Coviplus

Coviplus is a web application designed to help people who are suffering diseases from COVID-19. It's basically a health management app with a health chatbot and an ML based disease prediction system. The project consists of a frontend built with React, Vite, and Tailwind CSS, and a backend built with Flask, along with an integrated machine learning model.

Project Structure

Coviplus/
│
├── coviplus_frontend/
│   └── coviplus_react/   # React + Vite + Tailwind CSS code
│
└── coviplus_backend/
    ├── server.py         # Flask server file
    ├── requirements.txt  # Dependencies for Flask and ML model
    └── static            # Uploaded files

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Frontend

The frontend of Coviplus is developed using React with Vite as the build tool and Tailwind CSS for styling.

Prerequisites

• Node.js (v16+)
• Vite (comes with React template)
• NPM or Yarn (NPM comes with Node.js installation)

Setup

1. Install Dependencies

   Navigate to the coviplus_react folder inside coviplus_frontend.

   cd coviplus_frontend/coviplus_react
   npm install

2. Run the Development Server

   To start the frontend development server, use the following command:

   npm run dev

   The app should now be running at http://localhost:3000.

Build for Production

To build the frontend for production, run:

npm run build

This will create an optimized production build of your React app in the dist folder.

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Backend

The backend of Coviplus is powered by Flask, which serves as the API for the frontend and integrates a machine learning model for predictions (or any other ML tasks).

Prerequisites

• Python 3.8+
• Flask (listed in requirements.txt)

Setup

1. Create a Virtual Environment (optional but recommended)

   python3 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install Dependencies

   Navigate to the coviplus_backend folder and install the required packages using pip:

   cd coviplus_backend
   pip install -r requirements.txt

3. Run the Backend Server

   To start the Flask server, run:

   python server.py

   The backend will now be running at http://localhost:5000.

Running the ML Model

The ML model is integrated with the Flask API, and the necessary code is in server.py. Depending on your model's structure, make sure the model file is located in the correct path and that it loads properly when the Flask server is running.

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Frontend-Backend Integration

Ensure both the frontend and backend are running simultaneously for the application to function properly. If your backend API is being called from the frontend, update the frontend configuration to point to the correct backend URL (e.g., http://localhost:5000 for local development).

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