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Mridul Bagla
Mridul Bagla

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Built for friend: A Local-First Medication Tracker with Open-Weight AI

#devchallenge #weekendchallenge #hf26challenge

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

What I Built

I built Naman’s Local Med Tracker, a small command-line medication checklist for my friend Naman.

It helps him keep a list of medicines and label- or clinician-provided instructions, see the day’s reminder-time checklist, and record doses as taken or skipped. The information is saved locally, so it’s still there after the app closes.

It’s a simple routine aid—not a medical device. Naman enters reminder times based on instructions from a healthcare professional. The app doesn’t calculate doses, send background notifications, or replace medical advice.

Code

GitHub logo MRIDUL-MB / hacktoberfest-2026-med-track

Created a medicine tracker usign a open source model.

Naman's Local Med Tracker

A small medication-routine helper built for my friend Naman. It keeps a local medicine list, shows a daily dose checklist, and records doses as taken or skipped so it's easier to keep track of a routine.

What it does

  • Saves medicine names, label/clinician instructions, and reminder times to a JSON file on this computer.
  • Shows today's reminder checklist and keeps a taken/skipped history across restarts.
  • Optionally reformats instructions with the open-weight HuggingFaceH4/zephyr-7b-beta model.
  • Keeps the tracker usable without an API token or an internet connection.

Reminder times must come from Naman's label or healthcare professional. This app does not calculate doses, send background notifications, or provide medical advice. It is a checklist, not a substitute for a prescription or care.

Run it

Install dependencies:

python -m pip install -r requirements.txt
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Start the tracker:

python med_tracker.py
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Use Check today's doses to see the checklist and…

View on GitHub

How I Built It

I built the project in Python using Cursor Pro during Global Hack Week.

The core tracker works offline and doesn’t require an AI service. I added an optional feature that uses the open-weight Zephyr-7b-beta model through Hugging Face’s inference router to reformat text pasted from a label or clinician’s instructions.

The AI feature is opt-in. It sends only the text entered for reformatting to Hugging Face and its inference provider; it does not automatically send the locally saved medication list or dose history. The result is only a readability aid and should always be checked against the original instructions.

Why Does Open Innovation Matter?

Medication routines are personal, so I wanted the useful part of this project—the checklist and dose history—to work locally without sending that information to an AI service.

Open-weight models also make the optional AI feature easier to inspect and potentially swap or run differently in the future. For this version, inference is hosted, so using that feature requires internet access and a Hugging Face token. The project’s core tracking features don’t depend on it.

Prize Categories

  • Best Use of Open Source Models
  • Most Creative Solution for a Friend

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