Text-Search-Image

This project implements an efficient image search system using the CLIP (Contrastive Language-Image Pre-training) model. It allows users to search for images with natural language queries, offering a seamless and intuitive way to interact with image datasets.

Features

• Image Processing: Load and process images from a specified directory.
• CLIP Model Integration: Encode images into embeddings using the CLIP model for precise image-to-text matching.
• Vector Database: Store image embeddings in ChromaDB for fast and scalable retrieval.
• Text-Based Search: Find relevant images based on natural language queries.
• User-Friendly Interface: A simple graphical interface for easy image search and browsing.

Requirements

• Python: Version 3.9.16
• Flet: For building the graphical user interface (GUI).
• CLIP Model: To generate high-quality embeddings from images.
• ChromaDB: For efficient storage and retrieval of image embeddings.

Installation

Follow these steps to set up and run the project on your local machine:

1. Clone the Repository:

   git clone https://github.com/tententgc/clip-image-search.git
   cd clip-image-search

2. Set Up a Virtual Environment (optional but recommended):

   python -m venv test
   source test/bin/activate  # On Windows, use `test\Scripts\activate`

3. Install Dependencies:

   pip install -r requirements.txt

Usage

1. Run the Application:

   python run.py

2. Using the Interface:

   • Enter the path to the directory containing your images.
   • Click the "Add" button to load and process the images into the system.
   • Enter a text query into the search box.
   • Click the "Search" button to retrieve matching images.

Project Structure

text_search_image_clip/
├── assets/                # Contains static assets like images and icons
├── data/                  # Stores processed images and embeddings
├── src/
│   ├── config.py          # Configuration settings for the application
│   ├── image_finder.py    # Handles image search functionality
│   └── image_generator.py # Manages image encoding and embedding generation
├── README.md              # Project documentation
├── requirements.txt       # List of dependencies
└── run.py                 # Main script to run the application