Keyword Extraction Web App

Project Description

This tutorial application extracts keywords from a document or user-suggested text.

You will learn how to:

• create applications in Flask
• extract text from pdf, doc, txt files
• use keyBert to extract keywords

The project structure includes:

• A code directory containing the Flask application.
• A tutorial directory with Jupyter notebooks that provide a detailed examination of keyword extraction algorithms and explanations of the application code.

Installation

To set up this project locally, follow these steps:

1. Clone the Repository

   git clone https://github.com/mlsamurai/keyword_extraction.git
   cd keyword_extraction
   

2. Navigate to the Code Directory

   cd code
   

3. Install Requirements Ensure you have Python 3.6+ installed, then run:

   conda env create -f environment.yml
   conda activate nlp
   

How to Run

1. Start the Flask App From the code directory, execute:

   python app.py

   This will start the server locally on http://127.0.0.1:5000/.

2. Access the Web Interface
   Open a web browser and go to http://127.0.0.1:5000/ to start using the application.

Tutorials

Explore the tutorial directory for notebooks:

• Part 1. Algorithms.ipynb: Breaks down various keyword extraction algorithms using the NIPS Papers dataset.
• Part 2. Application.ipynb: Contains a detailed walkthrough of the application code.

References

• KeyBERT: Maarten Grootendorst. KeyBERT: Minimal keyword extraction with BERT. Available at KeyBERT GitHub.