<PATH-TO-YOUR-TRAINING-CSV>')
    # Upload the model to NextBrain service
    model_id: str = await nb.upload_model(table)
    # Train the model
    # You can re-train a previous model
    await nb.train_model(model_id, '<YOUR-TARGET-COLUMN>')

    predict_table: List[List[Any]] = nb.load_csv('<PATH-TO-YOUR-PREDICTING-CSV>')
    # You can predict multiple using the same model (don't need to create a new model each time)
    response = await nb.predict_model(model_id, predict_table)
    print(response)

if __name__ == '__main__':
    import asyncio
    asyncio.run(main())

Extra notes

Everytime you train, you can select an option to create lightning models. is_lightning is an optional parameter that by default is set to False but can be overrided in train_model and upload_and_predict.

We also recommend that you investigate all the methods that the class provides you with to make the most of the functionalities we offer. For example, you can use the get_accuracy method to obtain all the information about the performance of your model.