Spam-Detector

A lightweight Python-based SMS spam detector using NLP, SVM, and Flet for spam/ham classification.

Overview

Spam-Detector classifies SMS messages as spam or ham using Natural Language Processing (NLP) for text preprocessing, a Support Vector Machine (SVM) for machine learning-based classification, and a Flet GUI for user interaction. It offers a simple interface with theme switching and a standalone executable for offline use. Key features:

• NLP: Tokenization, stemming, and stopword removal for message preprocessing.
• SVM: Accurate spam/ham classification using a trained model.
• Flet GUI: User-friendly interface for instant predictions with light/dark theme support.
• Offline support with bundled models and NLTK data.
• Error handling displayed in the GUI.

Installation

1. Clone the repository:

   git clone https://github.com/AliAminiCode/Spam-Detector.git
   cd Spam-Detector

2. Install dependencies:

   pip install -r requirements.txt

3. Download NLTK data:

   python -c "import nltk; nltk.download('stopwords'); nltk.download('punkt_tab')"

Usage

Run the GUI:

python src/spam_detector_app.py

Enter a message (e.g., "Win a free iPhone!" for spam) to see predictions.

To train the model:

python src/train_spam_classifier.py

Screenshots

Check out Spam-Detector in action:

• Ham Prediction(Dark Mode):
  

• Spam Prediction(Light Mode):
  

Download Executable

Try the standalone executable, which runs completely offline without needing an internet connection: exe-v1.0 release:

Contribute

Found a bug? Report it at https://github.com/AliAminiCode/Spam-Detector/issues.
Developed by Ali Amini.
Licensed under the MIT License.