HomeMemory {Memo}

Laiba Ashfaq — Sun, 04 Oct 2026 22:52:56 +0000

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

What I Built

HomeMemory is a private memory for the small things families keep forgetting. Mother,sister,father and I are always asking: where are the spare keys, what's the Wi-Fi password, where did the wallet go, which hospital receipt was that, when does this medicine expire.

You take a photo, add a short note ("wallet on the side table in my bedroom"), and a mascot called Memo remembers it. Later you ask in plain English, Urdu or Roman Urdu and get an answer with the source photo. A "Due soon" tab flags medicines and documents before they expire.

I built it for my Mother, who is not at the age to remember everything so 1 time she forget her credit card in some shop so we all are very concern i build this for her so she is in ease .

Demo

Code

laiba-Ashfaq / Home-Memory

A private memory assistant for the small things families keep forgetting.

HomeMemory

A private memory assistant for the small things families keep forgetting.

About

HomeMemory is a personal memory assistant designed to help families remember where important everyday things are and keep track of dates such as document or medicine expiry dates.

The idea came from a simple experience: my mother once left her credit card behind at a shop. It made me think about how often we forget small but important things at home — where we kept our wallet, keys, receipts, documents, or other belongings.

Instead of expecting someone to remember everything, HomeMemory lets them simply ask.

What It Does

With HomeMemory, you can:

Take or upload a photo of an item.
Add a short note about where it is.
Use an open model to analyze the photo and extract useful details.
Review and edit the information before saving it.
Ask questions about saved items in English, Urdu…

View on GitHub

Live Deploy :
http://home-memorygit-an9izpjmre3yesgikuoybq.streamlit.app/

How I Built It

Gemma 4 runs locally through Ollama. It reads each photo and returns structured JSON (category, item, description, readable text, expiry date) and writes the final answers.
EmbeddingGemma turns each memory into a vector, so "extra chabi" can still find "spare keys".
MongoDB Atlas Vector Search stores the memories and finds the closest match to a question.
Streamlit powers the interface, including a dark mode and an animated mascot, Memo.

What I did to keep answers reliable:

Forced a fixed JSON schema and temperature 0, so the model can't invent categories.
Made your own note the source of truth for where something is.
Added a confirm-and-edit step before anything is saved.
Added a match threshold, so Memo says "I couldn't find that" instead of guessing.
Wrote a small test script: top-1 retrieval accuracy was [X/Y = 78%] on 10 real items.
What went wrong: a real failure, my wallet was first labelled "document" because of the cards inside, and Then i add edit button and dropdown menue chnage into add also button for belongings .
result: it takes 10-15 minutes to find these things before This chatbot ,after this it goes to 2-3 minutes maximum

Why Does Open Innovation Matter?

This ChatBot holds Wi-Fi passwords, ID documents, medical receipts and where the jewelry is kept. I didn't want to send those photos to a closed API.

Because Gemma is open-weight, the photo reading and answering run on my own laptop. The images never leave it, there are no per-request costs, and I could change the prompts, schema and model size freely.

One honest limit: the text records and their embeddings are stored in MongoDB Atlas, not locally. The same code also works against a local MongoDB.

My Agent Session

I built HomeMemory by working with Claude in a chat window, then running and testing
everything on my own laptop. I didn't use a coding-agent tool, so I have no agent-session
file to share. The main fixes I made along the way:

wallet was first labelled "document"; fixed with a new category and a stricter prompt
search threshold tuned so Memo says "not found" instead of guessing
UI fixes for dark mode and the upload button

Prize Categories

Best Use of Gemma: Gemma 4 runs locally through Ollama. It reads each photo and returns structured JSON, and it writes the answers. EmbeddingGemma creates the embeddings used for search.
Best Use of MongoDB Atlas: Atlas stores every memory and Atlas Vector Search finds the closest match to a question.
Best Use of ElevenLabs: I used ElevenLabs to generate the narration for my demo video.
Best Use of GitHub Copilot: I used Copilot in VS Code to fix errors i encounter like toggle button not working so i had to fix it using copilot ollama takes to much time so i also fixed this issue it now reads images quickly with better score.

DEV Community: Laiba Ashfaq

HomeMemory {Memo}

What I Built

Demo

Code

laiba-Ashfaq / Home-Memory

A private memory assistant for the small things families keep forgetting.

HomeMemory

About

What It Does

How I Built It

Why Does Open Innovation Matter?

My Agent Session

Prize Categories