submission from T101_CODE_NOT_FOUND

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submissions/README.md

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+# Health Assessment AI 🚑✨
+
+An **AI-driven personalized health assessment app** that predicts diabetes risk based on user health indicators.
+Built with **React + Supabase + TensorFlow.js**, this app provides actionable insights, secure data storage, and automated workflows.
+
+---
+
+## 🚀 Features
+
+* 🔐 **User Authentication** via Supabase
+* 🤖 **On-device AI predictions** with TensorFlow.js (client-side, fast & private)
+* 📊 **Diabetes risk assessment** with risk level, key factors, and recommendations
+* 💾 **Save assessments** to user dashboard (requires login)
+* ⚡ **Make.com workflow integration** for external automation
+* 🎨 **Modern UI** with Tailwind CSS + Lucide icons
+
+---
+
+## 👥 Team ID: T101
+
+| Team Member       | Role                                                                  |
+| ----------------- | --------------------------------------------------------------------- |
+| **Kriti Mahajan** | Team Leader – AI model development & Make.com AI workflow integration |
+| **Bhavisha**      | GitHub & Web Development – Repository management & core web app       |
+| **Kartik**        | Designing & Web Development – UI/UX & frontend implementation         |
+| **Krish**         | Designing & Presentation – Visual design & final presentation         |
+
+---
+
+## 🧩 Problem Statement
+
+Develop an **AI-powered personalized health assessment app** that:
+
+* Predicts **diabetes risk** based on user health indicators
+* Provides **actionable insights** & recommendations
+* Integrates **AI models, workflows, and user history tracking**
+
+---
+
+## 🛠️ Tech Stack
+
+* **Frontend:** React (TypeScript)
+* **Backend & Auth:** Supabase
+* **AI & ML:** TensorFlow.js (Client-side inference)
+* **Automation:** Make.com webhook integration
+* **UI:** Tailwind CSS + Lucide Icons
+
+---
+
+## ⚙️ Setup & Running Instructions
+
+### 1. Clone Repository
+
+```bash
+git clone <repository-url>
+cd <repository-directory>
+```
+
+### 2. Install Dependencies
+
+```bash
+npm install
+```
+
+### 3. Model Hosting
+
+* Place the **pre-trained TensorFlow.js model** files (`model.json` and weight files) inside:
+
+```
+/public/model/
+```
+
+### 4. Configure Supabase
+
+* Create a **Supabase project**.
+* Add `.env` file in root with:
+
+```env
+VITE_SUPABASE_URL=your-supabase-url
+VITE_SUPABASE_ANON_KEY=your-supabase-anon-key
+```
+
+* Set up authentication & create an **assessments** table in your database.
+
+### 5. Configure Make.com Webhook
+
+* Replace placeholder webhook URL with your **actual Make.com webhook** in the code.
+
+### 6. Run Development Server
+
+```bash
+npm run dev
+```
+
+Visit: [http://localhost:3000](http://localhost:3000)
+
+---
+
+## 📌 Important Notes
+
+* 🔑 User **must be logged in** to save assessments.
+* ⚡ Model inference runs **locally on the device** (private & fast).
+* 🔗 Webhook integration allows automation via Make.com.
+* 🔒 Keep API keys & webhook URLs **secure**.
+
+---
+
+## 📞 Contact & Contributions
+
+* Raise issues & PRs via **GitHub Issues**.
+* Contact **Kriti Mahajan (Team Lead)** through repository channels.
+
+💡 *If you found this project useful, don’t forget to give it a ⭐️!*
+
+---
+
+## ⚠️ Disclaimer
+
+> This app is **to assist not replace qualified healthcare professionals**
+> Always consult a **qualified healthcare professional** for medical concerns.
+
+---