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Watson NLP Python Client

In this tutorial you will build and deploy a Watson NLP client application. The sample client application is a web service built in Python that performs Tone Classification on user-supplied texts. The client application uses the Watson NLP Python client library to interact with a back-end model service. You can adapt the sample code from this tutorial to your own projects.

Architecture Diagram

Reference architecure

Prerequisites

Tip:

  • Podman provides a Docker-compatible command line front end. Unless otherwise noted, all the the Docker commands in this tutorial should work for Podman, if you simply alias the Docker CLI with alias docker=podman shell command.

Steps

1. Clone the GitHub repository

Clone the repository containing the sample code used in this tutorial.

git clone https://github.com/ibm-ecosystem-engineering/Watson-NLP

Go to the root directory for this tutorial.

cd Watson-NLP/MLOps/Dash-App-gRPC-Client

2. Start the model service

Follow the steps below to build a model image and run it, to serve the Watson NLP pretrained model classification_ensemble-workflow_lang_en_tone-stock.

cd runtime
docker build . -t watson-nlp-container:v1
docker run -d -e ACCEPT_LICENSE=true -p 8085:8085 watson-nlp-container:v1

Check that the service is running.

docker ps

3. Run the gRPC dash application

Return to this tutorial's root directory.

cd ..

Prepare your Python environment.

python3 -m venv client-env
source client-env/bin/activate

Install the required libraries.

pip3 install -r requirements.txt

Note that this will install the Watson NLP Runtime client library, among other things.

Run the application.

python3 Tone_dash_app.py

3.3 Test

You can now access the application from your browser at the following URL.

http://localhost:8050

Understanding the Application Code

This application relies on the Watson NLP Python client library watson-nlp-runtime-client to communicate with the Watson NLP Runtime using gRPC.

The gRPC code is in GrpcClient.py. The following code fragment creates a gRPC channel and then using the channel object it creates the client stub to communicate to the server.

GRPC_SERVER_URL = os.getenv("GRPC_SERVER_URL", default="localhost:8085")
channel = grpc.insecure_channel(GRPC_SERVER_URL)
self.stub = common_service_pb2_grpc.NlpServiceStub(channel)

The client stub accepts two parameters: a request object, and header parameter.

def call_tone_model(self, inputText):
    request = common_service_pb2.EmotionRequest(
        raw_document=syntax_types_pb2.RawDocument(text=inputText)
    )
    TONE_CLASSIFICATION_STOCK_MODEL = os.getenv(
        "TONE_CLASSIFICATION_STOCK_MODEL",
        default="classification_ensemble-workflow_lang_en_tone-stock",
    )
    response = self.stub.ClassificationPredict(
        request,
        metadata=[(self.NLP_MODEL_SERVICE_TYPE, TONE_CLASSIFICATION_STOCK_MODEL)],
    )
    return response