Developing Custom Machine learning Model and Running on KYMA - Enterprise AI Extension Part 2

[sudipghosh]

Hello All,

Welcome to Part - 2 of Developing Enterprise AI Extension, Well in first part i have discussed about overview of different types of AI you can Infuse into your Enterprise. In this Blog i will show you how you can create Custom Machine Learning based Image Classification Model with Teachable Machine ( A No Code Machine Learning Model Development Platform) and then How you can Dockerize it and Finally Deploy that machine Learning Model in SAP Cloud Platform Kyma Run Time.

 

What Is teachable Machine?

Teachable Machine is Web Based tool where you can train and create custom machine learning model for Image Classification, Voice and Pose. Cool isn't it. Best Part is Non Machine learning Developer can create Machine Learning Model, Train with their data and export it as saved model or keras or tensorflow.js and can create rest api out of it or embed it into Application.

More Information you can find out here.

Lets get into Action

Create Three Materials in SAP S/4HANA Cloud with Iphone X, Iphone 11 pro and Iphone 6 and we will create custom tensorflow based  Image Classification Model for those Material in Teachable machine and finally we will embed that model in flask based application and dockerize it to deploy into Kyma.

Now in order to train and build the Machine Learning Model Create Training Data Set and Upload the Training data Set as below. I have created three class accrodingly.

After That Click on Train Model to train the model and then export the model in Tensorflow Keras Format as below by clicking on the Download my model button.

Now extract the Zip format, you will find keras model and Label.txt.

These two Files are Very Important and we need this two files to build Python Flask based application.

Creating Machine Learning Model Embeded Python Flask based Application

Everyone needs rest api, isn't it because ultimately that could be integrated everywhere like any UI application or any Conversational AI based Application. 

As this model is based on Keras, In order to expose this machine Learning Model as Rest API we need to create Flask Based Application and tweak this generated model into it.

Lets open Visual Studio Code (My Favourite IDE) and Create the Application Structure as Below

 

Below are the Code Snippet for each files.

app.py

import json

import os

import io



# Imports for the REST API

from flask import Flask, request, jsonify



# Imports for image procesing

from PIL import Image



# Imports for prediction

from predict import predict_url



app = Flask(__name__)



# 4MB Max image size limit

app.config['MAX_CONTENT_LENGTH'] = 4 * 1024 * 1024









@app.route('/')

def index():

    return 'GET Methods are Not Allowed'







@app.route('/image', methods=['POST'])



def predict_url_handler():

    try:

        image_url = json.loads(request.get_data().decode('utf-8'))['url']

        results = predict_url(image_url)

        return jsonify(results)

    except Exception as e:

        print('EXCEPTION:', str(e))

        return 'Error processing image'





if __name__ == '__main__':

    #     # Load and intialize the model

    #     initialize()



    # Run the server

    app.run(host='0.0.0.0', port=80)

 

labels.txt

MZ-PROC-IT-IP-0032

MZ-PROC-IT-IP-0035

MZ-PROC-IT-IP-0100

predict.py

import tensorflow.keras

from urllib.request import Request, urlopen

from PIL import Image, ImageOps

import numpy as np

import ssl





def predict_url(imageUrl):

    """

    predicts image by url

    """

    ssl._create_default_https_context = ssl._create_unverified_context

    # log_msg("Predicting from url: " + imageUrl)

    imgrequest = Request(imageUrl, headers={"User-Agent": "Mozilla/5.0"})

    with urlopen(imgrequest) as testImage:

        # with urlopen(imageUrl) as testImage:



        image = Image.open(testImage)

        return predict_image(image)





def predict_image(image):

    # code snippet from teachable machine start-----------------------------

    # Disable scientific notation for clarity

    np.set_printoptions(suppress=True)



# Load the model

    model = tensorflow.keras.models.load_model('model.h5')

# model = keras.models.load_model('model.h5')

# Create the array of the right shape to feed into the keras model

# The 'length' or number of images you can put into the array is

# determined by the first position in the shape tuple, in this case 1.

    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)



# # Replace this with the path to your image

# image = Image.open('test.jpg')



#resize the image to a 224x224 with the same strategy as in TM2:

#resizing the image to be at least 224x224 and then cropping from the center

    size = (224, 224)

    image = ImageOps.fit(image, size, Image.ANTIALIAS)



#turn the image into a numpy array

    image_array = np.asarray(image)



# display the resized image

# image.show()



# Normalize the image

    normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1



# Load the image into the array

    data[0] = normalized_image_array



# run the inference

    predictions = model.predict(data)

# code snippet end teachable machine start-----------------------------



    # print('result')

    # print(predictions)

    labels_filename = 'labels.txt'

    labels = []

    with open(labels_filename, 'rt') as lf:

        # global labels

        labels = [l.strip() for l in lf.readlines()]

        result = []

        for p, label in zip(predictions[0], labels):

            result.append({

                'tagName': label,

                'probability': p * 100

            })



        response = {



            'predictions': result

        }



        # log_msg("Results: " + str(response))

        return response

    # return prediction

Dockerfile

FROM python:3.7-slim



RUN pip install -U pip

RUN pip install --no-cache-dir numpy~=1.17.5 tensorflow~=2.4.0 flask~=1.1.2 pillow~=7.2.0



COPY app /app







# Expose the port

EXPOSE 80



# Set the working directory

WORKDIR /app



# Run the flask server for the endpoints

CMD python -u app.py

Building Docker Image

docker build -t codersudip/tmachineofficesupply:aarini .

Docker Run Locally

docker run -p 127.0.0.1:80:80 -d codersudip/tmachineofficesupply:aarini

 

Testing in Postman

In order to test we need to find some image from google, i tested with Iphone 6s Picture and i must say result is pretty goodin terms of Accuracy. (If you remember MZ-PROC-IT-IP-0032 was the Material we have created for Iphone 6 in SAP S/4HANA CLOUD)

 

Now Deploying and running  into KYMA

In order to run and deploy this machine learning model into SAP Cloud Platform Kyma Run Time.

docker push codersudip/tmachineofficesupply:aarini            

 

Once it is pushed to Docker Hub, You are ready to Deploy into Kyma for that you need to enable Kyma Run Time.

In order to enable Kyma Run Please Follow this SAP Developer Tutorial for Enable Kyma Run Time by kevin.muessig

Pre-requisite: Install Kubectl and get you kubeconfig

 

Setting the Kube Config  Path.

export KUBECONFIG=kubeconfig.yml

 

Create Deployment

kubectl create deployment --image=codersudip/tmachineofficesupply:aarini officesupplytm

kubectl set env deployment/officesupplytm DOMAIN=cluster

Expose the service

kubectl expose deployment officesupplytm --port=80 --name=officesupplytm

Now go to Kyma and Look at the Deployment, You can see the recent deployment we just did

Now go to services and expose it as API.

 

Click on Expose Service

For this Scenario I am not showing How to secure this API because some one have already written blog on it. I am going to share the blog here insted 🙂

Please Follow Kyma for Dymmies [2]: First Simple Microservice with Security by carlos.roggan

 

Testing this Kyma Based ML Micro Service in Postman.

 

I have also done Youtube Podcast Session  on this topic, you can Watch if you would like to..



For today thats it, i hope you really enjoyed this blog, In next blog i would show How we can Integrate with Conversational AI and Finally Integrate with SAP S/4HANA Cloud to create Image Based Buying Feature for SAP S/4HANA Cloud from WhatsApp Based Conversation AI.

In the meanwhile have a good read, share with your friends and enjoy the weekend.

 

Regards,

Sudip