Multimodal Search Engine

This project is a Multi-Modal Search Engine developed using CLIP by OpenAI, with Flask API for backend and HTML/CSS for the frontend web application.

Introduction

This project provides a seamless web interface where users can input text queries, and the system retrieves relevant images based on the textual description based on CLIP architecture read the paper.

Take a look

Demo Video

• This video demonstrates how to use our project's main feature.

How to use for your own images?

• Sample data of 130 images is present in the file or
• See the video or
• Place your images in src/minidata
• Run the notebook src/image-processor
• Move the data in src/image_embeddings & the data in src/minidata to flaskapp/image_embeddings & flaskapp/static respectively (caution: transfer the data, not the directories)

Features

• Multi-Modal Search: Users can input textual descriptions of images to retrieve relevant images.
• Intuitive Web Interface: The frontend is built using React to provide a user-friendly experience.
• Scalable Backend: Flask API serves as the backend, handling requests and interacting with the CLIP model.

Clone the repository:

git clone https://github.com/ahmedembeddedx/Multi-Modal_Search_Engine.git

Usage

Start the backend server:

cd flaskapp/
flask run

Access the web application in your browser at http://127.0.0.1:5000/.

Stacks

• OpenAI for developing CLIP.
• Flask for the backend framework.

Future Expectences

• Shift the app to ReactJs
• Use ImageBind by MetaAI
• More accurate modal evaluation
• Integrate Audio & Video Functionality