Wellify

Wellify is a comprehensive healthcare management platform designed to assist patients and doctors in managing health-related tasks efficiently. It includes features like disease prediction, medical report analysis, appointment scheduling, and a chatbot assistant for health-related queries.

Features

1. Disease Prediction

• Users can select symptoms to predict potential diseases using a machine learning model.
• Provides detailed information about the predicted disease, including precautions, medications, diet, and workout recommendations.

2. Medical Report Analyzer

• Doctors can upload medical reports in PDF format.
• Extracts key information like patient details, diagnosis, and generates a summary.

3. Appointment Management

• Patients can book appointments with doctors.
• Doctors can view and manage upcoming appointments.

4. Wellness Tips

• Provides daily wellness tips categorized into nutrition, exercise, mental health, and sleep.

5. Chatbot Assistant

• A chatbot to assist users with health-related queries.
• Supports real-time conversation with a user-friendly interface.

6. User Profiles

• Separate profiles for patients and doctors.
• Doctors can manage their specializations, qualifications, and experience.

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Tech Stack

Frontend

• React: For building the user interface.
• React Router: For navigation and routing.
• Tailwind CSS: For styling.
• DaisyUI: For pre-designed UI components.
• Axios: For API requests.

Backend

• Flask: For building the backend API.
• MongoDB: For storing user and appointment data.
• PyMuPDF: For extracting text from PDF files.
• Scikit-learn: For the machine learning model.

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Project Structure

Frontend

Here is a README.md file for your project based on the provided context:

# Wellify

Wellify is a comprehensive healthcare management platform designed to assist patients and doctors in managing health-related tasks efficiently. It includes features like disease prediction, medical report analysis, appointment scheduling, and a chatbot assistant for health-related queries.

## Features

### 1. Disease Prediction
   - Users can select symptoms to predict potential diseases using a machine learning model.
   - Provides detailed information about the predicted disease, including precautions, medications, diet, and workout recommendations.

### 2. Medical Report Analyzer
   - Doctors can upload medical reports in PDF format.
   - Extracts key information like patient details, diagnosis, and generates a summary.

### 3. Appointment Management
   - Patients can book appointments with doctors.
   - Doctors can view and manage upcoming appointments.

### 4. Wellness Tips
   - Provides daily wellness tips categorized into nutrition, exercise, mental health, and sleep.

### 5. Chatbot Assistant
   - A chatbot to assist users with health-related queries.
   - Supports real-time conversation with a user-friendly interface.

### 6. User Profiles
   - Separate profiles for patients and doctors.
   - Doctors can manage their specializations, qualifications, and experience.

### 7. Admin Features
   - Admins can view all users and manage the platform.

---

## Tech Stack

### Frontend
- React: For building the user interface.
- React Router: For navigation and routing.
- Tailwind CSS: For styling.
- DaisyUI: For pre-designed UI components.
- Axios: For API requests.

### Backend
- Flask: For building the backend API.
- MongoDB: For storing user and appointment data.
- PyMuPDF: For extracting text from PDF files.
- Scikit-learn: For the machine learning model.

---

### Project Structure
Wellify/
├── frontend/
│   ├── public/                     # Static assets
│   ├── src/
│   │   ├── components/            # Reusable React components
│   │   ├── pages/                 # Page-level components
│   │   ├── store/                 # Zustand state management
│   │   ├── constants/             # App-wide constants
│   │   ├── App.jsx                # Main app component
│   │   ├── main.jsx               # React entry point
│   │   └── index.css              # Global styles
│   ├── package.json               # Frontend dependencies
│   └── vite.config.js             # Vite configuration

├── backend/
│   ├── src/
│   │   ├── models/                # Mongoose models
│   │   ├── controllers/           # Express API controllers
│   │   ├── routes/                # API route handlers
│   │   ├── lib/                   # Utility/helper functions
│   │   └── server.js              # Express server entry point
│   ├── datasets/                  # CSVs for symptoms, medications, etc.
│   ├── svc_model.pkl              # Trained ML model for disease prediction
│   ├── app.py                     # Flask app for disease prediction
│   ├── Report.py                  # Flask app for report analysis
│   ├── package.json               # Backend dependencies (Node.js)
│   └── .env                       # Environment variables

└── README.md                      # Project documentation (optional but recommended)

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Installation

Prerequisites

• Node.js
• Python 3.x
• MongoDB

Steps

1. Clone the Repository

   git clone https://github.com/your-repo/wellify.git
   cd wellify

2. Setup Backend

   cd backend
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install -r requirements.txt
   python app.py  # Start the Flask server

3. Setup Frontend

   cd frontend
   npm install
   npm run dev  # Start the development server

4. Run MongoDB Ensure MongoDB is running locally or update the connection string in the backend .env file.

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Usage

1. Disease Prediction

   • Navigate to the "Predict Disease" page.
   • Select symptoms and submit to get predictions.

2. Medical Report Analyzer

   • Doctors can upload PDF reports on the "Report Analyzer" page.

3. Appointments

   • Patients can book appointments, and doctors can manage them.

4. Chatbot

   • Click the chatbot icon to interact with the Wellify Assistant.

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API Endpoints

Disease Prediction API

• GET /api/symptoms: Get a list of symptoms.
• POST /api/predict: Predict disease based on symptoms.

Report Analyzer API

• POST /upload: Upload a PDF report for analysis.

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Contributing

1. Fork the repository.
2. Create a new branch.
3. Make your changes and commit them.
4. Push to your fork and submit a pull request.

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License

This project is licensed under the MIT License.

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Acknowledgments

• React
• Flask
• MongoDB
• Scikit-learn
• PyMuPDF

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