04_deploy_model/xgboost_source_dir/inference.py [8:41]:
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    content_types, encoders, env, modules, transformer, worker)

from sagemaker_xgboost_container import encoder as xgb_encoders

def input_fn(input_data, content_type):    
    if content_type == content_types.JSON:
        obj = json.loads(input_data)
        features = obj['instances'][0]['features']
        array = np.array(features).reshape((1, -1))
        return xgb.DMatrix(array)
    else:
        return xgb_encoders.decode(input_data, content_type)

def model_fn(model_dir):
    model_file = model_dir + '/model.bin'
    model = pkl.load(open(model_file, 'rb'))
    return model

def output_fn(prediction, accept):
    
    pred_array_value = np.array(prediction)
    score = pred_array_value[0]
    
    if accept == "application/json":
        predicted_label = 1 if score > 0.5 else 0
        return_value = {
            'predictions': [{'score': score.astype(float), 'predicted_label': predicted_label }]
        }
        return worker.Response(json.dumps(return_value), mimetype=accept)
    elif accept == 'text/csv':
        return_value = 'yes' if score > 0.5 else 'no'
        return worker.Response(encoders.encode(prediction, accept), mimetype=accept)
    else:
        raise RuntimeException("{} accept type is not supported.".format(accept))
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08_projects/modelbuild/pipelines/endtoendmlsm/deploy/xgboost/inference.py [8:41]:
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    content_types, encoders, env, modules, transformer, worker)

from sagemaker_xgboost_container import encoder as xgb_encoders

def input_fn(input_data, content_type):    
    if content_type == content_types.JSON:
        obj = json.loads(input_data)
        features = obj['instances'][0]['features']
        array = np.array(features).reshape((1, -1))
        return xgb.DMatrix(array)
    else:
        return xgb_encoders.decode(input_data, content_type)

def model_fn(model_dir):
    model_file = model_dir + '/model.bin'
    model = pkl.load(open(model_file, 'rb'))
    return model

def output_fn(prediction, accept):
    
    pred_array_value = np.array(prediction)
    score = pred_array_value[0]
    
    if accept == "application/json":
        predicted_label = 1 if score > 0.5 else 0
        return_value = {
            'predictions': [{'score': score.astype(float), 'predicted_label': predicted_label }]
        }
        return worker.Response(json.dumps(return_value), mimetype=accept)
    elif accept == 'text/csv':
        return_value = 'yes' if score > 0.5 else 'no'
        return worker.Response(encoders.encode(prediction, accept), mimetype=accept)
    else:
        raise RuntimeException("{} accept type is not supported.".format(accept))
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