Health-Connect 🌐

Health-Connect is a modern and intelligent platform for booking medical appointments. It leverages AI-powered chatbot technology to help users quickly identify suitable specialties and seamlessly schedule appointments. This project integrates advanced technologies across the backend and frontend to deliver a user-friendly and efficient healthcare experience.

Key Features ✨

• 🤖 AI-Powered Chatbot: Assists users in determining the right medical specialty based on symptoms.
• 📅 Seamless Appointment Booking: Enables users to schedule appointments effortlessly through an intuitive interface.
• 🧠 Integrated Prediction System: Utilizes AI to predict potential health issues and suggest relevant specialties.
• 🔒 Secure Data Handling: Ensures privacy and security of user data.
• 📱 Cross-Platform Compatibility: Accessible from web and mobile devices.

Tech Stack 🛠️

Backend

• Spring Boot
  • Manages business logic and API endpoints.
  • Receives AI prediction results and processes appointment requests.
• Flask
  • Hosts the AI model for disease prediction.
  • Processes user inputs to provide predictions.

Frontend

• ReactJS

  • Delivers a responsive and dynamic user interface.
  • Fetches predictions and booking statuses from Spring Boot APIs.

• Flutter

  • Native mobile application for Android and iOS.
  • Implements platform-specific features and optimizations.
  • Material Design 3 components for modern Android UI.

Architecture Overview 🏗️

graph TB
    subgraph "Client Layer"
        UI["React.js Web Application"]
        Chat["Web Chatbot Interface"]
        Mobile["Flutter Mobile App"]
        MobileChat["Mobile Chatbot UI"]
    end

    subgraph "Back-end Layer"
        SB["Spring Boot Backend"]
        NLP["VNCoreNLP Service"]
        subgraph "AI Service Layer"
            Flask["Flask AI Service"]
            Model["ML Model"]
        end
    end

    subgraph "Database Layer"
        DB[("Database")]
        Cache[("Redis Cache")]
    end

    %% User Interactions
    WebUser(("Web User")) -->|"Accesses"| UI
    WebUser -->|"Interacts"| Chat
    MobileUser(("Mobile User")) -->|"Accesses"| Mobile
    MobileUser -->|"Interacts"| MobileChat

    %% Direct Communications
    UI -->|"REST API"| SB
    Chat -->|"WebSocket"| SB
    Mobile -->|"REST API"| SB
    MobileChat -->|"WebSocket"| SB

    %% Spring Boot Operations
    SB -->|"Stores Data"| DB
    SB -->|"Caches"| Cache
    SB -->|"Requests Prediction"| Flask

    %% NLP Flow
    Chat -->|"Vietnamese Text"| SB
    MobileChat -->|"Vietnamese Text"| SB
    SB -->|"Text Processing"| NLP
    NLP -->|"Processed Text"| SB

    %% AI Service Flow
    Flask -->|"Uses"| Model
    Model -->|"Returns Prediction"| Flask
    Flask -->|"Returns Result"| SB

    %% Style Definitions
    classDef userNode fill:#FFE5CC,stroke:#FF6600,stroke-width:3px,color:#000000,font-weight:bold
    classDef clientNode fill:#E6F3FF,stroke:#0066CC,stroke-width:3px,color:#000000,font-weight:bold
    classDef backendNode fill:#E6FFE6,stroke:#009900,stroke-width:3px,color:#000000,font-weight:bold
    classDef databaseNode fill:#FFE6E6,stroke:#CC0000,stroke-width:3px,color:#000000,font-weight:bold
    classDef aiNode fill:#F2E6FF,stroke:#6600CC,stroke-width:3px,color:#000000,font-weight:bold
    classDef nlpNode fill:#FFE6FF,stroke:#990099,stroke-width:3px,color:#000000,font-weight:bold

    %% Apply Styles
    class WebUser,MobileUser userNode
    class UI,Chat,Mobile,MobileChat clientNode
    class SB backendNode
    class DB,Cache databaseNode
    class Flask,Model aiNode
    class NLP nlpNode

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1. User Interaction: Users interact with the chatbot through the ReactJS and Flutter frontend.
2. Prediction Request: The chatbot sends user symptoms to the Flask backend.
3. AI Prediction: Flask processes the symptoms and predicts the likely health issue.
4. API Communication: Flask sends the prediction to the Spring Boot backend.
5. Appointment Management: Spring Boot provides the prediction result to the frontend and handles booking logic.
6. User Feedback: The frontend displays the predicted issue and allows users to confirm an appointment.

