nextbrain 0.2.4

Convenient access to the NextBrain API from python

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
    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[0], predict_table[1:])
    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[0], predict_table[1:])
    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.