🧠 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.

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✨ 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.

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⚙️ 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

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🚀 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

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📡 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

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🛠️ 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.

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📂 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.

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🤝 Contributing

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

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📜 License

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