MomentSort：AI 婚禮相片整理工具

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built

MomentSort AI-Powered Wedding Photo Organizer

Wedding photographers often return from an event with thousands of photos. Finding a specific moment—like a bride's portrait, a groom's candid shot, or wedding decor can be time-consuming when everything is scattered across folders.

MomentSort is an AI-powered photo organization project designed to make searching and organizing wedding memories easier. It combines image embeddings, similarity-based search, blur detection, and photo grouping to help photographers find relevant images and organize event moments.

The goal is simple: spend less time manually browsing photos and more time delivering beautiful memories to clients.

Potential users include wedding photographers, photography studios, event photographers, and anyone managing large collections of event photos.

Demo

Live Demo: https://week2-project-1.onrender.com

Explore the API documentation and try the available demo search functionality.

The hosted version currently runs in demo mode. Full folder-based analysis is intended to run locally because it requires access to the user's photo folders and a heavier AI model.

Code

GitHub Repository: https://github.com/snehach03/week2_project

The repository contains the backend, API endpoints, search functionality, and the photo-analysis pipeline.

How I Built It

I built MomentSort using Python and FastAPI, with a focus on making AI-powered photo organization practical.

The project brings together:

FastAPI for the backend and API endpoints.

SigLIP image embeddings for representing images as vectors and enabling similarity-based search.

NumPy for vector operations and similarity calculations.

OpenCV for blur detection and image-quality checks.

Photo grouping and duplicate detection to help organize related images and identify similar shots.

Render for hosting a lightweight demo of the application.

The project separates the lightweight hosted demo from the heavier local analysis pipeline. This allows users to explore the available search functionality online without requiring the full AI model to run on a memory-constrained hosting service.

Why Does Open Innovation Matter?

Open innovation made it possible to experiment with AI-powered image understanding without building an entire computer vision stack from scratch.

Open-source tools and model ecosystems gave me access to image embeddings, vector operations, image-processing libraries, and backend frameworks that I could combine into a practical application.

It also helped me understand the real-world engineering trade-offs behind AI products: model size, memory constraints, deployment environments, and the difference between a local workflow and a hosted service.

For me, the biggest lesson is that building with AI is not just about choosing a model. It is about making the complete system useful, accessible, and reliable.