#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
Explanation: Copyright 2020 The TensorFlow Authors.
End of explanation
"""


import sys

# Confirm that we're using Python 3
assert sys.version_info.major == 3, 'Oops, not running Python 3. Use Runtime > Change runtime type'

# TensorFlow and tf.keras
print("Installing dependencies for Colab environment")
!pip install -Uq grpcio==1.26.0

import tensorflow as tf
from tensorflow import keras

# Helper libraries
import numpy as np
import matplotlib.pyplot as plt
import os
import subprocess

print('TensorFlow version: {}'.format(tf.__version__))

"""
Explanation: Train and serve a TensorFlow model with TensorFlow Serving
Warning: This notebook is designed to be run in a Google Colab only.  It installs packages on the system and requires root access.  If you want to run it in a local Jupyter notebook, please proceed with caution.
Note: You can run this example right now in a Jupyter-style notebook, no setup required!  Just click "Run in Google Colab"
<div class="devsite-table-wrapper"><table class="tfo-notebook-buttons" align="left">
<tr><td><a target="_blank" href="https://www.tensorflow.org/tfx/tutorials/serving/rest_simple">
<img src="https://www.tensorflow.org/images/tf_logo_32px.png" />View on TensorFlow.org</a></td>
<td><a target="_blank" href="https://colab.research.google.com/github/tensorflow/tfx/blob/master/docs/tutorials/serving/rest_simple.ipynb">
<img src="https://www.tensorflow.org/images/colab_logo_32px.png">Run in Google Colab</a></td>
<td><a target="_blank" href="https://github.com/tensorflow/tfx/blob/master/docs/tutorials/serving/rest_simple.ipynb">
<img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png">View source on GitHub</a></td>
<td><a href="https://storage.googleapis.com/tensorflow_docs/tfx/docs/tutorials/serving/rest_simple.ipynb"><img src="https://www.tensorflow.org/images/download_logo_32px.png" />Download notebook</a></td>
</tr></table></div>

This guide trains a neural network model to classify images of clothing, like sneakers and shirts, saves the trained model, and then serves it with TensorFlow Serving.  The focus is on TensorFlow Serving, rather than the modeling and training in TensorFlow, so for a complete example which focuses on the modeling and training see the Basic Classification example.
This guide uses tf.keras, a high-level API to build and train models in TensorFlow.
End of explanation
"""


fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

# scale the values to 0.0 to 1.0
train_images = train_images / 255.0
test_images = test_images / 255.0

# reshape for feeding into the model
train_images = train_images.reshape(train_images.shape[0], 28, 28, 1)
test_images = test_images.reshape(test_images.shape[0], 28, 28, 1)

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

print('\ntrain_images.shape: {}, of {}'.format(train_images.shape, train_images.dtype))
print('test_images.shape: {}, of {}'.format(test_images.shape, test_images.dtype))

"""
Explanation: Create your model
Import the Fashion MNIST dataset
This guide uses the Fashion MNIST dataset which contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 by 28 pixels), as seen here:
<table>
  <tr><td>
    <img src="https://tensorflow.org/images/fashion-mnist-sprite.png"
         alt="Fashion MNIST sprite"  width="600">
  </td></tr>
  <tr><td align="center">
    <b>Figure 1.</b> <a href="https://github.com/zalandoresearch/fashion-mnist">Fashion-MNIST samples</a> (by Zalando, MIT License).<br/>&nbsp;
  </td></tr>
</table>

Fashion MNIST is intended as a drop-in replacement for the classic MNIST dataset—often used as the "Hello, World" of machine learning programs for computer vision. You can access the Fashion MNIST directly from TensorFlow, just import and load the data.
Note: Although these are really images, they are loaded as NumPy arrays and not binary image objects.
End of explanation
"""


model = keras.Sequential([
  keras.layers.Conv2D(input_shape=(28,28,1), filters=8, kernel_size=3, 
                      strides=2, activation='relu', name='Conv1'),
  keras.layers.Flatten(),
  keras.layers.Dense(10, name='Dense')
])
model.summary()

testing = False
epochs = 5

model.compile(optimizer='adam', 
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=[keras.metrics.SparseCategoricalAccuracy()])
model.fit(train_images, train_labels, epochs=epochs)

test_loss, test_acc = model.evaluate(test_images, test_labels)
print('\nTest accuracy: {}'.format(test_acc))

"""
Explanation: Train and evaluate your model
Let's use the simplest possible CNN, since we're not focused on the modeling part.
End of explanation
"""


# Fetch the Keras session and save the model
# The signature definition is defined by the input and output tensors,
# and stored with the default serving key
import tempfile

MODEL_DIR = tempfile.gettempdir()
version = 1
export_path = os.path.join(MODEL_DIR, str(version))
print('export_path = {}\n'.format(export_path))

tf.keras.models.save_model(
    model,
    export_path,
    overwrite=True,
    include_optimizer=True,
    save_format=None,
    signatures=None,
    options=None
)

print('\nSaved model:')
!ls -l {export_path}

"""
Explanation: Save your model
To load our trained model into TensorFlow Serving we first need to save it in SavedModel format.  This will create a protobuf file in a well-defined directory hierarchy, and will include a version number.  TensorFlow Serving allows us to select which version of a model, or "servable" we want to use when we make inference requests.  Each version will be exported to a different sub-directory under the given path.
End of explanation
"""


