  from fate import Pipeline, DataReader, DataTransformer, Scaler, LogisticRegression, Evaluator, MultiPartyComponent

def main(config_file):
    with open(config_file, 'r') as f:
        config = json.load(f)

    # Create a pipeline for the guest
    guest_pipeline = Pipeline()
    guest_pipeline.add(DataReader(config['guest']['data_table']))
    guest_pipeline.add(DataTransformer(config['guest']['transformations']))
    guest_pipeline.add(Scaler(config['guest']['scaling']))
    guest_pipeline.add(LogisticRegression(config['guest']['model']))
    guest_pipeline.add(Evaluator(config['guest']['evaluation']))

    # Create a pipeline for the host
    host_pipeline = Pipeline()
    host_pipeline.add(DataReader(config['host']['data_table']))
    host_pipeline.add(DataTransformer(config['host']['transformations']))
    host_pipeline.add(Scaler(config['host']['scaling']))
    host_pipeline.add(LogisticRegression(config['host']['model']))
    host_pipeline.add(Evaluator(config['host']['evaluation']))

    # Create a pipeline for the arbiter
    arbiter_pipeline = Pipeline()
    arbiter_pipeline.add(DataReader(config['arbiter']['data_table']))
    arbiter_pipeline.add(DataTransformer(config['arbiter']['transformations']))
    arbiter_pipeline.add(Scaler(config['arbiter']['scaling']))
    arbiter_pipeline.add(LogisticRegression(config['arbiter']['model']))
    arbiter_pipeline.add(Evaluator(config['arbiter']['evaluation']))

    # Create a multi-party component for the guest, host, and arbiter
    multi_party_component = MultiPartyComponent(guest_pipeline, host_pipeline, arbiter_pipeline)

    # Compile and fit the multi-party component
    multi_party_component.compile()
    multi_party_component.fit()

    # Deploy the selected components from the training pipeline
    guest_component = multi_party_component.get_guest_component()
    host_component = multi_party_component.get_host_component()
    arbiter_component = multi_party_component.get_arbiter_component()

    # Create a prediction pipeline for the guest
    guest_prediction_pipeline = Pipeline()
    guest_prediction_pipeline.add(DataReader(config['guest']['data_table']))
    guest_prediction_pipeline.add(guest_component)

    # Create a prediction pipeline for the host
    host_prediction_pipeline = Pipeline()
    host_prediction_pipeline.add(DataReader(config['host']['data_table']))
    host_prediction_pipeline.add(host_component)

    # Create a prediction pipeline for the arbiter
    arbiter_prediction_pipeline = Pipeline()
    arbiter_prediction_pipeline.add(DataReader(config['arbiter']['data_table']))
    arbiter_prediction_pipeline.add(arbiter_component)

    # Compile and use the prediction pipelines to make predictions
    guest_prediction_pipeline.compile()
    guest_prediction_pipeline.predict()
    host_prediction_pipeline.compile()
    host_prediction_pipeline.predict()
    arbiter_prediction_pipeline.compile()
    arbiter_prediction_pipeline.predict()

    # Save the DSL and configuration of the prediction pipelines as JSON files
    with open('guest_prediction_pipeline.json', 'w') as f:
        json.dump(guest_prediction_pipeline.dsl, f)
    with open('host_prediction_pipeline.json', 'w') as f:
        json.dump(host_prediction_pipeline.dsl, f)
    with open('arbiter_prediction_pipeline.json', 'w') as f:
        json.dump(arbiter_prediction_pipeline.dsl, f)

    # Print the summaries of the logistic regression and evaluation components
    print(guest_pipeline.get_component('logistic_regression').summary())
    print(guest_pipeline.get_component('evaluator').summary())
    print(host_pipeline.get_component('logistic_regression').summary())
    print(host_pipeline.get_component('evaluator').summary())
    print(arbiter_pipeline.get_component('logistic_regression').summary())
    print(arbiter_pipeline.get_component('evaluator').summary())

if __name__ == '__main__':
    main('config.json')