CliniqueAI

Next-Gen Clinical Intelligence — Bridging the gap between patient data and predictive care.

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CliniqueAI is a state-of-the-art SaaS platform designed to bridge the gap between clinical data and predictive diagnostics. By utilizing advanced machine learning and real-time messaging, CliniqueAI empowers doctors with predictive risk assessments and patients with a streamlined care experience.

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✨ Key Features

• 🛡️ Clinical AI Engine: Real-time diabetes risk prediction utilizing multi-factor clinical biomarkers.
• 📊 Doctor Dashboard: High-fidelity interface for patient registry management and predictive health analytics.
• 📨 Pro-Messaging Hub: Real-time clinical messaging system powered by Socket.io with WhatsApp-grade UX.
• 📜 Hospital-Grade Reports: One-click generation of professional clinical reports (PDF) with institutional branding.
• 🏥 Patient Portal: Dedicated space for patients to view clinical insights and communicate with their care team.
• 🆘 Integrated Help Hub: Premium support center with a searchable knowledge base and documentation center.

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🏗️ Technical Architecture

graph TD
    subgraph Frontend [Next.js 14 Web Portal]
        UI[Tailwind CSS / Radix UI]
        State[React Context / Hooks]
    end

    subgraph Backend [Express.js API Node]
        Server[Express / Node.js]
        Socket[Socket.io Real-time]
        Auth[JWT / RBAC]
    end

    subgraph AI_Service [Python Flask Microservice]
        Model[Scikit-learn / joblib]
        Scale[StandardScaler Engine]
        API[Flask / Pydantic]
    end

    subgraph Database [Persistence Layer]
        MongoDB[(MongoDB Atlas)]
    end

    Frontend --> Backend
    Backend --> Database
    Backend --> AI_Service
    AI_Service --> Model

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🛠️ Tech Stack

Frontend

• Next.js 14 (App Router)
• Tailwind CSS (Styling)
• Framer Motion (Animations)
• Lucide React (Icons)
• Socket.io-client (Messaging)

Backend

• Node.js & Express
• MongoDB & Mongoose (ORM)
• Socket.io (Real-time Communication)
• JWT (Security)

AI Microservice

• Python 3.x
• Flask (API)
• Scikit-learn (Predictive Modeling)
• Waitress (WSGI Server)

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⚙️ Environment Configuration

To run this project, you will need to add the following environment variables to your .env files in each service directory.

AI Service (Ai/.env)

• GROQ_API_KEY: Your Groq Cloud API Key for clinical reasoning.

Backend (backend/.env)

• MONGO_URI: MongoDB Atlas connection string.
• JWT_SECRET: Secure string for token signing.
• CLIENT_URL: Frontend deployment URL (for CORS).
• EMAIL_USER & EMAIL_PASS: SMTP credentials (e.g., Brevo or Gmail) for notifications.
• CLOUDINARY_CLOUD_NAME, CLOUDINARY_API_KEY, CLOUDINARY_API_SECRET: For clinical image storage.
• TWILIO_ACCOUNT_SID, TWILIO_AUTH_TOKEN: For WhatsApp/SMS care updates.
• GOOGLE_CLIENT_ID: For One-Tap authentication.

Frontend (frontend/.env.local)

• NEXT_PUBLIC_API_URL: URL of your Node backend (e.g., http://localhost:5000/api).
• NEXT_PUBLIC_GOOGLE_CLIENT_ID: Same as backend Google ID.
• NEXT_PUBLIC_FIREBASE_API_KEY: (Optional) For push notifications and analytics.

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🚀 Getting Started

1. Clone the Repository

git clone https://github.com/soham04010/CliniqueAI.git
cd CliniqueAI

2. Setup AI Service

cd Ai
python -m venv venv
source venv/bin/activate  # venv\Scripts\activate on Windows
pip install -r requirements.txt
python app.py

3. Setup Backend

cd ../backend
npm install
npm run dev

4. Setup Frontend

cd ../frontend
npm install
npm run dev

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👨‍💻 Authors

• Prem Patel (prempatel-ai) - Lead Implementation & Clinical Architecture.
• Soham Chaudhary (soham04010) - Frontend Architecture & Backend Orchestration.

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📄 License

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

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Built with ❤️ by Prem & Soham for the future of clinical technology.