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import pandas as pd
import joblib
from io import StringIO
import os
import numpy as np
import logging

from sagemaker_containers.beta.framework import (
    encoders, worker)

logger = logging.getLogger()
logger.setLevel(logging.INFO)
logger.addHandler(logging.StreamHandler())

XGBOOST_CONTENT_TYPE='text/csv'

feature_columns_names = [
    "sex",
    "length",
    "diameter",
    "height",
    "whole_weight",
    "shucked_weight",
    "viscera_weight",
    "shell_weight",
]

label_column = "rings"

def input_fn(input_data, content_type):
    """Parse input data payload

    We currently only take csv input. Since we need to process both labelled
    and unlabelled data we first determine whether the label column is present
    by looking at how many columns were provided.
    """
    logger.info(f"input data {input_data} with format {content_type}")

    if content_type == 'text/csv':
        # Read the raw input data as CSV.
        df = pd.read_csv(StringIO(input_data), 
                         header=None)

        if len(df.columns) == len(feature_columns_names) + 1:
            # This is a labelled example, includes the ring label
            df.columns = feature_columns_names + [label_column]
        elif len(df.columns) == len(feature_columns_names):
            # This is an unlabelled example.
            df.columns = feature_columns_names

        return df
    else:
        raise ValueError("{} not supported by script!".format(content_type))

def output_fn(prediction, accept):
    """Format prediction output.
       XGBoost only support text/csv and text/libsvm. Use text/csv here. 
    """
    logger.info(f"output data {prediction}")

    return worker.Response(encoders.encode(prediction, XGBOOST_CONTENT_TYPE), XGBOOST_CONTENT_TYPE, mimetype=XGBOOST_CONTENT_TYPE)

def predict_fn(input_data, model):
    """Preprocess input data

    We implement this because the default predict_fn uses .predict(), but our model is a preprocessor
    so we want to use .transform().

    The output is returned in the following order:

        rest of features either one hot encoded or standardized
    """
    features = model.transform(input_data)

    if label_column in input_data:
        # Return the label (as the first column) and the set of features.
        return np.insert(features, 0, input_data[label_column], axis=1)
    else:
        # Return only the set of features
        return features

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