🌐 Overview

MediSinCare is a Flask-based healthcare web application that predicts diseases based on user symptoms using a Logistic Regression model.
It also provides personalized prevention recommendations, integrates Firebase Authentication for secure login/logout, and uses Firestore for managing user feedback.

✨ Features

• 🔐 User Authentication – Secure Login & Logout via Firebase
• 🧬 Disease Prediction – Powered by a trained Logistic Regression model
• 💡 Personalized Recommendations – Prevention tips for diagnosed diseases
• 📝 Feedback Collection – User feedback stored in Firestore
• 📑 Multi-Page Navigation – Includes About Us, Diet Plans, and Main Dashboard

Technologies Used

• Flask (Backend Framework)
• Firebase Authentication & Firestore (User management & database)
• Joblib (Machine Learning Model Serialization)
• Scikit-learn (Machine Learning - Logistic Regression)
• Jinja2 (Templating Engine for Flask)

Installation

Prerequisites

Make sure you have the following installed:

• Python (>=3.7)
• pip (Python package manager)
• Git (optional)

Steps

1. Clone the repository

   git clone https://github.com/your-username/Disease-Prediction.git
   cd Disease-Prediction

2. Create a virtual environment (Optional but recommended)

   python -m venv venv
   source venv/bin/activate  # For Linux/macOS
   venv\Scripts\activate    # For Windows

3. Install dependencies

   pip install -r requirements.txt

4. Set up environment variables

   • Create a .env file in the project root and add the following:

     FLASK_SECRET_KEY=your_flask_secret_key
     FIREBASE_CREDENTIALS=your_firebase_credentials_json
     FIREBASE_API_KEY=your_firebase_api_key
     FIREBASE_AUTH_DOMAIN=your_firebase_auth_domain
     FIREBASE_PROJECT_ID=your_firebase_project_id
     FIREBASE_STORAGE_BUCKET=your_firebase_storage_bucket
     FIREBASE_MESSAGING_SENDER_ID=your_firebase_messaging_sender_id
     FIREBASE_APP_ID=your_firebase_app_id

5. Run the application

   python app.py

   The application will be available at https://medisincare.onrender.com/.

API Endpoints

1. Firebase Config

Endpoint: /firebase-config
Method: GET Response:

{
  "apiKey": "your_firebase_api_key",
  "authDomain": "your_firebase_auth_domain",
  "projectId": "your_firebase_project_id",
  "storageBucket": "your_firebase_storage_bucket",
  "messagingSenderId": "your_firebase_messaging_sender_id",
  "appId": "your_firebase_app_id"
}

2. Predict Disease

Endpoint: /form
Method: POST Payload:

{
  "symptom1": 1,
  "symptom2": 0,
  "symptom3": 1
}

Response:

{
  "disease": "Flu",
  "disease_recommendations": ["Drink warm fluids", "Rest well"]
}

3. Feedback Submission

Endpoint: /feedback
Method: POST Payload:

{
  "name": "John Doe",
  "email": "john@example.com",
  "rating": "5",
  "recommend": "Yes",
  "comments": "Great experience!"
}

Folder Structure

Disease-Prediction/
│── templates/
│   ├── login1.html
│   ├── ques_form.html
│   ├── hospital_main.html
│   ├── abt2.html
│   ├── diet.html
│   ├── feedback_form.html
│   ├── prediction.html
│── static/
│   │── css/
│   ├── images/
│   ├── js/
│── app.py  # Main Flask application
│── if_else_conditions.py  # Disease recommendation logic
│── symptoms.txt  # List of symptoms
│── logistic_model.joblib  # Trained ML model
│── requirements.txt  # Dependencies
│── .env  # Environment variables (not included in repo)
│── README.md  # Project documentation

Contact

For questions or suggestions, feel free to reach out at mondal.paushali384@gmail.com.

License

This project is licensed under the MIT License.