📧 SMS Spam Classifier with PyTorch

📖 Overview

This project predicts whether a given SMS message is spam or not using a feedforward neural network implemented in PyTorch.

• 🧠 Neural Network with multiple hidden layers using LeakyReLU activation function and Dropout
• ⚖️ Binary Cross-Entropy (BCE) Loss for binary classification
• 🔄 Adam optimizer for training
• 🔀 Train/Validation/Test split with mini-batches for robust evaluation
• 📈 CountVectorizer feature extraction for converting text messages into numerical features
• 💾 Saving and loading vectorizer for consistent preprocessing during inference
• 🎨 Interactive Gradio Interface for real-time prediction

---

🖼️ Application Screenshot

Below is a preview of the Gradio Interface used for real-time classification:

---

🧩 Libraries

• PyTorch – model, training, and inference
• pandas – data handling
• matplotlib – loss visualization
• pickle – saving/loading vectorizer and trained model
• Gradio — interactive web interface for real-time model demos

---

⚙️ Requirements

• Python 3.13+
• Recommended editor: VS Code

---

📦 Installation

• Clone the repository

git clone https://github.com/hurkanugur/SMS-Spam-Classifier.git

• Navigate to the SMS-Spam-Classifier directory

cd SMS-Spam-Classifier

• Install dependencies

pip install -r requirements.txt

---

🔧 Setup Python Environment in VS Code

1. View → Command Palette → Python: Create Environment
2. Choose Venv and your Python version
3. Select requirements.txt to install dependencies
4. Click OK

---

📂 Project Structure

assets/
└── app_screenshot.png              # Screenshot of the application

data/
└── SMSSpamCollection               # Raw dataset

model/
├── sms_spam_classifier.pth         # Trained model (after training)
└── vectorizer.pkl                  # Saved CountVectorizer for text preprocessing

src/
├── config.py                       # Paths, hyperparameters, split ratios
├── dataset.py                      # Data loading & preprocessing
├── device_manager.py               # Selects and manages compute device
├── train.py                        # Training pipeline
├── inference.py                    # Inference pipeline
├── model.py                        # Neural network definition
└── visualize.py                    # Training/validation plots

main/
├── main_train.py                   # Entry point for training
└── main_inference.py               # Entry point for inference

requirements.txt                    # Python dependencies

---

📂 Model Architecture

Input → Linear(256) → LeakyReLU(0.01) → Dropout(0.5)  
      → Linear(128) → LeakyReLU(0.01) → Dropout(0.5)  
      → Linear(64)  → LeakyReLU(0.01) → Dropout(0.5)  
      → Linear(32)  → LeakyReLU(0.01) → Dropout(0.5)  
      → Linear(1)   → Sigmoid(Output)

---

📂 Train the Model

Navigate to the project directory:

cd SMS-Spam-Classifier

Run the training script:

python -m main.main_train

or

python3 -m main.main_train

---

📂 Run Inference / Make Predictions

Navigate to the project directory:

cd SMS-Spam-Classifier

Run the app:

python -m main.main_inference

or

python3 -m main.main_inference