npm package discovery and stats viewer.

Discover Tips

  • General search

    [free text search, go nuts!]

  • Package details

    pkg:[package-name]

  • User packages

    @[username]

Sponsor

Optimize Toolset

I’ve always been into building performant and accessible sites, but lately I’ve been taking it extremely seriously. So much so that I’ve been building a tool to help me optimize and monitor the sites that I build to make sure that I’m making an attempt to offer the best experience to those who visit them. If you’re into performant, accessible and SEO friendly sites, you might like it too! You can check it out at Optimize Toolset.

About

Hi, 👋, I’m Ryan Hefner  and I built this site for me, and you! The goal of this site was to provide an easy way for me to check the stats on my npm packages, both for prioritizing issues and updates, and to give me a little kick in the pants to keep up on stuff.

As I was building it, I realized that I was actually using the tool to build the tool, and figured I might as well put this out there and hopefully others will find it to be a fast and useful way to search and browse npm packages as I have.

If you’re interested in other things I’m working on, follow me on Twitter or check out the open source projects I’ve been publishing on GitHub.

I am also working on a Twitter bot for this site to tweet the most popular, newest, random packages from npm. Please follow that account now and it will start sending out packages soon–ish.

Open Software & Tools

This site wouldn’t be possible without the immense generosity and tireless efforts from the people who make contributions to the world and share their work via open source initiatives. Thank you 🙏

© 2024 – Pkg Stats / Ryan Hefner

@ibm-watson/natural-language-classifier-demo

v1.2.0

Published

Natural Language Classifier sample application

Downloads

6

Readme

The IBM Watson™ Natural Language Classifier service applies deep learning techniques to make predictions about the best predefined classes for short sentences or phrases. The classes can trigger a corresponding action in an application, such as directing a request to a location or person, or answering a question. After training, the service returns information for texts that it hasn't seen before. The response includes the name of the top classes and confidence values.

demo

You can view a demo of this app.

Prerequisites

  1. Sign up for an IBM Cloud account.
  2. Download the IBM Cloud CLI.
  3. Create an instance of the Natural Language Classifier service and get your credentials:
    • Go to the Natural Language Classifier page in the IBM Cloud Catalog.
    • Log in to your IBM Cloud account.
    • Click Create.
    • Click Show to view the service credentials.
    • Copy the apikey value, or copy the username and password values if your service instance doesn't provide an apikey.
    • Copy the url value.

Configuring the application

  1. The Natural Language Classifier service must be trained before you can successfully use this application. The training data is provided in the file training/weather_data_train.csv.
    If you have username and password credentials, train a classifier by using the following command:
curl -i -u "<username>":"<password>" \
-F training_data=@training/weather_data_train.csv \
-F training_metadata="{\"language\":\"en\",\"name\":\"TutorialClassifier\"}" \
"<url>/v1/classifiers"

Make sure to replace <username>, <password> and <url>.
If you have apikey credentials, use the word "apikey" as your username and your apikey as the password.
After running the command, copy the value for classifier_id.

  1. In the application folder, copy the .env.example file and create a file called .env

    cp .env.example .env
  2. Open the .env file and add the service credentials that you obtained in the previous step.

    Example .env file that configures the apikey and url for a Natural Language Classifier service instance hosted in the US East region:

    NATURAL_LANGUAGE_CLASSIFIER_IAM_APIKEY=X4rbi8vwZmKpXfowaS3GAsA7vdy17Qh7km5D6EzKLHL2
    NATURAL_LANGUAGE_CLASSIFIER_URL=https://gateway.watsonplatform.net/natural-language-classifier/api
    • If your service instance uses username and password credentials, add the NATURAL_LANGUAGE_CLASSIFIER_USERNAME and NATURAL_LANGUAGE_CLASSIFIER_PASSWORD variables to the .env file.

    Example .env file that configures the username, password, and url for a Natural Language Classifier service instance hosted in the Sydney region:

    NATURAL_LANGUAGE_CLASSIFIER_USERNAME=522be-7b41-ab44-dec3-g1eab2ha73c6
    NATURAL_LANGUAGE_CLASSIFIER_PASSWORD=A4Z5BdGENrwu8
    NATURAL_LANGUAGE_CLASSIFIER_URL=https://gateway-syd.watsonplatform.net/natural-language-classifier/api
  3. Add the CLASSIFIER_ID to the previous properties

    CLASSIFIER_ID=522be-7b41-ab44-dec3-g1eab2ha73c6

Running locally

  1. Install the dependencies

    npm install
  2. Run the application

    npm start
  3. View the application in a browser at localhost:3000

Deploying to IBM Cloud as a Cloud Foundry Application

  1. Login to IBM Cloud with the IBM Cloud CLI

    ibmcloud login
  2. Target a Cloud Foundry organization and space.

    ibmcloud target --cf
  3. Edit the manifest.yml file. Change the name field to something unique.
    For example, - name: my-app-name.

  4. Deploy the application

    ibmcloud app push
  5. View the application online at the app URL.
    For example: https://my-app-name.mybluemix.net

Directory structure

.
├── app.js                      // express routes
├── config                      // express configuration
│   ├── error-handler.js
│   ├── express.js
│   └── security.js
├── manifest.yml
├── package.json
├── public                      // static resources
├── server.js                   // entry point
├── test                        // unit tests
├── training
│   └── weather_data_train.csv  // training file
└── views                       // react components

License

This sample code is licensed under Apache 2.0.
Full license text is available in LICENSE.

Contributing

See CONTRIBUTING.

Open Source @ IBM

Find more open source projects on the IBM Github Page.