Run Oracle VBCS Application on Your Own Server

Latest VBCS release brings an option to export VBCS application and run on your own server (or different cloud provider). This is a truly strong step forward for VCBS. Read more about it in Shay Shmeltzer blog post. If you decide to keep running VBCS app within VBCS itself, then you get additional functionality of VBCS Business Services, Oracle Cloud security, etc. out of the box. If you export VBCS application and run on your own environment, these features are not included, but then you don't need to pay for VBCS Cloud runtime when hosting the app. It is great to have alternatives and depending on the customer either one or another of the use cases would work.

One of the use cases - customer even don't need to have its own VBCS instance. We could develop Oracle JET app in our VBCS instance, export and deploy it in the customer environment. Later we could provide support for version upgrade.

I have exported sample VBCS app with the external REST service call (REST service). Deployed app on our own server. You can try it yourself - http://138.68.79.219:7001/vbcsapp/webApps/countries/:

I must say it is simple to export VBCS app, no hassle at all. Make sure VBCS app you are exporting is set with anonymous access (this will disable Oracle Cloud security model). You will need to implement security and backend secure calls yourself:

Next go to REST service control and specify Bypass Proxy option (this will enable direct REST service call from VBCS app, bypassing Oracle Cloud proxy service). Important: to work with Bypass Proxy option, REST service must be invoked through HTTPS:

Nothing else on VBCS side. Next need to push application code to Oracle Developer Cloud Service Git repository and build artifact which can be exported. I suggest reading Shay Shmeltzer blog post about how to proceed with VBCS and Oracle Developer Cloud Service setup.

In VBCS do push to Git for the selected app:

If it is the first time with Oracle Developer Cloud Service, you will need to set up (refer to Shay post mentioned above) a build job. Create build job configuration, point to Git repo:

Provide a set of parameters for the build job:

Add Unix Shell script to the build job. This script will execute Node.js NPM command to run vb-build job to construct artifact which can be exported and deployed in your own environment. It is important to make sure that property values used in the script match property values defined in the build job earlier. To execute npm command, make sure to use Oracle Developer Cloud Service machine with Node.js support:

Run the job, once it completes and if there are no errors, go to job artifacts and download optimized.zip - this is the archive with VBCS application you can deploy:

Important: when exported VCBS application is accessed, it loads a bunch of scripts and executes HTTPS requests. There is one request which slows down VBCS application initial loading - call to _currentuser. It is trying to execute the _currentuser request on VBCS instance, but if the instance is down - it will wait until a timeout and only then will proceed with application loading. To fix that, search for _currentuser URL in the exported code and change URL to some dummy value, so that this request will fail immediately and will not keep VBCS application from continue loading:

Build it Yourself — Chatbot API with Keras/TensorFlow Model

Is not that complex to build your own chatbot (or assistant, this word is a new trendy term for chatbot) as you may think. Various chatbot platforms are using classification models to recognize user intent. While obviously, you get a strong heads-up when building a chatbot on top of the existing platform, it never hurts to study the background concepts and try to build it yourself. Why not use a similar model yourself. Chatbot implementation main challenges are:

1. Classify user input to recognize intent (this can be solved with Machine Learning, I’m using Keras with TensorFlow backend)
2. Keep context. This part is programming and there is nothing much ML related here. I’m using Node.js backend logic to track conversation context (while in context, typically we don’t require a classification for user intents — user input is treated as answers to chatbot questions)

Complete source code for this article with readme instructions is available on my GitHub repo (open source).

This is the list of Python libraries which are used in the implementation. Keras deep learning library is used to build a classification model. Keras runs training on top of TensorFlow backend. Lancaster stemming library is used to collapse distinct word forms:

Chatbot intents and patterns to learn are defined in a plain JSON file. There is no need to have a huge vocabulary. Our goal is to build a chatbot for a specific domain. Classification model can be created for small vocabulary too, it will be able to recognize a set of patterns provided for the training:

Before we could start with classification model training, we need to build vocabulary first. Patterns are processed to build a vocabulary. Each word is stemmed to produce generic root, this would help to cover more combinations of user input:

This is the output of vocabulary creation. There are 9 intents (classes) and 82 vocabulary words:

