Creating a cool machine learning project is one thing, another thing when you need other people to be able to see it too. Of course, you can put the whole project on GitHub, but how will your grandparents understand what you did? No, we need to deploy our deep learning model as a web application that will be available to anyone in the world.

In this article, we will learn how to write a web application that uses the Keras trained recurrent neural network and allows users to create new patent annotations. This project is based on work from the article "Recurrent Neural Networks by Example", however, it is not necessary to know how RNN is built now. At the moment, we will just consider it as a black box: we set the initial sequence, and it displays a completely new patent annotation, which can be viewed in a browser!

Typically, data analysts develop models, and front-end show them to the world. In this project, we will have to play both roles and plunge into web development (albeit almost entirely in Python).

This project will require combining several tools:

• Flask: to create a basic web application in Python;

• Keras: Deploying a Pre-trained Recurrent Neural Network

• Templates from the Jinja template engine;

• HTML and CSS for creating web pages.

Ultimately, we get a web application that allows users to create completely new patent annotations using a trained recurrent neural network.

All project code is available on GitHub.

An approach

The goal was to deploy the web application as quickly as possible. For this, I chose Flask, which allows you to write applications in Python. I don’t like messing with styles (I think you’ve already seen), so I copy and paste almost all CSS from third-party sources. This Keras team article was useful for learning the basics, and this article is also a helpful guide.

In general, this project meets my design principles: create a prototype that works quickly (copying and pasting as much code as needed), and then just repeat the same thing to improve the product.

Basic web application on Flask

The fastest way to create a Python web application is to use Flask (http://flask.pocoo.org/). To create our own application, we can use the following:

from flask import Flask
app = Flask (__ name__)

@ app.route ("/")
def hello ():
    return "
Not much going on here
"
app.run (host = '0.0.0.0', port = 50000)

If you run this code, you will see your own application at localhost: 50000. Of course, we need something more, so we will use a more complex function that generally does the same: it processes requests from your browser and serves some content in the form of HTML.

User input form

When users go to the main page of the application, we will show them a form with three options:

1. Enter the source sequence for the RNN or randomly generate it.
2. Selection of the “diversity” of RNN forecasts.
3. The choice of the number of words output from the RNN.

To create a form in Python, we use wtforms. You will see the code for its creation below:

from wtforms import (Form, TextField, validators, SubmitField,
DecimalField, IntegerField)

class ReusableForm (Form):
    "" "User entry form for entering specifics for generation" ""
    # Starting seed
    seed = TextField ("Enter a seed string or 'random':", validators =[
                     validators.InputRequired()])
    # Diversity of predictions
    diversity = DecimalField ('Enter diversity:', default = 0.8,
                             validators =[validators.InputRequired(),
                                         validators.NumberRange(min=0.5, max=5.0,
                                         message='Diversity must be between 0.5 and 5.')])
    # Number of words
    words = IntegerField ('Enter number of words to generate:',
                         default = 50, validators =[validators.InputRequired(),
                                                 validators.NumberRange(min=10, max=100, 
                                                 message='Number of words must be between 10 and 100')])
    # Submit button
    submit = SubmitField ("Enter")

Using it, we created the form shown below (with styles from main.css):

A check in the code is needed to make sure that the user enters the correct information. For example, we check that all fields are filled, and that the value of the diversity parameter is between 0.5 and 5. These conditions must be met in order to get started.

Error validating entered data

We process this form using templates on Flask.

Patterns

A template is a file with an already prepared “frame” that just needs to be filled in a certain way. For a Flask web application, we can use the Jinja template library to translate Python code into an HTML document. For example, in our main function, we will send the contents of the form to a template named index.html.

from flask import render_template

# Home page
@ app.route ("/", methods =['GET', 'POST'])
def home ():
    "" "Home page of app with form" ""
    # Create form
    form = ReusableForm (request.form)

    # Send template information to index.html
    return render_template ('index.html', form = form)

When the user goes to the main page, our application will provide index.html input from the form. A template is a complete HTML structure in which we reference Python variables using syntax {{variable}}.

  
  
  
  



  

    

      Writing Novel Patent Abstracts with Recurrent Neural Networks
    

    

    {% block content%}
    {% for message in form.seed.errors%}
    
{{message}} 
    {% endfor%}

    {% for message in form.diversity.errors%}
    
{{message}} 
    {% endfor%}

    {% for message in form.words.errors%}
    
{{message}} 
    {% endfor%}

    


      {{form.seed.label}}
      {{form.seed}}

      {{form.diversity.label}}
      {{form.diversity}}

      {{form.words.label}}
      {{form.words}}

      {{form.submit}}
    
    {% endblock%} 

When an error occurs in the form (input that cannot be verified), an error message will flash. In addition, the form will be displayed as in the file above.

