Hi everyone, I wanted to share a portfolio project I just finished.. # MedVerify — AI-Powered Medicine Authenticator 🛡️ MedVerify is a production-grade, offline-capable Progressive Web Application (PWA) designed to detect counterfeit medicines in rural and low-connectivity environments across India. It uses an edge-deployed 5-layer AI analysis engine (Visual AI, OCR, Barcode/QR, Pricing Intelligence, and Pharmacy Registry Checks) backed by a massive locally-cached CDSCO database. ![Architecture: Zero-Storage, Edge-First] 📡 Hybrid "Offline-Second" Architecture: Gracefully degrades from Cloud AI to a local IndexedDB cache when internet is lost. Online Mode (Full Power): Uses LLaMA 3, EasyOCR, and Python to extract text from photos and answer complex usage questions. Offline Mode (Backup): AI image extraction and LLM answering are disabled. Users manually enter the medicine name or barcode to instantly verify it against a highly-compressed 50MB local CDSCO registry cached on their mobile phone. 🔒 Zero-Storage Security Architecture: All uploaded images are processed entirely in-memory using BytesIO. No user data, prescriptions, or images are ever written to disk, ensuring 100% HIPAA and privacy compliance. 🤖 Defensive AI Pipeline: Employs rigorous LLM prompt injection defenses, strict max_tokens limits (to prevent token-exhaustion attacks), and real-time usage logging via Groq's high-speed inference engine. 🚧 Hardened API Infrastructure: Protected by multi-tier rate limiting (global 60/min, AI routes 5/min), strict CORS policies, and rigorous MIME-type and byte-signature validation for all file uploads. ⚡ PWA Edge Caching: Installs directly to iOS and Android home screens, bypassing App Store delays, with fully automated background syncing. 🌐 Bilingual (English/Hindi): Deep context translation powered by deep-translator to support grassroots health workers. Frontend (Vercel) Framework: React 18 + Vite + TypeScript Styling: Tailwind CSS + shadcn/ui + ScrollReveal (micro-animations) Offline Engine: Workbox Service Workers + IndexedDB (idb) Routing: React Router DOM Backend (HuggingFace Spaces) Framework: Python Flask (Application Factory Pattern) AI / Inference: Groq API (LLaMA 3 70B) + EasyOCR + OpenCV Database: Google BigQuery (Parameterized Queries for SQLi prevention) Security: Flask-Limiter, Flask-CORS, secure HTTP headers (HSTS, CSP, X-Frame-Options) This application has been meticulously hardened for production scale: Dependency Pinning: All packages strictly version-pinned to prevent supply chain attacks. HTTP Security Headers: Enforced Content-Security-Policy, X-Content-Type-Options, and Strict-Transport-Security. Graceful Error Boundaries: Stack traces and internal server paths are strictly hidden behind NODE_ENV checks in production. Token Cost Management: Granular throttling and token-capping prevent malicious automated scraping and API bill shocks. git clone https://github.com/Amit4517187/med-verify-authenticator.git cd med-verify-authenticator npm install npm run dev Frontend runs at http://localhost:5173 Ensure you have Python 3.10+ installed. cd backend # Assuming you have cloned the backend repo python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate pip install -r requirements.txt Create a .env file in the backend root: GROQ_API_KEY=your_key_here GOOGLE_APPLICATION_CREDENTIALS=/path/to/your/gcp/key.json MEDVERIFY_ACCESS_TOKEN=your_secure_secret_here Run the server: gunicorn "app:create_app()" -w 4 -b 0.0.0.0:5000 This project was architected to solve a genuine, life-threatening problem: ₹6,000 Cr worth of counterfeit medicines circulating in India. Instead of building a simple wrapper, MedVerify is built with "Resilient Engineering" in mind. Knowing that the primary users are ASHA workers and community pharmacists in tier-3 cities with patchy 3G connections, the application uses a Graceful Degradation approach: Cloud-Heavy Operations (Online): We offload OCR, image processing, and LLM generative answers to the HuggingFace backend because mobile browsers cannot handle heavy AI inference or massive batch-number cross-referencing without crashing. Edge-Caching Fallback (Offline): If the connection drops, the app switches to an offline backup mode. The LLM is disabled, and the app relies strictly on the user manually typing the name or barcode to verify against a 50MB IndexedDB CDSCO cache. This strict separation ensures that the app never just shows a "No Internet" screen; it always provides a critical path to verification. Built with ❤️ to protect lives.