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metadata

title: ShutterSearch
emoji: 📷
colorFrom: indigo
colorTo: purple
sdk: gradio
sdk_version: 6.18.0
python_version: 3.13.3
app_file: app.py
pinned: false
license: apache-2.0
tags:
  - track:backyard-ai
  - sponsor:openbmb
  - sponsor:modal-labs
  - sponsor:minicpm
short_description: Local-first photo search powered by MiniCPM-V-4.6
fullWidth: true

📷 ShutterSearch — Intelligent Photo Archive

ShutterSearch is a local-first, privacy-preserving semantic search and photography archive manager. It turns unstructured folders of raw photographs into organized, natural-language searchable catalogs without uploading your private master files to any third-party cloud.

🏆 Hackathon Submission Details

This project was built for the Hugging Face Build Small Hackathon under the following tracks:

Primary Track: Backyard AI (Local-first / Offline track)
Sponsor Award Compatibility:
- OpenBMB Awards: Powered by the flagship lightweight visual-understanding model MiniCPM-V-4.6 (≤7B params) for high-performance visual scene parsing.
- Modal Labs Awards: Seamlessly integrates with Modal Labs serverless GPU compute infrastructure to scale visual ingestion pipelines off-site, returning detailed annotations back to your local cache.

👥 The Team

Subash-Lamichhane (@Subash-Lamichhane)
najus (@najus)
Swikar Gautam (@SwikarG)

✅ Pre-Flight Validation Checklist

This section verifies compliance with the submission requirements of the Build Small Hackathon:

Stay under 32B: The core Vision Language Model used is MiniCPM-V-4.6 (8B/7B class model), and the text embedding model is all-MiniLM-L6-v2 (22M parameters). Combined, the total parameter footprint is less than 9B parameters, safely under the 32B limit.
Ship a Gradio App: Fully deployed as a native Gradio Application Space within the official Build Small organization on Hugging Face.
Record a Demo: A visual walkthrough demonstrating local indexing, search, selection, and download is linked below.
Post It: A public showcase of ShutterSearch has been published on social media.
Mind the GPU Limit: Fully self-contained. Local execution relies on local memory and GPU resources, while our offloaded inference operates within standard boundaries.

📹 Presentation & Links

Demo Video: https://www.youtube.com/watch?v=aTLrOBhSRwU&feature=youtu.be
Social Media Post: https://x.com/SUJANKOIRA96725/status/2066583761597436253

💡 The Problem & The Backyard Solution

Photographers manage massive directories of RAW/JPEG images across external drives. Managing them typically requires sacrificing ownership by uploading them to third-party image hosts, manually tagging files, or enduring slow loading speeds when viewing high-resolution imagery.

ShutterSearch solves this on your terms:

Local Caching & Privacy: Your original master image files never leave your machine.
Dual-Inference Pipeline (Local & Modal Labs): Run inference fully offline on your own local GPU, or scale up your pipeline using Modal Labs serverless containers to process large batches on cloud-based H100s, caching the resulting semantic captions back locally.
On-the-Fly WebP Thumbnails: Avoid high-resolution display lag. ShutterSearch caches images as lightweight WebP thumbnails (300px max, 70% quality) for smooth, lag-free visual scrolling.
Multi-Select Bulk Export: Select multiple images (indicated visually by inline ✅ overlay badges) across Search results or Collections to package and download high-resolution originals in a structured ZIP.

🛠️ Tech Stack & Model Selection

Component	Technology	Role
Core VLM	`openbmb/MiniCPM-V-4.6`	Scene parsing, composition classification, and tagging
Semantic Search	`all-MiniLM-L6-v2`	High-dimensional text-to-image semantic index maps
Inference Scaling	`Modal Labs` (Optional)	Serverless GPU execution for scalable batch parsing
Frontend UI	Gradio	Dark-workspace layout and interface state engine
Thumbnail Optimizer	`Pillow` (PIL)	Compresses files to WebP (300px, 70% quality)
Local Database	Flat JSON Storage	No bulky setups; simple, human-readable data mapping

Why MiniCPM-V-4.6?

We chose OpenBMB’s MiniCPM-V-4.6 because it matches or outperforms larger models (like Claude 3 Opus and GPT-4V) in optical character recognition (OCR), layout understanding, and fine-grained visual reasoning while remaining compact enough to run on standard consumer-grade workstations.

📦 Setting Up Locally

Prerequisites

Make sure your environment has Python 3.10+ and a GPU with at least 8GB VRAM (or a configured CPU environment).

Clone the repository:

git clone https://huggingface.co/spaces/build-small-hackathon/ShutterSearch
cd ShutterSearch

Set up a virtual environment and activate it:

python -m venv venv
# On Windows:
.\venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

Install dependencies:
```
pip install -r requirements.txt
```

Setup modal

modal setup
modal deploy modal_caption.py

Launch the application:
```
python app.py
```

🏆 Hackathon Details

Developed for the Hugging Face "Build Small" Hackathon (Backyard AI / OpenBMB Tracks). Focused on model-efficiency, local UI caching pipelines, high-fidelity source protection, and a professional workspace interface.