04_deploy_model/sklearn_source_dir/inference.py [14:48]:
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    content_types, encoders, env, modules, transformer, worker)

feature_columns_names = ['Type', 'Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]']

def input_fn(input_data, content_type):
    print(input_data)
    
    if content_type == 'text/csv':
        df = pd.read_csv(StringIO(input_data), header=None)
        if len(df.columns) == len(feature_columns_names):
            df.columns = feature_columns_names 
        return df
    else:
        raise ValueError("{} not supported by script!".format(content_type))
        
def predict_fn(input_data, model):
    features = model.transform(input_data)
    return features

def output_fn(prediction, accept):
    if accept == "application/json":
        instances = []
        for row in prediction.tolist():
            instances.append({"features": row})
        json_output = {"instances": instances}

        return worker.Response(json.dumps(json_output), mimetype=accept)
    elif accept == 'text/csv':
        return worker.Response(encoders.encode(prediction, accept), mimetype=accept)
    else:
        raise RuntimeException("{} accept type is not supported.".format(accept))

def model_fn(model_dir):
    preprocessor = joblib.load(os.path.join(model_dir, "model.joblib"))
    return preprocessor
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08_projects/modelbuild/pipelines/endtoendmlsm/deploy/sklearn/inference.py [14:48]:
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    content_types, encoders, env, modules, transformer, worker)

feature_columns_names = ['Type', 'Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]']

def input_fn(input_data, content_type):
    print(input_data)
    
    if content_type == 'text/csv':
        df = pd.read_csv(StringIO(input_data), header=None)
        if len(df.columns) == len(feature_columns_names):
            df.columns = feature_columns_names 
        return df
    else:
        raise ValueError("{} not supported by script!".format(content_type))
        
def predict_fn(input_data, model):
    features = model.transform(input_data)
    return features

def output_fn(prediction, accept):
    if accept == "application/json":
        instances = []
        for row in prediction.tolist():
            instances.append({"features": row})
        json_output = {"instances": instances}

        return worker.Response(json.dumps(json_output), mimetype=accept)
    elif accept == 'text/csv':
        return worker.Response(encoders.encode(prediction, accept), mimetype=accept)
    else:
        raise RuntimeException("{} accept type is not supported.".format(accept))

def model_fn(model_dir):
    preprocessor = joblib.load(os.path.join(model_dir, "model.joblib"))
    return preprocessor
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