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Examples

This folder contains examples that demonstrate various use cases and features of the entity-recognition library. The examples are categorized into three main sections: basic usage, advanced features, and integration samples. Each example is designed to showcase specific functionalities and provide you with code snippets that you can easily adapt and integrate into your own projects.

Basic Usage

Detailed Entity Recognition

The detailed_entity_recognition.py script serves as a starting point for understanding the core functionalities of the entity-recognition library. It demonstrates how to:

  • Extract entities from text
  • Classify the text into a topic
  • Generate technology recommendations based on the extracted entities

This example provides a solid foundation for working with the library and understanding its basic usage.

Advanced Features

Analyze and Visualize Results

For those looking to dive deeper into the capabilities of the entity-recognition library, the analyze_and_visualize_results.py script showcases advanced features. It illustrates how to:

  • Analyze the recognition results
  • Perform additional processing on the extracted entities
  • Visualize the extracted entities and recommendations using popular data visualization libraries

This example is perfect for users who want to explore more advanced techniques and gain insights from the recognition results.

Process and Visualize Results

The process_and_visualize.py script showcases how to process text using the entity-recognition library and visualize the results in a readable format. It demonstrates how to:

  • Create an instance of the EntityRecognizer
  • Process text and obtain the recognition results
  • Parse the JSON results into a Python list of dictionaries
  • Iterate over the results and print them in a formatted manner
  • Measure and display the execution time

This example is useful for understanding how to work with the recognition results and present them in a user-friendly way.

Integration Samples

Flask Integration

Integrating the entity-recognition library into web applications is a common use case. The flask_integration.py script provides a sample implementation of how to integrate the library with Flask, a popular Python web framework. It demonstrates:

  • Creating a Flask application
  • Exposing an API endpoint for entity recognition and recommendation generation
  • Handling requests and returning the recognition results

This example serves as a starting point for building web applications that leverage the entity-recognition library.

Streamlit Integration

For a more interactive and user-friendly experience, the streamlit_integration.py script showcases the integration of the entity-recognition library with Streamlit. Streamlit is a framework that allows you to easily build interactive web applications using Python. The example demonstrates:

  • Creating a simple web app with Streamlit
  • Accepting user input for text analysis
  • Displaying the recognized entities and recommendations in real-time

This example is ideal for users who want to create interactive applications and provide a seamless user experience.

To get started with any of these examples, make sure you have the entity-recognition library installed in your Python environment. You can then explore the code snippets provided in each example file and adapt them to fit your specific use case.

We encourage you to experiment with these examples, modify them, and use them as building blocks for your own projects. If you encounter any issues or have questions, please refer to the main README file for more information on installation, usage, and troubleshooting.

Happy exploring and building with the entity-recognition library!