import sys
import models.pipelines.neural_word2vec_pipeline as nn
import utility.util as ut

def main():
    """
    Entry point
    """

    if len(sys.argv) == 4:
        database_filepath, table_name, model_filepath = sys.argv[1:]
        
        print('Create and Fit Pipeline...')
        model = nn.NeuralWord2VecPipeline(database_filepath, 'Disaster')
        model.init_fit_eval()

        print('Saving pipeline...\n    PIPELINE: {}'.format(model_filepath))
        ut.to_pkl(model, model_filepath)
        print('Trained pipeline saved!')

    else:
        print('Please provide the filepath of the disaster messages database '\
              'as the first argument, the table name as the second argument, and'
              ' the filepath of the pickle file to save the model to as the third'
              ' argument. \n\nExample: python -m models.train_classifier '
              'data/DisasterResponse.db Disaster models/model.pkl')


if __name__ == '__main__':
    main()
809a7d0f63c6e847f9ea0a694da59f8bf44e490b
<mask token>

<mask token>


def main():
    """
    Entry point
    """
    if len(sys.argv) == 4:
        database_filepath, table_name, model_filepath = sys.argv[1:]
        print('Create and Fit Pipeline...')
        model = nn.NeuralWord2VecPipeline(database_filepath, 'Disaster')
        model.init_fit_eval()
        print('Saving pipeline...\n    PIPELINE: {}'.format(model_filepath))
        ut.to_pkl(model, model_filepath)
        print('Trained pipeline saved!')
    else:
        print(
            """Please provide the filepath of the disaster messages database as the first argument, the table name as the second argument, and the filepath of the pickle file to save the model to as the third argument. 

Example: python -m models.train_classifier data/DisasterResponse.db Disaster models/model.pkl"""
            )


<mask token>

<mask token>


def main():
    """
    Entry point
    """
    if len(sys.argv) == 4:
        database_filepath, table_name, model_filepath = sys.argv[1:]
        print('Create and Fit Pipeline...')
        model = nn.NeuralWord2VecPipeline(database_filepath, 'Disaster')
        model.init_fit_eval()
        print('Saving pipeline...\n    PIPELINE: {}'.format(model_filepath))
        ut.to_pkl(model, model_filepath)
        print('Trained pipeline saved!')
    else:
        print(
            """Please provide the filepath of the disaster messages database as the first argument, the table name as the second argument, and the filepath of the pickle file to save the model to as the third argument. 

Example: python -m models.train_classifier data/DisasterResponse.db Disaster models/model.pkl"""
            )


if __name__ == '__main__':
    main()

import sys
import models.pipelines.neural_word2vec_pipeline as nn
import utility.util as ut


def main():
    """
    Entry point
    """
    if len(sys.argv) == 4:
        database_filepath, table_name, model_filepath = sys.argv[1:]
        print('Create and Fit Pipeline...')
        model = nn.NeuralWord2VecPipeline(database_filepath, 'Disaster')
        model.init_fit_eval()
        print('Saving pipeline...\n    PIPELINE: {}'.format(model_filepath))
        ut.to_pkl(model, model_filepath)
        print('Trained pipeline saved!')
    else:
        print(
            """Please provide the filepath of the disaster messages database as the first argument, the table name as the second argument, and the filepath of the pickle file to save the model to as the third argument. 

Example: python -m models.train_classifier data/DisasterResponse.db Disaster models/model.pkl"""
            )


if __name__ == '__main__':
    main()

import sys
import models.pipelines.neural_word2vec_pipeline as nn
import utility.util as ut

def main():
    """
    Entry point
    """

    if len(sys.argv) == 4:
        database_filepath, table_name, model_filepath = sys.argv[1:]
        
        print('Create and Fit Pipeline...')
        model = nn.NeuralWord2VecPipeline(database_filepath, 'Disaster')
        model.init_fit_eval()

        print('Saving pipeline...\n    PIPELINE: {}'.format(model_filepath))
        ut.to_pkl(model, model_filepath)
        print('Trained pipeline saved!')

    else:
        print('Please provide the filepath of the disaster messages database '\
              'as the first argument, the table name as the second argument, and'
              ' the filepath of the pickle file to save the model to as the third'
              ' argument. \n\nExample: python -m models.train_classifier '
              'data/DisasterResponse.db Disaster models/model.pkl')


if __name__ == '__main__':
    main()