smer-visual 1.0.1

This package provides means of explainable classification of images with LLM-generated text descriptions, that are then analyzed using the Self Model Entities Related (SMER) explanation technique.

SMERVisual

SMERVisual is a Python package designed for explainable machine learning using the SMER method. It provides an intuitive interface for analyzing image classification models by highlighting key features and generating bounding boxes around important words in source images.

The package supports both OpenAI API models and local language models, offering flexibility in model selection.

---

Installation

Install SMERVisual using pip:

pip install smer-visual

---

Features

ImageClassifier

The ImageClassifier class performs logistic regression-based classification on image datasets while computing LIME and SMER values for explainability. Key features include:

• Classification of image datasets using pre-trained language models
• Computation of LIME and SMER scores for each word in the dataset
• Visualization of most important words for explainability

BoundingBoxGenerator

The BoundingBoxGenerator class enhances model interpretability by overlaying bounding boxes on images, highlighting critical words identified in classification. It:

• Uses ImageClassifier output to detect significant words
• Supports vision models from OpenAI and open-source alternatives
• Enables zero-shot object detection for explainable AI

---

Usage Example

Here's a simple way to use SMERVisual for explainable image classification:

from smer_visual import ImageClassifier, BoundingBoxGenerator

# Initialize and train the classifier
classifier = ImageClassifier(openai_model="openai", openai_key='123abc123')
classifier(data="path/to/dataset")
influential_words = classifier.get_top_words()

# Generate bounding boxes for top words
bbox_generator = BoundingBoxGenerator(data='path/to/dataset', top_words=influential_words,
                                      local_model_path='path/to/local/model')
bbox_generator()

---

Why Use SMERVisual?

• Explainable AI – Provides insight into model decision-making
• Model-Agnostic – Compatible with OpenAI APIs and open-source models
• Zero-Shot Detection – No additional training data required
• Easy Integration – Simple API for seamless use with existing machine learning workflows

---

Contributing

Contributions are welcome. If you’d like to contribute, follow these steps:

1. Fork the repository
2. Create a new branch (git checkout -b feature-branch)
3. Commit changes (git commit -m "Add new feature")
4. Push to the branch (git push origin feature-branch)
5. Open a pull request

---

License

SMERVisual is released under the MIT License.

---