📸 Embed-Photos 🖼️

Welcome to Embed-Photos, a powerful photo similarity search engine built by @harperreed! 🎉 This project leverages the CLIP (Contrastive Language-Image Pre-training) model to find visually similar images based on textual descriptions. 🔍🖼️

🌟 Features

🚀 Fast and efficient image search using the CLIP model
💻 Works on Apple Silicon (MLX) only
💾 Persistent storage of image embeddings using SQLite and Chroma
🌐 Web interface for easy interaction and exploration
🔒 Secure image serving and handling
📊 Logging and monitoring for performance analysis
🔧 Configurable settings using environment variables

Screenshot

📂 Repository Structure

embed-photos/
├── README.md
├── generate_embeddings.py
├── requirements.txt
├── start_web.py
└── templates
    ├── README.md
    ├── base.html
    ├── display_image.html
    ├── index.html
    ├── output.txt
    └── query_results.html

generate_embeddings.py: Script to generate image embeddings using the CLIP model
requirements.txt: Lists the required Python dependencies
start_web.py: Flask web application for the photo similarity search
templates/: Contains HTML templates for the web interface

🚀 Getting Started

Clone the repository:

git clone https://github.com/harperreed/photo-similarity-search.git

Install the required dependencies:
```
pip install -r requirements.txt
```
Configure the application by setting the necessary environment variables in a .env file.
Generate image embeddings:
```
python generate_embeddings.py
```
Start the web application:
```
python start_web.py
```
Open your web browser and navigate to http://localhost:5000 to explore the photo similarity search!

Todo

Use siglip instead of clip
add a more robust config
make mlx optional

🙏 Acknowledgments

The Embed-Photos project builds upon the work of the Apple (mlx!), the CLIP model and leverages various open-source libraries. We extend our gratitude to the authors and contributors of these projects.

Happy searching! 🔍✨