@Kalaivanicholan26

This is another feature for an app that I’m making, and it is a question builder for appointments. You pick an appointment type or clinical setting and specifics, and AI generates questions based on that information. For the Question Builder, I built the feature in Flutter using a stateful widget so the generated questions can dynamically appear and update on the screen. I created functions to send the user’s input to my backend API and process the returned questions so they can be displayed as a list of individual question cards. I also added state management to track when questions are being generated, store the generated questions, and update the UI without rebuilding the entire app manually. To make the interface easier to use, I implemented a dedicated scrollable section for the generated questions so users can browse through them while keeping the rest of the page in place. One of the main challenges was connecting the asynchronous API response to Flutter’s UI state and making sure the questions appeared correctly after the request finished. I’ve also started working on a health summary generated based on what a user enters. This is the first screen of information where a user adds all their medical conditions, with options to edit or delete. The health summary feature is still ongoing, but hopefully I’ll be back with a devlog soon for that! I don’t think it actually took me like 10 hours but thats how much is logged.

📅 Milestone OverviewSuccessfully engineered a secure, full-stack, dual-language cloud data pipeline to deliver contextual, natural-language healthcare explanations. The architecture decouples the user interface from data processing nodes to guarantee compliance and protect core system secrets.🛠️ Technical ImplementationFrontend Matrix (Mobile Node): Programmed a responsive, single-screen conversation tree interface using Flutter and Dart. Integrated asynchronous network sockets to stream JSON transaction payloads natively.Backend Middleware (Gateway Node): Built a standalone server cluster using Python and Flask to intercept traffic, manage localized data operations, and act as a cryptographic firewall.Security & Compliance Framework: Configured isolated local environment vaults (.env) to eliminate source-code key extraction risks. Structured automated text-sanitization filters using Regular Expressions (RegEx) to strip personal identifiers before data transit.Context Bridge Logic: Implemented dynamic conversation-array mappings. The system appends historical assistant dialogue states directly into user payloads, preventing semantic prompt leaks and enabling complex follow-up reference trees.📈 Current Performance MetricsNetwork Status: Fully verified. Local loopback connections configured via localhost:8000/explain bypass local platform sandboxes smoothly.UI Responsiveness: Integrated asynchronous UI event bindings (setState) coupled with active scroll physics handlers to automatically snap viewports onto new runtime bubble components.Data Integrity: 100% accuracy achieved on edge-case follow-ups (e.g., multi-phase medical breakdowns) using strict factual temperature constraints (0.3).🚀 Next Flight Maneuvers: Migrate the local Python script cluster into an always-on cloud hosting platform architecture (Render/Railway). Integrate the AI Explanation module with the application’s pre-existing offline database reference screens.

So for anyone new, I’ve been working on a healthcare app for personal interests. I made this function where you search for a lab test, enter your value, and analyze to get some facts and what your result means. This operates based on the data I have stored in one of the files in my projects, and it gets what’s needed based on the user’s number and test. There are also tiles for common tests if you’d rather not search, just to make it a little more fun. For the future, I want to be able to add a function where users can upload photos or screenshots and add a toggle to switch between unit measurements for certain tests because the units aren’t universal for each test. I’ll do that if I have time, though, because I’m also currently working on other features and will keep anyone interested updated. Also, it only took this long. I manually entered the data because I wanted my own database so I could control what was being output to keep answers medically accurate, and I get sidetracked. If you made it this far, thanks for listening!

I’m currently working on increasing the amount of vocabulary in my medical terms database for my Flutter-based app.