fakenews-ussu321 2.0.0

NeuroGuard FND - Fake News Detection Desktop Application

NeuroGuard FND — Fake News Detection

PyPI Package: fakenews-ussu321
Python Package: fakenews_ussu321
Version: 2.0.0
Developer: Mohammed Usman
GitHub: @issu321
Website: issu321.github.io

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Overview

NeuroGuard FND is a professional desktop application for detecting fake news using an Enhanced Ensemble NLP model. It combines TF-IDF vectorization, statistical feature engineering, sentiment analysis, and machine learning to classify news articles as Real or Fake with confidence scores and explainable AI insights.

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Features

• Single Article Analysis — Paste any news article and get instant real/fake classification with confidence score
• Batch Processing — Upload CSV or Excel files to analyze multiple articles at once
• Explainable AI — Get detailed explanations of why an article was flagged (sensationalist language, emotional tone, capitalization, etc.)
• Sentiment Analysis — Real-time polarity and subjectivity scoring via TextBlob
• User Authentication — Secure registration and login with Flask-Login
• Desktop App — Native desktop window powered by pywebview (no browser needed)
• Web Server Mode — Optional --server-only flag for browser-based deployment
• Model Statistics API — Compare ensemble model performance metrics

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Installation

pip install fakenews-ussu321

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Usage

Desktop Mode (Default)

fakenews

Or:

python -m fakenews_ussu321

Launches a native desktop window at http://127.0.0.1:5000/.

Web Server Mode

fakenews --server-only

Or:

python -m fakenews_ussu321 --server-only

Runs the Flask development server accessible from any browser.

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Package Structure

fakenews_ussu321/
├── __init__.py          # Package metadata
├── __main__.py          # python -m entry point
├── cli.py               # CLI entry point (fakenews command)
├── desktop.py           # pywebview desktop launcher
├── app.py               # Flask application factory
├── config.py            # Configuration settings
├── models/
│   ├── __init__.py      # SQLAlchemy & LoginManager init
│   ├── predictor.py     # FakeNewsPredictor (ensemble NLP model)
│   └── user.py          # User database model
├── routes/
│   ├── __init__.py      # Blueprint registration
│   ├── main.py          # Public pages (index, about, contact, features)
│   ├── auth.py          # Authentication (login, register, logout)
│   └── dashboard.py     # Prediction API & dashboard routes
├── templates/           # Jinja2 HTML templates
├── static/              # CSS, JS, images
├── Datasets/            # Sample datasets
├── instance/            # SQLite database storage
└── *.pkl                # Trained ML models

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Dependencies

Package	Version
Flask	>=2.3.0
Flask-SQLAlchemy	>=3.0.0
Flask-Login	>=0.6.0
Werkzeug	>=2.3.0
pandas	>=2.0.0
numpy	>=1.24.0
scikit-learn	>=1.3.0
joblib	>=1.3.0
textblob	>=0.17
openpyxl	>=3.1.0
pywebview	>=4.4.0

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Model Architecture

The Enhanced Ensemble NLP model extracts the following features:

1. TF-IDF Vectorization — Term frequency-inverse document frequency
2. Statistical Features (6 features):
   • Capitalization ratio
   • Exclamation mark count
   • Question mark count
   • Word count
   • Average word length
   • Sensationalist word count
3. Sentiment Features (2 features):
   • Polarity (via TextBlob)
   • Subjectivity (via TextBlob)

All features are combined into a sparse matrix and fed into an ensemble classifier.

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API Endpoints

Endpoint	Method	Auth	Description
/	GET	No	Landing page
/features	GET	No	Features page
/about	GET	No	About page
/contact	GET	No	Contact page
/auth/register	GET/POST	No	User registration
/auth/login	GET/POST	No	User login
/auth/logout	GET	Yes	User logout
/auth/api/check-username/<username>	GET	No	Username availability
/dashboard/	GET	Yes	Main dashboard
/dashboard/api/predict	POST	Yes	Single article prediction
/dashboard/api/predict-batch	POST	Yes	Batch file prediction
/dashboard/api/stats	GET	Yes	Model performance stats
/api/model-stats	GET	No	Best model statistics

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Developer

Mohammed Usman

• Email: [e-mail]
• Phone: [phone]
• GitHub: issu321
• Website: issu321.github.io

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License

MIT License

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