Training would not run based on the vocabulary of words, words are meaningless for the machine. We need to translate words into bags of words with arrays containing 0/1. Array length will be equal to vocabulary size and 1 will be set when a word from the current pattern is located in the given position:

Training data — X (pattern converted into array [0,1,0,1…, 0]), Y (intents converted into array [1, 0, 0, 0,…,0], there will be single 1 for intents array). Model is built with Keras, based on three layers. According to my experiments, three layers provide good results (but it all depends on training data). Classification output will be multiclass array, which would help to identify encoded intent. Using softmax activation to produce multiclass classification output (result returns an array of 0/1: [1,0,0,…,0] — this set identifies encoded intent):

Compile Keras model with SGD optimizer:

Fit the model — execute training and construct classification model. I’m executing training in 200 iterations, with batch size = 5:

Model is built. Now we can define two helper functions. Function bow helps to translate user sentence into a bag of words with array 0/1:

Check this example — translating the sentence into a bag of words:

When the function finds a word from the sentence in chatbot vocabulary, it sets 1 into the corresponding position in the array. This array will be sent to be classified by the model to identify to what intent it belongs:

It is a good practice to save the trained model into a pickle file to be able to reuse it to publish through Flask REST API:

Before publishing model through Flask REST API, is always good to run an extra test. Use model.predict function to classify user input and based on calculated probability return intent (multiple intents can be returned):

Example to classify sentence:

The intent is calculated correctly:

To publish the same function through REST endpoint, we can wrap it into Flask API:

I have explained how to implement the classification part. In the GitHub repo referenced at the beginning of the post, you will find a complete example of how to maintain the context. Context is maintained by logic written in JavaScript and running on Node.js backend. Context flow must be defined in the list of intents, as soon as the intent is classified and backend logic finds a start of the context — we enter into the loop and ask related questions. How advanced is context handling all depends on the backend implementation (this is beyond Machine Learning scope at this stage).

Chatbot UI:

Publishing Machine Learning API with Python Flask

Flask is fun and easy to setup, as it says on Flask website. And that's true. This microframework for Python offers a powerful way of annotating Python function with REST endpoint. I’m using Flask to publish ML model API to be accessible by the 3rd party business applications.

This example is based on XGBoost.

For better code maintenance, I would recommend using a separate Jupyter notebook where ML model API will be published. Import Flask module along with Flask CORS:

Model is trained on Pima Indians Diabetes Database. CSV data can be downloaded from here. To construct Pandas data frame variable as input for model predict function, we need to define an array of dataset columns:

Previously trained and saved model is loaded using Pickle:

It is always a good practice to do a test run and check if the model performs well. Construct data frame with an array of column names and an array of data (using new data, the one which is not present in train or test datasets). Calling two functions — model.predict and model.predict_proba. Often I prefer model.predict_proba, it returns probability which describes how likely will be 0/1, this helps to interpret the result based on a certain range (0.25 to 0.75 for example). Pandas data frame is constructed with sample payload and then the model prediction is executed:

Flask API. Make sure you enable CORS, otherwise API call will not work from another host. Write annotation before the function you want to expose through REST API. Provide an endpoint name and supported REST methods (POST in this example). Payload data is retrieved from the request, Pandas data frame is constructed and model predict_proba function is executed:

Response JSON string is constructed and returned as a function result. I’m running Flask in Docker container, that's why using 0.0.0.0 as the host on which it runs. Port 5000 is mapped as external port and this allows calls from the outside.

While it works to start Flask interface directly in Jupyter notebook, I would recommend to convert it to Python script and run from command line as a service. Use Jupyter nbconvert command to convert to Python script:

jupyter nbconvert — to python diabetes_redsamurai_endpoint_db.ipynb

Python script with Flask endpoint can be started as the background process with PM2 process manager. This allows to run endpoint as a service and start other processes on different ports. PM2 start command:

pm2 start diabetes_redsamurai_endpoint_db.py

pm2 monit helps to display info about running processes:

ML model classification REST API call from Postman through endpoint served by Flask:

More info:

- GitHub repo with source code
- Previous post about XGBoost model training