Tabiya Documentation
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  • Tabiya Documentation
🇬🇧 English
  • Welcome
  • Overview
    • About Tabiya
    • The Global Youth Employment Challenge
      • The Role of Labor Market Intermediation
      • Digital Platforms and AI in LMIC Labor Market Intermediation
  • Open-Source Tech for Labor Markets
  • Our Tech Stack
    • Inclusive Livelihoods Taxonomy
      • Methodology
      • Why ESCO?
      • Core Taxonomy
      • Open Taxonomy Platform
      • Taxonomy CSV Format
    • Livelihoods Classifier
      • Getting Started
      • Web Application
      • Datasets
      • Training
      • Advanced Topics
      • Contributing Guide
      • FAQs
      • Demo Video
    • Compass
      • Technical Overview
      • UX Evaluation
        • UX Testing Discussion Guide
      • Roadmap
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  1. Our Tech Stack
  2. Livelihoods Classifier

Web Application

PreviousGetting StartedNextDatasets

Last updated 2 months ago

For ease of use, we developed a simple FullStack application (a Flask-based API as a BackEnd and jQuery FrontEnd) to analyze job descriptions and predict relevant occupations, skills, and qualifications using the entity-linking model.

Usage

First, activate the virtual environment as explained . Then, run the following command in python in the root directory:

Running the API

Run the Flask application:

python app/server/matching.py

Or set the Flask application environment variable and use the Flask command:

export FLASK_APP=app/server/matching.py
flask run --host=0.0.0.0 --port=5001

Example Usage

  1. Open the browser and navigate to http://127.0.0.1:5001/.

  2. Paste a job description into the provided text area.

  3. Click the "Analyze Job" button to send the job description to the /match endpoint.

  4. View the results under "Predicted Occupations," "Predicted Skills," and "Predicted Qualifications."

This app is just for demonstration purposes. If you wish to deploy this model, use a more reliable/secure strategy.

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