# fmt: off
# __doc_import_begin__
from ray import serve

import os
import tempfile
import numpy as np

import tensorflow as tf
# __doc_import_end__
# fmt: on

# __doc_train_model_begin__
TRAINED_MODEL_PATH = os.path.join(tempfile.gettempdir(), "mnist_model.h5")


def train_and_save_model():
    # Load mnist dataset
    mnist = tf.keras.datasets.mnist
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_train, x_test = x_train / 255.0, x_test / 255.0

    # Train a simple neural net model
    model = tf.keras.models.Sequential(
        [
            tf.keras.layers.Flatten(input_shape=(28, 28)),
            tf.keras.layers.Dense(128, activation="relu"),
            tf.keras.layers.Dropout(0.2),
            tf.keras.layers.Dense(10),
        ]
    )
    loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
    model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"])
    model.fit(x_train, y_train, epochs=1)

    model.evaluate(x_test, y_test, verbose=2)
    model.summary()

    # Save the model in h5 format in local file system
    model.save(TRAINED_MODEL_PATH)


if not os.path.exists(TRAINED_MODEL_PATH):
    train_and_save_model()
# __doc_train_model_end__


# __doc_define_servable_begin__
@serve.deployment(route_prefix="/mnist")
class TFMnistModel:
    def __init__(self, model_path):
        import tensorflow as tf

        self.model_path = model_path
        self.model = tf.keras.models.load_model(model_path)

    async def __call__(self, starlette_request):
        # Step 1: transform HTTP request -> tensorflow input
        # Here we define the request schema to be a json array.
        input_array = np.array((await starlette_request.json())["array"])
        reshaped_array = input_array.reshape((1, 28, 28))

        # Step 2: tensorflow input -> tensorflow output
        prediction = self.model(reshaped_array)

        # Step 3: tensorflow output -> web output
        return {"prediction": prediction.numpy().tolist(), "file": self.model_path}
        # __doc_define_servable_end__


# __doc_deploy_begin__
app = TFMnistModel.bind(TRAINED_MODEL_PATH)
# __doc_deploy_end__