When a user enters information and clicks submit (Post – request), if the information is correct, we redirect the input data to the corresponding function to make predictions using trained RNN. This means that you need to change home ().

from flask import request
# User defined utility functions
from utils import generate_random_start, generate_from_seed

# Home page
@ app.route ("/", methods =['GET', 'POST'])
def home ():
    "" "Home page of app with form" ""
    
    # Create form
    form = ReusableForm (request.form)

    # On form entry and all conditions met
    if request.method == 'POST' and form.validate ():
        # Extract information
        seed = request.form['seed']
        diversity = float (request.form['diversity'])
        words = int (request.form['words'])
        # Generate a random sequence
        if seed == 'random':
            return render_template ('random.html',
                                   input = generate_random_start (model = model,
                                                               graph = graph,
                                                               new_words = words,
                                                               diversity = diversity))
        # Generate starting from a seed sequence
        else:
            return render_template ('seeded.html',
                                   input = generate_from_seed (model = model,
                                                            graph = graph,
                                                            seed = seed,
                                                            new_words = words,
                                                            diversity = diversity))
    # Send template information to index.html
    return render_template ('index.html', form = form)

Now when the user clicks submit and the entered information is correct, depending on the input data, the input is sent either to generate_random_start, either in generate_from_seed. These features use the Keras trained model to create a new patent with parameters. diversity and num_wordsdefined by the user. The output of this function, in turn, is sent randomly to templates. random.html or seeded.htmlto display as a web page.

Creating Predictions Using the Keras Pre-trained Model

In parameter model There is a pre-trained Keras model that loads as follows:

from keras.models import load_model
import tensorflow as tf

def load_keras_model ():
    "" "Load in the pre-trained model" ""
    global model
    model = load_model ('../ models / train-embeddings-rnn.h5')
    # Required for model to work
    global graph
    graph = tf.get_default_graph ()
    
load_keras_model ()

(Essentially, there is a workaround that looks like tf.get_default_graph ())
I will not show completely two functions util (you will find the code here), because all you need to understand is that they store the already trained Keras model along with the parameters and make predictions – patent annotations.

Both of these functions return a string in Python with formatted HTML. This line is sent to another template, which will appear as a web page. For example, generate_random_start returns formatted HTML that is sent to random.html:

    

    
    
    

        
Home
    



    

        
        {% block content%}
        {{input | safe}}
        {% endblock%} 

Here we use the template engine again Jinja to display a formatted HTML file. Since the Python string is already translated into HTML, all we need to do to display it is use {{input | safe}} (Where input Is a Python variable). Then in main.css we can add styles to this page, however, as well as other HTML pages.

Work result

Function generate_random_start selects a random patent annotation as an input sequence and makes predictions based on it. It then displays the input sequence, the RNN generated output, and the actual output:

The output is based on a random input sequence.

Function generate_from_seed accepts the initial sequence entered by the user and makes forecasts based on it. The output is as follows:

Output based on user input sequence.

Although the results do not always form logical sequences, they nevertheless show that the neural network is familiar with the basics of the English language. She was trained to predict each next word based on the previous 50 and thereby select a compelling annotation. Depending on the parameter diversity the output may be completely random or cyclical.

Application launch

To start the application for yourself, all you need to do is download the repository, go to the directory where the deployment is and enter run_keras_server.py. The application will be available at localhost: 10000.

Depending on how your home Wi-Fi is configured, you should be able to access the application from any computer on the network using your IP address.

Next steps

The web application running on your personal computer is great for sharing with friends and family. However, I definitely do not recommend opening it to everyone on your home network. Later we will learn how to deploy the application on AWS EC2 and finally show it to the world.

To improve the application, you can change the styles (in main.css) and, possibly, add additional parameters, such as the ability to select a pre-trained network. The great thing about personal projects is that you can develop them as long as you want. If you want to play with the application, download the repository and get started.

Conclusion

In this article, we learned how to deploy a Keras pre-trained deep learning model as a web application. This required the integration of a number of different technologies, including recurrent neural networks, web applications, templates, HTML, CSS and, of course, Python.

Although this is a basic application, it shows that you can create web applications using deep learning with relatively little effort. Few people can say that they were able to deploy a deep learning model in the form of a web application, but if you follow the instructions in this article, you can become one of them!