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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Embedding and Lookup (TF Hub and Annoy)
This example logs a class (instead of an object instance) as a model.
This allows for custom setup configuration in the class's __init__() method,
and access to logged artifacts at deployment time.
Step1:  This example features
Step2:  Imports
Step3:  Run Workflow
Prepare Data
Step4:  Instantiate Client
Step5:  Build Nearest Neighbor Embedding Index
Step7:  Define Model Class
A TensorFlow model—particularly one using TensorFlow Hub and a pre-built Annoy index—will require some setup at deployment time.
To support this, the Verta platform allows a model to be defined as a class that will be instantiated when it's deployed.
This class should have provide the following interface
Step8:  Earlier we logged an artifact with the key "nn_index".
You can obtain an artifacts mapping mentioned above using run.fetch_artifacts(keys) to work with locally.
A similar mapping—that works identically—will be passed into __init__() when the model is deployed.
Step9:  Log Model
The keys expected in the artifacts mapping mentioned above must be passed into run.log_model() to be available during deployment!
Step10:  We also have to make sure we provide every package involved in the model.
Step11:  Make Live Predictions
Access the Experiment Run through the Verta Web App and deploy it!
Step12:  Prepare Data
Step13:  Load Deployed Model
Step14:  Query Deployed Model
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Python Code:
try:
    import verta
except ImportError:
    !pip install verta
Explanation: Embedding and Lookup (TF Hub and Annoy)
This example logs a class (instead of an object instance) as a model.
This allows for custom setup configuration in the class's __init__() method,
and access to logged artifacts at deployment time.
End of explanation
HOST = "app.verta.ai"
PROJECT_NAME = "Film Review Embeddings"
EXPERIMENT_NAME = "TF Hub and Annoy"
# import os
# os.environ['VERTA_EMAIL'] = 
# os.environ['VERTA_DEV_KEY'] = 
Explanation: This example features:
- embedding model built around a TensorFlow Hub module
- nearest neighbor embedding lookup via Annoy
- verta's Python client logging a class as a model to be instantiated at deployment time
- predictions against a deployed model
End of explanation
from __future__ import print_function
import os
import time
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
import annoy
try:
    import wget
except ImportError:
    !pip install wget  # you may need pip3
    import wget
Explanation: Imports
End of explanation
train_data_url = "http://s3.amazonaws.com/verta-starter/imdb_master.csv"
train_data_filename = wget.detect_filename(train_data_url)
if not os.path.isfile(train_data_filename):
    wget.download(train_data_url)
all_reviews = pd.read_csv(train_data_filename, encoding='latin')['review'].values.tolist()
reviews = all_reviews[:2000]  # just a subset for this example
reviews[0]
Explanation: Run Workflow
Prepare Data
End of explanation
from verta import Client
from verta.utils import ModelAPI
client = Client(HOST)
proj = client.set_project(PROJECT_NAME)
expt = client.set_experiment(EXPERIMENT_NAME)
run = client.set_experiment_run()
Explanation: Instantiate Client
End of explanation
EMBEDDING_LENGTH = 512
NN_INDEX_FILENAME = "reviews.ann"
os.environ["TFHUB_CACHE_DIR"] = "tf_cache_dir"
# define graph
g = tf.Graph()
with g.as_default():
    text_input = tf.placeholder(dtype=tf.string, shape=[None])
    encoder = hub.Module("https://tfhub.dev/google/universal-sentence-encoder-large/3")
    embed = encoder(text_input)
    init_op = tf.group([tf.global_variables_initializer(), tf.tables_initializer()])
g.finalize()
# initialize session
sess = tf.Session(graph=g)
sess.run(init_op)
# build and save embedding index
t = annoy.AnnoyIndex(EMBEDDING_LENGTH, 'angular')  # Length of item vector that will be indexed
for i, review in enumerate(reviews):
    # produce embedding with TF
    embedding = sess.run(embed, feed_dict={text_input: [review]})
    t.add_item(i, embedding[0])
t.build(10) # 10 trees
t.save(NN_INDEX_FILENAME)
run.log_artifact("nn_index", open(NN_INDEX_FILENAME, 'rb'))
Explanation: Build Nearest Neighbor Embedding Index
End of explanation
class EmbeddingAndLookupModel:
    def __init__(self, artifacts):
        
        Parameters
        ----------
        artifacts
            Mapping of Experiment Run artifact keys to filepaths.
            This is provided by ``run.fetch_artifacts(artifact_keys)``.
        
        
        # get artifact filepath from `artifacts` mapping
        annoy_index_filepath = artifacts['nn_index']
        
        # load embedding index
        self.index = annoy.AnnoyIndex(EMBEDDING_LENGTH, "angular")
        self.index.load(annoy_index_filepath)
        
        os.environ["TFHUB_CACHE_DIR"] = "tf_cache_dir"
        
        # define graph
        g = tf.Graph()
        with g.as_default():
            self.text_input = tf.placeholder(dtype=tf.string, shape=[None])
            self.encoder = hub.Module("https://tfhub.dev/google/universal-sentence-encoder-large/3")
            self.embed = self.encoder(self.text_input)
            init_op = tf.group([tf.global_variables_initializer(), tf.tables_initializer()])
        g.finalize()
        self.graph = g
        
        # initialize session
        self.session = tf.Session(graph=self.graph)
        self.session.run(init_op)
     
    def predict(self, data):
        predictions = []
        for review in data:
            # embed sentence
            embedding = self.session.run(self.embed, feed_dict={self.text_input: [review]})
            # find closest
            predictions.append({
                review: self.index.get_nns_by_vector(embedding[0], 10)
            })
        return predictions
Explanation: Define Model Class
A TensorFlow model—particularly one using TensorFlow Hub and a pre-built Annoy index—will require some setup at deployment time.
To support this, the Verta platform allows a model to be defined as a class that will be instantiated when it's deployed.
This class should have provide the following interface:
__init__(self, artifacts) where artifacts is a mapping of artifact keys to filepaths. This will be explained below, but Verta will provide this so you can open these artifact files and set up your model. Other initialization steps would be in this method, as well.
predict(self, data) where data—like in other custom Verta models—is a list of input values for the model.
End of explanation
artifacts = run.fetch_artifacts(["nn_index"])
model = EmbeddingAndLookupModel(artifacts=artifacts)
model.predict(["Good film.", "Bad film!"])
Explanation: Earlier we logged an artifact with the key "nn_index".
You can obtain an artifacts mapping mentioned above using run.fetch_artifacts(keys) to work with locally.
A similar mapping—that works identically—will be passed into __init__() when the model is deployed.
End of explanation
run.log_model(
    model=EmbeddingAndLookupModel,
    artifacts=['nn_index'],
)
Explanation: Log Model
The keys expected in the artifacts mapping mentioned above must be passed into run.log_model() to be available during deployment!
End of explanation
run.log_requirements([
    "annoy==1.16.2",
    "tensorflow",
    "tensorflow_hub",
])
Explanation: We also have to make sure we provide every package involved in the model.
End of explanation
run
Explanation: Make Live Predictions
Access the Experiment Run through the Verta Web App and deploy it!
End of explanation
reviews = all_reviews[-2000:]
Explanation: Prepare Data
End of explanation
from verta.deployment import DeployedModel
deployed_model = DeployedModel(HOST, run.id)
Explanation: Load Deployed Model
End of explanation
for review in reviews:
    print(deployed_model.predict([review]))
    time.sleep(.5)
Explanation: Query Deployed Model
End of explanation
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