1.1 KiB
1.1 KiB
Product Context: PyNamer
Problem: Manually naming large numbers of image files is tedious and time-consuming. Generic filenames (e.g., IMG_1234.JPG) lack descriptive value, making it hard to find specific images later.
Solution: pynamer automates the process of generating descriptive filenames for images by leveraging the image understanding capabilities of multimodal LLMs.
User Experience:
- The user provides one or more image paths via the command line.
- The tool processes each image, interacts with an LLM (configured via
config.yaml), and renames the file with a descriptive, clean filename. - A dry-run option allows users to preview the changes without modifying files.
- Efficiency Enhancement: By resizing large images before sending them to the LLM, the tool aims to:
- Reduce the amount of data transferred.
- Potentially lower API costs (as some models charge based on input size/tokens).
- Speed up the processing time.
Target User: Individuals or teams dealing with many images who need a better way to organize and retrieve them based on content (e.g., photographers, researchers, content creators).