Installation and Setup 🚀

Prerequisites

• Java 17 or higher
• Python 3.9 or higher
• Node.js 16 or higher
• Flutter SDK
• MySQL database

Backend Setup

Spring Boot (Appointment Management)

1. Clone the repository:

   git clone --recurse-submodules https://github.com/Mixture-Tech/Doctor-Appointment.git

   cd back-end

2. Configure the application.properties file with your database credentials.
3. Build and run the application:

   ./mvnw spring-boot:run

Flask (AI Prediction)

1. Navigate to the AI folder:

   cd predict_disease_base_on_symptoms

2. Install dependencies:

   pip install -r requirements.txt

3. Run the Flask server:

   python scripts/predict.py

Frontend Setup

1. Clone the repository:

   git clone --recurse-submodules https://github.com/Mixture-Tech/Doctor-Appointment.git

   cd front-end

2. Install dependencies:

   npm install

3. Start the development server:

   npm start

Usage 🎯

1. Open the application in your browser.
2. Interact with the chatbot to input your symptoms.
3. View the suggested specialty and confirm your appointment.

Project Directory Structure 📂

graph TB
    Root[Health-Connect Project]

    Root --> IDE[.idea]
    Root --> VSCode[.vscode]

    Root --> App[app]

    Root --> Assets[assets]
    Assets --> Images[images]

    Root --> Backend[back-end]
    Backend --> SpringBootApp[Spring Boot Application]
    SpringBootApp --> SpringConfig[Configuration]
    SpringBootApp --> SpringControllers[Controllers]
    SpringBootApp --> SpringServices[Services]
    SpringBootApp --> SpringModels[Models]
    SpringBootApp --> SpringRepositories[Repositories]

    Root --> Frontend[front-end]
    Frontend --> ReactApp[React Application]
    ReactApp --> Components[Components]
    ReactApp --> Pages[Pages]
    ReactApp --> Assets[Assets]
    ReactApp --> Services[API Services]

    Root --> AIModule[predict_disease_base_on_symptoms]
    AIModule --> FlaskApp[Flask Application]
    FlaskApp --> MLModels[ML Models]
    FlaskApp --> PredictionAPI[Prediction API]
    FlaskApp --> Utils[Utilities]

    Root --> GitModules[.gitmodules]
    Root --> CodeConduct[CODE_OF_CONDUCT.md]
    Root --> License[LICENSE]

    %% Style Definitions
    classDef default fill:#283446,stroke:#6B8299,stroke-width:2px,color:#fff,font-weight:bold
    classDef folder fill:#1F4068,stroke:#6B8299,stroke-width:2px,color:#fff,font-weight:bold
    classDef file fill:#162B3C,stroke:#6B8299,stroke-width:2px,color:#fff,font-weight:bold
    classDef root fill:#1A365D,stroke:#6B8299,stroke-width:3px,color:#fff,font-weight:bold

    %% Apply styles
    class Root root
    class IDE,VSCode,App,Assets,Backend,Frontend,AIModule folder
    class SpringBootApp,ReactApp,FlaskApp,Components,Pages,Services folder
    class SpringConfig,SpringControllers,SpringServices,SpringModels,SpringRepositories folder
    class MLModels,PredictionAPI,Utils folder
    class GitModules,CodeConduct,License file

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Contributing 🤝

We welcome contributions! Please read our Contributing Guide for more details.

License 📄

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments 🙏

• OpenAI for AI model inspiration.
• Spring Boot and Flask communities for backend support.
• ReactJS developers for a smooth frontend experience.

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Feel free to report issues or suggest improvements in our GitHub Issues section. Together, let's make healthcare more accessible and intelligent!

Contact:

• Email: vanhoang1231234@gmail.com