import os

import pandas as pd
from google.cloud import bigquery

"""
Explanation: Reusable Embeddings
Learning Objectives
1. Learn how to use a pre-trained TF Hub text modules to generate sentence vectors
1. Learn how to incorporate a pre-trained TF-Hub module into a Keras model
1. Learn how to deploy and use a text model on CAIP
Introduction
In this notebook, we will implement text models to recognize the probable source (GitHub, TechCrunch, or The New York Times) of the titles we have in the title dataset.
First, we will load and pre-process the texts and labels so that they are suitable to be fed to sequential Keras models with first layer being TF-hub pre-trained modules. Thanks to this first layer, we won't need to tokenize and integerize the text before passing it to our models. The pre-trained layer will take care of that for us, and consume directly raw text. However, we will still have to one-hot-encode each of the 3 classes into a 3 dimensional basis vector.
Then we will build, train and compare simple DNN models starting with different pre-trained TF-Hub layers.
End of explanation
"""


PROJECT = !(gcloud config get-value core/project)
PROJECT = PROJECT[0]
BUCKET = PROJECT
REGION = "us-central1"
%env PROJECT = {PROJECT}
%env BUCKET = {BUCKET}
%env REGION = {REGION}

%%bash
gcloud config set project $PROJECT
gcloud config set ai_platform/region $REGION

"""
Explanation: Replace the variable values in the cell below:
End of explanation
"""


%%bigquery --project $PROJECT

SELECT
    url, title, score
FROM
    `bigquery-public-data.hacker_news.stories`
WHERE
    LENGTH(title) > 10
    AND score > 10
    AND LENGTH(url) > 0
LIMIT 10

"""
Explanation: Create a Dataset from BigQuery
Hacker news headlines are available as a BigQuery public dataset. The dataset contains all headlines from the sites inception in October 2006 until October 2015. 
Here is a sample of the dataset:
End of explanation
"""


%%bigquery --project $PROJECT

SELECT
    ARRAY_REVERSE(SPLIT(REGEXP_EXTRACT(url, '.*://(.[^/]+)/'), '.'))[OFFSET(1)] AS source,
    COUNT(title) AS num_articles
FROM
    `bigquery-public-data.hacker_news.stories`
WHERE
    REGEXP_CONTAINS(REGEXP_EXTRACT(url, '.*://(.[^/]+)/'), '.com$')
    AND LENGTH(title) > 10
GROUP BY
    source
ORDER BY num_articles DESC
  LIMIT 100

"""
Explanation: Let's do some regular expression parsing in BigQuery to get the source of the newspaper article from the URL. For example, if the url is http://mobile.nytimes.com/...., I want to be left with <i>nytimes</i>
End of explanation
"""


regex = ".*://(.[^/]+)/"


sub_query = """
SELECT
    title,
    ARRAY_REVERSE(SPLIT(REGEXP_EXTRACT(url, '{0}'), '.'))[OFFSET(1)] AS source
    
FROM
    `bigquery-public-data.hacker_news.stories`
WHERE
    REGEXP_CONTAINS(REGEXP_EXTRACT(url, '{0}'), '.com$')
    AND LENGTH(title) > 10
""".format(
    regex
)


query = """
SELECT 
    LOWER(REGEXP_REPLACE(title, '[^a-zA-Z0-9 $.-]', ' ')) AS title,
    source
FROM
  ({sub_query})
WHERE (source = 'github' OR source = 'nytimes' OR source = 'techcrunch')
""".format(
    sub_query=sub_query
)

print(query)

"""
Explanation: Now that we have good parsing of the URL to get the source, let's put together a dataset of source and titles. This will be our labeled dataset for machine learning.
End of explanation
"""


bq = bigquery.Client(project=PROJECT)
title_dataset = bq.query(query).to_dataframe()
title_dataset.head()

"""
Explanation: For ML training, we usually need to split our dataset into training and evaluation datasets (and perhaps an independent test dataset if we are going to do model or feature selection based on the evaluation dataset). AutoML however figures out on its own how to create these splits, so we won't need to do that here.
End of explanation
"""


print(f"The full dataset contains {len(title_dataset)} titles")