!saved_model_cli show --dir {export_path} --all

"""
Explanation: Examine your saved model
We'll use the command line utility saved_model_cli to look at the MetaGraphDefs (the models) and SignatureDefs (the methods you can call) in our SavedModel.  See this discussion of the SavedModel CLI in the TensorFlow Guide.
End of explanation
"""


import sys
# We need sudo prefix if not on a Google Colab.
if 'google.colab' not in sys.modules:
  SUDO_IF_NEEDED = 'sudo'
else:
  SUDO_IF_NEEDED = ''

# This is the same as you would do from your command line, but without the [arch=amd64], and no sudo
# You would instead do:
# echo "deb [arch=amd64] http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal" | sudo tee /etc/apt/sources.list.d/tensorflow-serving.list && \
# curl https://storage.googleapis.com/tensorflow-serving-apt/tensorflow-serving.release.pub.gpg | sudo apt-key add -

!echo "deb http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal" | {SUDO_IF_NEEDED} tee /etc/apt/sources.list.d/tensorflow-serving.list && \
curl https://storage.googleapis.com/tensorflow-serving-apt/tensorflow-serving.release.pub.gpg | {SUDO_IF_NEEDED} apt-key add -
!{SUDO_IF_NEEDED} apt update

"""
Explanation: That tells us a lot about our model!  In this case we just trained our model, so we already know the inputs and outputs, but if we didn't this would be important information.  It doesn't tell us everything, like the fact that this is grayscale image data for example, but it's a great start.
Serve your model with TensorFlow Serving
Warning: If you are running this NOT on a Google Colab, following cells
will install packages on the system with root access. If you want to run it in
a local Jupyter notebook, please proceed with caution.
Add TensorFlow Serving distribution URI as a package source:
We're preparing to install TensorFlow Serving using Aptitude since this Colab runs in a Debian environment.  We'll add the tensorflow-model-server package to the list of packages that Aptitude knows about.  Note that we're running as root.
Note: This example is running TensorFlow Serving natively, but you can also run it in a Docker container, which is one of the easiest ways to get started using TensorFlow Serving.
End of explanation
"""


!{SUDO_IF_NEEDED} apt-get install tensorflow-model-server

"""
Explanation: Install TensorFlow Serving
This is all you need - one command line!
End of explanation
"""


os.environ["MODEL_DIR"] = MODEL_DIR

%%bash --bg 
nohup tensorflow_model_server \
  --rest_api_port=8501 \
  --model_name=fashion_model \
  --model_base_path="${MODEL_DIR}" >server.log 2>&1


!tail server.log

"""
Explanation: Start running TensorFlow Serving
This is where we start running TensorFlow Serving and load our model.  After it loads we can start making inference requests using REST.  There are some important parameters:

rest_api_port: The port that you'll use for REST requests.
model_name: You'll use this in the URL of REST requests.  It can be anything.
model_base_path: This is the path to the directory where you've saved your model.
End of explanation
"""


def show(idx, title):
  plt.figure()
  plt.imshow(test_images[idx].reshape(28,28))
  plt.axis('off')
  plt.title('\n\n{}'.format(title), fontdict={'size': 16})

import random
rando = random.randint(0,len(test_images)-1)
show(rando, 'An Example Image: {}'.format(class_names[test_labels[rando]]))

"""
Explanation: Make a request to your model in TensorFlow Serving
First, let's take a look at a random example from our test data.
End of explanation
"""


import json
data = json.dumps({"signature_name": "serving_default", "instances": test_images[0:3].tolist()})
print('Data: {} ... {}'.format(data[:50], data[len(data)-52:]))

"""
Explanation: Ok, that looks interesting.  How hard is that for you to recognize? Now let's create the JSON object for a batch of  three inference requests, and see how well our model recognizes things:
End of explanation
"""


# docs_infra: no_execute
!pip install -q requests

import requests
headers = {"content-type": "application/json"}
json_response = requests.post('http://localhost:8501/v1/models/fashion_model:predict', data=data, headers=headers)
predictions = json.loads(json_response.text)['predictions']

show(0, 'The model thought this was a {} (class {}), and it was actually a {} (class {})'.format(
  class_names[np.argmax(predictions[0])], np.argmax(predictions[0]), class_names[test_labels[0]], test_labels[0]))

"""
Explanation: Make REST requests
Newest version of the servable
We'll send a predict request as a POST to our server's REST endpoint, and pass it three examples.  We'll ask our server to give us the latest version of our servable by not specifying a particular version.
End of explanation
"""


# docs_infra: no_execute
headers = {"content-type": "application/json"}
json_response = requests.post('http://localhost:8501/v1/models/fashion_model/versions/1:predict', data=data, headers=headers)
predictions = json.loads(json_response.text)['predictions']

for i in range(0,3):
  show(i, 'The model thought this was a {} (class {}), and it was actually a {} (class {})'.format(
    class_names[np.argmax(predictions[i])], np.argmax(predictions[i]), class_names[test_labels[i]], test_labels[i]))

"""
Explanation: A particular version of the servable
Now let's specify a particular version of our servable.  Since we only have one, let's select version 1.  We'll also look at all three results.
End of explanation
"""


