Clinexa is a comprehensive virtual healthcare ecosystem designed to bridge the gap between patients, doctors, and pharmacies. By integrating Machine Learning for disease prediction, secure tele-consultations, offline clinic bookings, and an e-pharmacy with strict prescription verification, Clinexa provides an end-to-end medical software solution.
- Machine Learning Integration: Trained and deployed Scikit-Learn ML models (
disease_model.pkl&symptom_disease_model.pkl) to predict diseases based on user-inputted symptoms. - Diagnostic Dashboard: Delivers disease predictions with confidence scores and disease-prevention advisories.
- Multi-Tenant Booking: Allows users to discover doctors across multiple specializations (Cardiology, Dermatology, Neurology, etc.).
- Audio/Video Call Support: Structured secure
CallBookingmodels handling call types, payment processing records, and consultation confirmations.
- Clinic Aggregation: Manages multiple physical clinics and associates doctors with specific clinic locations.
- Automated Check-in Optimization: Engineered a dynamic unique passcode generation engine (
generate_passcode()) during appointment creation. This 6-digit code minimizes check-in bottlenecks at physical clinics, allowing for fast, verified patient entry.
- A-Z Medicine Database: Integrated a massive dataset of Indian medicines (
GlobalMedicine), mapped to individual pharmacies for stock and price management. - Multi-Stage Verification Pipeline: Built a strict prescription upload flow requiring users to upload valid prescription files before ordering medications.
- Cart & Order Management: Robust schema handling dynamic cart calculations, processing times, and shipping logistics.
- Intent Detection & Routing: Developed a smart chatbot utilizing NLP intent detection to route user queries (e.g., detecting emergency keywords vs. general health tips vs. app navigation).
- Local Knowledge Base: Queries a local database to provide instant health tips, disease information, and symptom guidance without relying on external APIs.
- Backend: Python, Django, Django REST Framework
- Machine Learning: Scikit-Learn, Pandas, NumPy (Pickle serialization for models)
- Database: SQLite (Local development), managed via Django ORM
- Frontend: Tailwind CSS (compiled via CLI), Alpine.js (for asynchronous client-side state handling and dark mode), HTML/CSS
The platform is built on a highly relational database architecture encompassing 6 major Django applications:
users: Extended user profiles, profile pictures, and authentication.home: Dashboard aggregations calculating metrics like active appointments, orders, and health scores.clinic: Relational mapping of Clinics to Doctors and handling physical DoctorAppointments.doctors: Specialized Doctor profiles, CallBooking logic for tele-medicine and remote consultations.store: Complex schema mapping GlobalMedicine to specific Pharmacy instances via Medicine, handling PrescriptionOrder pipelines.predictor: PredictionHistory model storing serialized symptom data and Machine Learning diagnostic results.
Follow these steps to set up and run the Clinexa project on your local machine.
- Python 3.8+
- Node.js & npm (For Tailwind CSS compilation)
- Git
-
Clone the repository:
git clone https://github.com/Shinu-Cherian/Clinexa.git cd Clinexa -
Create and activate a virtual environment:
python -m venv venv # On Windows: venv\Scripts\activate # On macOS/Linux: source venv/bin/activate
-
Install Python dependencies:
pip install django pandas numpy scikit-learn # (Ensure you install any other dependencies required by the project) -
Install Node.js dependencies for Tailwind CSS:
npm install
-
Apply Database Migrations:
python manage.py makemigrations python manage.py migrate
-
Compile Tailwind CSS (Open a separate terminal window):
npm run build:css
-
Run the Django Development Server:
python manage.py runserver
-
Access the application: Open your browser and navigate to
http://127.0.0.1:8000/.