"""
Explanation: AutoML for text classification requires that
* the dataset be in csv form with 
* the first column being the texts to classify or a GCS path to the text 
* the last colum to be the text labels
The dataset we pulled from BiqQuery satisfies these requirements.
End of explanation
"""


title_dataset.source.value_counts()

"""
Explanation: Let's make sure we have roughly the same number of labels for each of our three labels:
End of explanation
"""


DATADIR = "./data/"

if not os.path.exists(DATADIR):
    os.makedirs(DATADIR)

FULL_DATASET_NAME = "titles_full.csv"
FULL_DATASET_PATH = os.path.join(DATADIR, FULL_DATASET_NAME)

# Let's shuffle the data before writing it to disk.
title_dataset = title_dataset.sample(n=len(title_dataset))

title_dataset.to_csv(
    FULL_DATASET_PATH, header=False, index=False, encoding="utf-8"
)

"""
Explanation: Finally we will save our data, which is currently in-memory, to disk.
We will create a csv file containing the full dataset and another containing only 1000 articles for development.
Note: It may take a long time to train AutoML on the full dataset, so we recommend to use the sample dataset for the purpose of learning the tool.
End of explanation
"""


sample_title_dataset = title_dataset.sample(n=1000)
sample_title_dataset.source.value_counts()

"""
Explanation: Now let's sample 1000 articles from the full dataset and make sure we have enough examples for each label in our sample dataset (see here for further details on how to prepare data for AutoML).
End of explanation
"""


SAMPLE_DATASET_NAME = "titles_sample.csv"
SAMPLE_DATASET_PATH = os.path.join(DATADIR, SAMPLE_DATASET_NAME)

sample_title_dataset.to_csv(
    SAMPLE_DATASET_PATH, header=False, index=False, encoding="utf-8"
)

sample_title_dataset.head()

import datetime
import os
import shutil

import pandas as pd
import tensorflow as tf
from tensorflow.keras.callbacks import EarlyStopping, TensorBoard
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.utils import to_categorical
from tensorflow_hub import KerasLayer

print(tf.__version__)

%matplotlib inline

"""
Explanation: Let's write the sample datatset to disk.
End of explanation
"""


MODEL_DIR = f"gs://{BUCKET}/text_models"

"""
Explanation: Let's start by specifying where the information about the trained models will be saved as well as where our dataset is located:
End of explanation
"""


ls $DATADIR

DATASET_NAME = "titles_full.csv"
TITLE_SAMPLE_PATH = os.path.join(DATADIR, DATASET_NAME)
COLUMNS = ["title", "source"]

titles_df = pd.read_csv(TITLE_SAMPLE_PATH, header=None, names=COLUMNS)
titles_df.head()

"""
Explanation: Loading the dataset
As in the previous labs, our dataset consists of titles of articles along with the label indicating from which source these articles have been taken from (GitHub, TechCrunch, or The New York Times):
End of explanation
"""


titles_df.source.value_counts()

"""
Explanation: Let's look again at the number of examples per label to make sure we have a well-balanced dataset:
End of explanation
"""


CLASSES = {"github": 0, "nytimes": 1, "techcrunch": 2}
N_CLASSES = len(CLASSES)

def encode_labels(sources):
    classes = [CLASSES[source] for source in sources]
    one_hots = to_categorical(classes, num_classes=N_CLASSES)
    return one_hots

encode_labels(titles_df.source[:4])

"""
Explanation: Preparing the labels
In this lab, we will use pre-trained TF-Hub embeddings modules for english for the first layer of our models. One immediate
advantage of doing so is that the TF-Hub embedding module will take care for us of processing the raw text. 
This also means that our model will be able to consume text directly instead of sequences of integers representing the words.
However, as before, we still need to preprocess the labels into one-hot-encoded vectors:
End of explanation
"""


