This is a submission for the Amazon Q Developer "Quack The Code" Challenge: Crushing the Command Line

What I Built

I created AI Medicine Analyzer, an intelligent web app that helps users analyze medications using the power of AI. It allows users to type in the name of any medicine and receive:

  • A clear, structured breakdown of its uses, dosage, side effects, precautions, and interactions.
  • AI-powered analysis in natural language, accessible to non-medical users.
  • A dynamic, chat-like interface that feels intuitive and fast.

The problem it solves: many patients struggle to understand complex medication instructions or identify drug interactions. This app simplifies that process using LLMs and brings trustworthy drug information to users' fingertips.

Demo

Try the live app here: https://medilyze.netlify.app
(Hosted on Netlify for fast global access.)

Here are the screenshots of the chat interface:

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Code Repository

You can explore the full source code here: GitHub

How I Used Amazon Q Developer

To align with the Amazon Q Developer Challenge, I explored how Amazon Q could enhance the intelligence and automation of the medicine analyzer:

  • I used Amazon Q's code generation and task breakdown capabilities during development to refactor components and set up Google Genai API routes efficiently.
  • Q helped in identifying the best way to implement an input pipeline for medicine prompts and how to structure the React components dynamically.
  • I plan to integrate Amazon Q directly into the medicine explanation engine to replace the current LLM with a more secure, AWS-native option (via Bedrock or Q APIs).

Tips:

  • Amazon Q is great at turning vague tasks into code stubs quickly, which saved time especially when setting up Framer Motion and Tailwind utilities.
  • Use Q's "Ask to Refactor" and "Add Tests" features — they work well for React code and keep things clean.

This was a solo project by @sumitkcs

Future Enhancements

  • Integrate streaming AI responses for real-time feedback.
  • Add medicine interaction checker to detect potential conflicts.
  • Replace GenAI with Amazon Q for direct LLM inference via AWS Bedrock.
  • HIPAA-compliant version for enterprise-grade, privacy-first deployments.
  • Image support:
    • Upload and analyze medical reports, X-rays, and prescriptions.
    • AI will extract structured insights (e.g., detect abnormalities in scans or parse handwriting on prescriptions).