Model Explainability

A Shiny-based web application that enables users to upload images and analyze how deep learning models make predictions. The app provides two interpretability techniques:

1. Grad-CAM (Gradient-weighted Class Activation Mapping) – Highlights important regions in an image that contributed to a model's prediction.
2. SHAP (SHapley Additive exPlanations) – A game-theoretic approach that assigns importance values to each pixel.

This project focuses on XAI (Explainable AI) techniques for deep learning with a clean, scalable architecture.

🔥 Features

• ✅ Upload and Analyze Images – Users can upload an image and visualize explanations
• ✅ Multiple Model Support – Select from models like VGG16, ResNet50, MobileNetV2, and EfficientNet
• ✅ Grad-CAM Visualization – See heatmaps overlaying important regions
• ✅ SHAP Interpretability – Generate pixel-based importance explanations
• ✅ Confidence Threshold Slider – Customize the minimum prediction confidence for analysis
• ✅ Interactive UI – Built with Shiny and R, integrating Python via reticulate
• ✅ Modular Architecture – Clean separation of concerns with scalable folder structure

📁 Project Structure

model-explainability/
├── src/
│   ├── python/                    # Python explainability modules
│   │   ├── models/                # Model loading and management
│   │   ├── explainability/        # Grad-CAM and SHAP implementations
│   │   ├── utils/                 # Utility functions
│   │   └── explainability_api.py  # Main API interface
│   └── shiny/                     # R Shiny application components
│       ├── ui.R                   # User interface
│       ├── server.R               # Server logic
│       └── modules/               # Modular Shiny components
├── tests/
│   ├── python/                    # Python unit tests
│   └── r/                         # R tests
├── assets/
│   ├── images/                    # Sample images and test data
│   └── docs/                      # Documentation and papers
├── config/                        # Configuration files
├── outputs/                       # Generated explanations (gitignored)
├── requirements.txt               # Python dependencies
├── app.R                         # Main Shiny app entry point
└── README.md

🛠️ Installation & Setup

Prerequisites

• R (>= 4.0)
• Python (>= 3.8)
• Shiny Server (for deployment)

1️⃣ Clone the Repository

git clone https://github.com/rodrick-mpofu/model-explainability.git
cd model-explainability

2️⃣ Install Python Dependencies

pip install -r requirements.txt

3️⃣ Install R Dependencies

# Install required R packages
install.packages(c("shiny", "shinydashboard", "shinythemes", 
                   "shinycssloaders", "reticulate", "png"))

4️⃣ Configure Python Environment

# In R, configure reticulate to use your Python environment
library(reticulate)
use_python("/path/to/your/python")  # or use_virtualenv()

5️⃣ Run the Application

# Run the Shiny app
shiny::runApp("app.R")

🎯 How It Works

1. Upload an Image 📷
2. Select Model & Technique (Grad-CAM or SHAP) 🏗️
3. Adjust Confidence Threshold 🎚️
4. View Model Explanation 🔥 (Heatmaps for Grad-CAM / SHAP values for SHAP)
5. Interpret Results ✅

🐍 Python API Usage

You can also use the Python API directly:

from src.python.explainability_api import explain_image

# Generate Grad-CAM explanation
results = explain_image(
    img_path="assets/images/dog.jpg",
    model_name="vgg16",
    technique="gradcam",
    confidence_threshold=0.5
)

# Generate SHAP explanation  
results = explain_image(
    img_path="assets/images/dog.jpg",
    model_name="resnet50", 
    technique="shap",
    confidence_threshold=0.3
)

🧪 Testing

Run the test suite:

# Python tests
python -m pytest tests/python/

# R tests (if available)
Rscript -e "testthat::test_dir('tests/r')"

📚 Architecture Benefits

The new modular structure provides:

• Separation of Concerns: Clear separation between UI, business logic, and utilities
• Scalability: Easy to add new models and explanation techniques
• Maintainability: Well-organized code with proper imports and dependencies
• Testability: Dedicated test structure for both Python and R components
• Reusability: Python modules can be used independently of the Shiny app
• Framework Conventions: Follows best practices for both Python and R/Shiny

📬 Contact & Contributions

• 👤 Rodrick Mpofu
• 📧 Email: rodrickmpofu@gmail.com
• 🔗 GitHub: @rodrick-mpofu
• 🔗 LinkedIn: Rodrick Mpofu

📄 License

This project is available under the MIT License. See LICENSE file for details.