N_TRAIN = int(len(titles_df) * 0.95)

titles_train, sources_train = (
    titles_df.title[:N_TRAIN],
    titles_df.source[:N_TRAIN],
)

titles_valid, sources_valid = (
    titles_df.title[N_TRAIN:],
    titles_df.source[N_TRAIN:],
)

"""
Explanation: Preparing the train/test splits
Let's split our data into train and test splits:
End of explanation
"""


sources_train.value_counts()

sources_valid.value_counts()

"""
Explanation: To be on the safe side, we verify that the train and test splits
have roughly the same number of examples per class.
Since it is the case, accuracy will be a good metric to use to measure
the performance of our models.
End of explanation
"""


X_train, Y_train = titles_train.values, encode_labels(sources_train)
X_valid, Y_valid = titles_valid.values, encode_labels(sources_valid)

X_train[:3]

Y_train[:3]

"""
Explanation: Now let's create the features and labels we will feed our models with:
End of explanation
"""


# TODO 1
NNLM = "https://tfhub.dev/google/nnlm-en-dim50/2"

nnlm_module = KerasLayer(
    NNLM, output_shape=[50], input_shape=[], dtype=tf.string, trainable=True
)

"""
Explanation: NNLM Model
We will first try a word embedding pre-trained using a Neural Probabilistic Language Model. TF-Hub has a 50-dimensional one called 
nnlm-en-dim50-with-normalization, which also
normalizes the vectors produced. 
Once loaded from its url, the TF-hub module can be used as a normal Keras layer in a sequential or functional model. Since we have enough data to fine-tune the parameters of the pre-trained embedding itself, we will set trainable=True in the KerasLayer that loads the pre-trained embedding:
End of explanation
"""


# TODO 1
nnlm_module(tf.constant(["The dog is happy to see people in the street."]))

"""
Explanation: Note that this TF-Hub embedding produces a single 50-dimensional vector when passed a sentence:
End of explanation
"""


# TODO 1
SWIVEL = "https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim-with-oov/1"
swivel_module = KerasLayer(
    SWIVEL, output_shape=[20], input_shape=[], dtype=tf.string, trainable=True
)

"""
Explanation: Swivel Model
Then we will try a word embedding obtained using Swivel, an algorithm that essentially factorizes word co-occurrence matrices to create the words embeddings. 
TF-Hub hosts the pretrained gnews-swivel-20dim-with-oov 20-dimensional Swivel module.
End of explanation
"""


# TODO 1
swivel_module(tf.constant(["The dog is happy to see people in the street."]))

"""
Explanation: Similarly as the previous pre-trained embedding, it outputs a single vector when passed a sentence:
End of explanation
"""


def build_model(hub_module, name):
    model = Sequential(
        [
            hub_module,  # TODO 2
            Dense(16, activation="relu"),
            Dense(N_CLASSES, activation="softmax"),
        ],
        name=name,
    )

    model.compile(
        optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"]
    )
    return model

"""
Explanation: Building the models
Let's write a function that 

takes as input an instance of a KerasLayer (i.e. the swivel_module or the nnlm_module we constructed above) as well as the name of the model (say swivel or nnlm)
returns a compiled Keras sequential model starting with this pre-trained TF-hub layer, adding one or more dense relu layers to it, and ending with a softmax layer giving the probability of each of the classes:
End of explanation
"""


def train_and_evaluate(train_data, val_data, model, batch_size=5000):
    X_train, Y_train = train_data

    tf.random.set_seed(33)

    model_dir = os.path.join(MODEL_DIR, model.name)
    if tf.io.gfile.exists(model_dir):
        tf.io.gfile.rmtree(model_dir)

    history = model.fit(
        X_train,
        Y_train,
        epochs=100,
        batch_size=batch_size,
        validation_data=val_data,
        callbacks=[EarlyStopping(patience=1), TensorBoard(model_dir)],
    )
    return history

