🧠 Lumi Alzheimer's and Elderly Care App - Backend (Flask API) This repository contains the backend code for the LUMI Alzheimer's and Elderly Care App 🧓👵. The backend powers all the core features such as reminders, face recognition, object detection, location tracking, real-time chat, and AI chatbot support. It is built using Flask and integrates with MongoDB, WebSocket, and AI models for intelligent support.

✨ Features

1. 📝 Reminders API

   📌 Create, update, retrieve, and delete reminders.

   ⚠️ Tag reminders as urgent or important.

   🔔 Notifications triggered for scheduled reminders.

2. 📸 Face Recognition API

   👤 Identifies familiar faces using the face_recognition Python library.

   📥 Accepts image input and returns recognition results.

3. 🔍 Object Detection API

   🤖 Detects common household or personal objects using YOLO.

   🏷️ Returns labels for detected objects from uploaded images.

4. 🌍 Location Tracking API

   📍 Tracks and stores users’ live location updates.

   🧭 Allows caregivers to monitor patient movements and receive alerts.

5. 💬 Real-time Chatroom (Flask-SocketIO)

   🧑‍🤝‍🧑 Enables communication between caregivers and patients.

   🔐 Secured using unique userID-based authentication and rooms.

   📨 Integrated with Expo push notifications for urgent messages.

6. 🤖 Chatbot Support

   🧠 Provides a built-in assistant to help elderly users perform tasks or answer questions.

   🗣️ Accessible via the chat interface to enhance user experience.

⚙️ Technology Stack

• 🔙 Flask — REST API and Socket.IO support

• 🧠 face_recognition — Face detection and recognition

• 🕵️ YOLO — Object detection

• 📦 MongoDB — NoSQL database

• 🌐 Flask-SocketIO — Real-time bi-directional chat

• ✨ Gemini / Custom AI model — For chatbot capabilities

• 🔐 Expo Push Notifications — For caregiver alerts and reminders

🚀 Getting Started

📋 Prerequisites

🐍 Python 3.x 📦 Flask & Flask-SocketIO 🗄️ MongoDB 📷 YOLO model setup 💬 Expo push notification token setup

🔧 Installation

git clone https://github.com/RaY8118/LUMI-Backend.git
cd LUMI-Backend

python -m venv venv
source venv/bin/activate  # For Windows: venv\Scripts\activate

pip install -r requirements.txt

⚙️ Configuration

Create a .env file for database credentials, secret keys, and token configs.
Set up YOLO model weights and config as per their documentation.

▶️ Run the server

python run.py

📡 API Endpoints

📝 Reminders

  GET /reminders
  POST /reminders
  PUT /reminders/:id
  DELETE /reminders/:id

📸 Face Recognition

  POST /detect_faces

🔍 Object Detection

  POST /detect_object

🌍 Location Tracking

  POST /location

💬 Chatroom

  WebSocket Endpoint: /chatroom
  Custom events for joining rooms, sending messages, and disconnecting

🤖 Chatbot

  POST /assistant: Send a message to the AI assistant and receive a response

🛠️ YOLO Model Setup

Download weights (e.g. yolov10b.pt) and config files.
Store them in a /model folder and load them via your object detection service.

📂 Folder Structure

/app: Contains the core logic for features like reminders, location tracking, chat, and more. This includes the implementation of Flask Blueprints for modular API handling.

/config: Holds the configuration settings for the application, including environment-specific variables and app settings.

/uploads: Stores user-uploaded files, such as profile images or other media for the app.

🤝 Contributing

Fork the project
Create a feature branch (git checkout -b feature/YourFeature)
Commit your changes (git commit -m 'Add feature')
Push to GitHub (git push origin feature/YourFeature)
Open a Pull Request

📜 License

This project is licensed under the Apache License 2.0. See the LICENSE file for full details.