llm-img-cat 0.1.0

LLM-based image categorization tool with focus on book cover detection

LLM Book Cover Detector

A Python package for detecting and analyzing book covers using Qwen Vision-Language model.

Features

• Accurate book cover detection
• Similarity scoring (0-100%)
• Concise reasoning
• Beautiful CLI interface
• JSON response format
• Raw API response display
• Rich output formatting

Installation

1. Clone the repository:

git clone <repository-url>
cd llm_image_categorizator

2. Create and activate virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Configure environment:

cp .env.template .env
# Edit .env with your DASHSCOPE_API_KEY

Usage

CLI Usage

The simplest way to use the book cover detector is through the CLI:

python scripts/llm_img_cat_cli.py path/to/image.jpg

This will:

1. Analyze if the image is a book cover
2. Provide a similarity score (0-100%)
3. Give a concise 5-word reasoning
4. Show raw API response

Python API Usage

from llm_img_cat.categorizer import llm_img_cat

# Analyze an image
result = llm_img_cat("path/to/image.jpg")

print(f"Is book cover: {result['is_category']}")
print(f"Similarity score: {result['confidence']}%")
print(f"Reasoning: {result['reasoning']}")

Example Output

╭── Book Cover Detection Results ───╮
│ Is Book Cover    │ Yes           │
│ Similarity Score │ 90%           │
╰────────────────────────────────╯
╭── Reasoning ──────────────────────╮
│ Text and design typical of books  │
╰────────────────────────────────╯

Configuration

Required environment variables in .env:

• DASHSCOPE_API_KEY: Your Qwen API key
• DEFAULT_MODEL: Default is "qwen2.5-vl-3b-instruct"

Development

• Run tests: ./run_qwen_tests.sh
• Check code: scripts/lint.sh
• Build docs: scripts/build_docs.sh

License

MIT License

Contributing

See CONTRIBUTING.md for guidelines.