"""
Explanation: Let's also wrap the training code into a train_and_evaluate function that 
* takes as input the training and validation data, as well as the compiled model itself, and the batch_size
* trains the compiled model for 100 epochs at most, and does early-stopping when the validation loss is no longer decreasing
* returns an history object, which will help us to plot the learning curves
End of explanation
"""


data = (X_train, Y_train)
val_data = (X_valid, Y_valid)

nnlm_model = build_model(nnlm_module, "nnlm")
nnlm_history = train_and_evaluate(data, val_data, nnlm_model)

history = nnlm_history
pd.DataFrame(history.history)[["loss", "val_loss"]].plot()
pd.DataFrame(history.history)[["accuracy", "val_accuracy"]].plot()

"""
Explanation: Training NNLM
End of explanation
"""


swivel_model = build_model(swivel_module, name="swivel")

swivel_history = train_and_evaluate(data, val_data, swivel_model)

history = swivel_history
pd.DataFrame(history.history)[["loss", "val_loss"]].plot()
pd.DataFrame(history.history)[["accuracy", "val_accuracy"]].plot()

"""
Explanation: Training Swivel
End of explanation
"""


!echo tensorboard --logdir $MODEL_DIR --port 6006

"""
Explanation: Comparing the models
Swivel trains faster but achieves a lower validation accuracy, and requires more epochs to train on.
At last, let's compare all the models we have trained at once using TensorBoard in order
to choose the one that overfits the less for the same performance level.
Run the output of the following command in your Cloud Shell to launch TensorBoard, and use the Web Preview on port 6006 to view it.
End of explanation
"""


OUTPUT_DIR = "./savedmodels"
shutil.rmtree(OUTPUT_DIR, ignore_errors=True)

EXPORT_PATH = os.path.join(OUTPUT_DIR, "swivel")
os.environ["EXPORT_PATH"] = EXPORT_PATH

shutil.rmtree(EXPORT_PATH, ignore_errors=True)

tf.keras.models.save_model(swivel_model, EXPORT_PATH)
swivel_model.save(EXPORT_PATH)

"""
Explanation: Deploying the model
The first step is to serialize one of our trained Keras model as a SavedModel:
End of explanation
"""


%%bash

# TODO 5

MODEL_NAME=title_model
VERSION_NAME=swivel

if [[ $(gcloud ai-platform models list --format='value(name)' | grep ^$MODEL_NAME$) ]]; then
    echo "$MODEL_NAME already exists"
else
    echo "Creating $MODEL_NAME"
    gcloud ai-platform models create --region=$REGION $MODEL_NAME
fi

if [[ $(gcloud ai-platform versions list --model $MODEL_NAME --format='value(name)' | grep ^$VERSION_NAME$) ]]; then
    echo "Deleting already existing $MODEL_NAME:$VERSION_NAME ... "
    echo yes | gcloud ai-platform versions delete --model=$MODEL_NAME $VERSION_NAME
    echo "Please run this cell again if you don't see a Creating message ... "
    sleep 2
fi

echo "Creating $MODEL_NAME:$VERSION_NAME"
gcloud ai-platform versions create \
  --model=$MODEL_NAME $VERSION_NAME \
  --framework=tensorflow \
  --python-version=3.7 \
  --runtime-version=2.1 \
  --origin=$EXPORT_PATH \
  --staging-bucket=gs://$BUCKET \
  --machine-type n1-standard-4 \
  --region=$REGION

"""
Explanation: Then we can deploy the model using the gcloud CLI as before:
End of explanation
"""


!saved_model_cli show \
 --tag_set serve \
 --signature_def serving_default \
 --dir {EXPORT_PATH}
!find {EXPORT_PATH}

"""
Explanation: Before we try our deployed model, let's inspect its signature to know what to send to the deployed API:
End of explanation
"""


%%writefile input.json
{"keras_layer_1_input": "hello"}

!gcloud ai-platform predict \
  --model title_model \
  --json-instances input.json \
  --version swivel \
  --region=$REGION

"""
Explanation: Let's go ahead and hit our model:
End of explanation
"""


