import os
os.environ["TF_CPP_MIN_LOG_LEVEL"]="2"

import tensorflow as tf
import numpy as np

## TODO
## - Move learning rate
## - Save model, restore model
class NNModel():
    def __init__(self, layers, num_classes):
        print("In NNModel constructor")
        self.layers = layers
        self.num_classes = num_classes
        self.learning_rate = 0.1
        self.model = tf.estimator.Estimator(self.build)

    def build_layers(self, dictionary):
        tmp = dictionary["x"]
        for layer in self.layers:
            tmp = tf.layers.dense(tmp, layer)
        tmp = tf.layers.dense(tmp, self.num_classes)
        return tmp

    def train(self, input_fn, num_steps):
        self.model.train(input_fn, steps = num_steps)

    def evaluate(self, input_fn):
        return self.model.evaluate(input_fn)

    def predict(self, input_fn):
        return self.model.predict(input_fn)

    def build(self, features, labels, mode):
        logits = self.build_layers(features)

        pred_classes = tf.argmax(logits, axis=1)
        pred_probas = tf.nn.softmax(logits)

        if mode == tf.estimator.ModeKeys.PREDICT:
            return tf.estimator.EstimatorSpec(mode, predictions=pred_classes)

        loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
            logits=logits, labels=tf.cast(labels, dtype=tf.int32)))
        optimizer = tf.train.GradientDescentOptimizer(learning_rate=self.learning_rate)
        train_op = optimizer.minimize(loss_op, global_step=tf.train.get_global_step())

        # Evaluate the accuracy of the model
        acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes)

        # TF Estimators requires to return a EstimatorSpec, that specify
        # the different ops for training, evaluating, ...
        estim_specs = tf.estimator.EstimatorSpec(
            mode=mode,
            predictions=pred_classes,
            loss=loss_op,
            train_op=train_op,
            eval_metric_ops={'accuracy': acc_op})
        return estim_specs