https://github.com/jarif87/text-key-extractor
A Django web app that uses TF-IDF to extract keywords from text, featuring a modern, responsive UI with animated gradients and glassmorphism.
https://github.com/jarif87/text-key-extractor
django-application keywords-extraction pandas python scikit-learn
Last synced: about 2 months ago
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A Django web app that uses TF-IDF to extract keywords from text, featuring a modern, responsive UI with animated gradients and glassmorphism.
- Host: GitHub
- URL: https://github.com/jarif87/text-key-extractor
- Owner: jarif87
- Created: 2025-06-21T16:40:38.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-23T15:56:29.000Z (about 1 year ago)
- Last Synced: 2025-09-04T13:53:30.101Z (10 months ago)
- Topics: django-application, keywords-extraction, pandas, python, scikit-learn
- Language: Jupyter Notebook
- Homepage:
- Size: 18.8 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Keyword Extractor
Keyword Extractor is a Django-based web application that extracts key terms from input text using Term Frequency-Inverse Document Frequency (TF-IDF). The application provides a sleek, user-friendly interface where users can input text, submit it, and receive a list of the top keywords ranked by their TF-IDF scores. The backend leverages scikit-learn’s CountVectorizer and TfidfTransformer to process text, and the frontend uses a modern, responsive design with animated gradients and glassmorphism effects.
## Table of Contents
- Features
- Project Structure
- Prerequisites
- Installation
- Usage
- Screenshots
- Contributing
- License
- Contact
## Features
- Keyword Extraction: Extracts top keywords from user-provided text using a pre-trained TF-IDF model.
- Responsive Design: Modern interface with an animated gradient background, glassmorphism effects, and a responsive layout for desktop and mobile devices.
- User-Friendly: Simple text input form with a dynamic results display showing keywords and their TF-IDF scores.
- Pre-trained Models: Uses serialized CountVectorizer (cv.pkl) and TfidfTransformer (tfidf.pkl) models for efficient keyword extraction.
- Customizable: Easily extendable for additional preprocessing (e.g., stopwords, stemming) or UI enhancements.
## Project Structure
```bash
keyword-extractor/
├── .gitignore
├── db.sqlite3
├── manage.py
├── README.md
├── requirements.txt
├── myapp/
│ ├── admin.py
│ ├── apps.py
│ ├── cv.pkl
│ ├── feature_names.pkl
│ ├── models.py
│ ├── sustain.py
│ ├── tests.py
│ ├── tfidf.pkl
│ ├── urls.py
│ ├── views.py
│ ├── __init__.py
│ ├── migrations/
│ │ ├── 0001_initial.py
│ │ ├── __init__.py
│ │ └── __pycache__/
│ ├── static/
│ │ ├── css/
│ │ │ └── style.css
│ │ ├── images/
│ │ └── js/
│ │ └── script.js
│ ├── templates/
│ │ └── index.html
│ └── __pycache__/
└── myproject/
├── asgi.py
├── settings.py
├── urls.py
├── wsgi.py
├── __init__.py
└── __pycache__/
```
## Prerequisites
- Python: 3.11 or later
- Django: 4.x or compatible version
- Dependencies: Listed in `requirements.txt`
- Web Browser: Chrome, Firefox, or other modern browser
## Installation
```bash
# Clone the repository
git clone https://github.com/your-username/keyword-extractor.git
cd keyword-extractor
# Create a virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Apply database migrations
python manage.py migrate
# Collect static files
python manage.py collectstatic
# Run the development server
python manage.py runserver
```
Now open your browser and go to:
📍 http://127.0.0.1:8000/
## Usage
- **Input Text:** Enter your text in the input field.
- **Submit:** Click “Extract” to process and extract keywords.
- **Results:** View the top keywords and their TF-IDF scores in styled cards.
### Example
Input:
```
Neural network for pattern classification and generalization performance.
```
Output:
```
neural network: 0.454
pattern: 0.321
generalization: 0.312
classification: 0.298
```
## Screenshots
## Contributing
Contributions are welcome! To contribute:
```bash
# Fork the repository
git checkout -b feature/your-feature
# Make your changes
git commit -m "Add your feature"
# Push your branch
git push origin feature/your-feature
```