Diabetes Care Home

A comprehensive web-based platform for diabetes risk assessment, health data visualization, and AI-powered health assistance.

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Table of Contents

• Overview
• Features
• Technology Stack
• Getting Started
• Project Structure
• API Endpoints
• Contributing
• License

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Overview

Diabetes Care Home is an open-source application that combines machine learning with interactive health tools to support diabetes awareness and management. The platform enables users to:

• Assess their diabetes risk using a trained machine learning model
• Explore and visualize health data patterns through interactive charts
• Get instant answers to health queries via an AI-powered chatbot
• Access evidence-based lifestyle recommendations
• Connect with a community through a discussion forum

Why This Project?

Diabetes affects millions worldwide, and early detection is crucial for effective management. This platform aims to make diabetes risk assessment accessible to everyone while providing educational resources and community support.

Medical Disclaimer: This application is intended for educational and informational purposes only. It does not provide medical advice, diagnosis, or treatment recommendations. Always consult a qualified healthcare professional for medical concerns.

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Features

1. Diabetes Risk Prediction

The core feature uses a machine learning model trained on the Pima Indians Diabetes Dataset. Users input eight health parameters:

Parameter	Description	Unit
Pregnancies	Number of pregnancies	Count
Glucose	Plasma glucose concentration (2 hours after glucose tolerance test)	mg/dL
Blood Pressure	Diastolic blood pressure	mm Hg
Skin Thickness	Triceps skin fold thickness	mm
Insulin	2-Hour serum insulin	mu U/ml
BMI	Body Mass Index	kg/m²
Diabetes Pedigree Function	Genetic predisposition score	Score
Age	Age of the individual	Years

The model processes these inputs through a StandardScaler for normalization and returns a binary prediction (Diabetic / Not Diabetic).

Note on Prediction Inputs

The diabetes prediction model is trained on the Pima Indians Diabetes Dataset. Due to limitations of the dataset, gender and individual height/weight values are not included as input features. BMI is used as a combined indicator of height and weight.

2. Data Exploration Dashboard

Interactive visualization tools for understanding diabetes-related health patterns:

• Correlation Heatmap — Displays relationships between all health variables using Seaborn
• Glucose Distribution — Interactive histogram showing glucose level patterns across the dataset
• BMI Distribution — Visual analysis of Body Mass Index distribution

All charts are rendered using Plotly for interactivity and Matplotlib/Seaborn for statistical visualizations.

3. AI-Powered Chatbot

An intelligent assistant powered by Google Gemini API that can:

• Answer diabetes-related health questions
• Provide general information about symptoms, causes, and management
• Offer guidance on lifestyle modifications
• Respond in a conversational, user-friendly manner

4. Lifestyle Recommendations

Curated, evidence-based guidance covering:

• Dietary recommendations for blood sugar management
• Exercise and physical activity guidelines
• Stress management techniques
• Sleep hygiene practices
• Regular monitoring schedules

5. Community Forum

A simple discussion platform where users can:

• Share their experiences and stories
• Ask questions to the community
• Provide peer support
• View posts in reverse chronological order

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Technology Stack

Backend

Technology	Purpose	Version
Python	Core programming language	3.8+
Flask	Web application framework	2.x
Flask-CORS	Cross-Origin Resource Sharing	Latest
Scikit-learn	Machine learning model	Latest
Pandas	Data manipulation and analysis	Latest
NumPy	Numerical computing	Latest
Gunicorn	Production WSGI server	Latest

Frontend

Technology	Purpose
HTML5	Page structure and semantics
CSS3	Styling and animations
Tailwind CSS	Utility-first CSS framework
JavaScript	Client-side interactivity

Data Visualization

Technology	Purpose
Plotly	Interactive web-based charts
Matplotlib	Static chart generation
Seaborn	Statistical data visualization

AI Integration

Technology	Purpose
Google Gemini API	Conversational AI chatbot
google-generativeai	Python SDK for Gemini

Deployment

Technology	Purpose
Render	Cloud hosting platform
Gunicorn	Production-grade WSGI server
python-dotenv	Environment variable management
Docker	Containerization

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Getting Started

Prerequisites

Before you begin, ensure you have the following installed:

• Python 3.8 or higher

  # Check Python version
  python --version
  

* **pip package manager**
```bash
# Check pip version
pip --version

• Git

# Check Git version
git --version

• Docker Desktop (Optional, for containerized setup)

• Required if you plan to run the application using Docker.

• Google Gemini API Key — Required for the chatbot feature

• Visit Google AI Studio

• Sign in with your Google account

• Click "Create API Key"

• Copy and save the key securely

Installation (Manual Method)

Step 1: Clone the Repository

# Clone via HTTPS
git clone [https://github.com/your-username/diabetes-care-home.git](https://github.com/your-username/diabetes-care-home.git)

# Or clone via SSH
git clone [email protected]:your-username/diabetes-care-home.git

# Navigate to project directory
cd diabetes-care-home

Step 2: Create Virtual Environment

Creating a virtual environment isolates project dependencies from your system Python.

Windows (Command Prompt):

python -m venv venv
venv\Scripts\activate

Windows (PowerShell):

python -m venv venv
.\venv\Scripts\Activate.ps1

macOS / Linux:

python3 -m venv venv
source venv/bin/activate

You should see (venv) prefix in your terminal indicating the virtual environment is active.

Step 3: Install Dependencies

# Upgrade pip to latest version
pip install --upgrade pip

# Install all required packages
pip install -r requirements.txt

This installs the following packages:

• flask, flask-cors
• numpy, pandas
• matplotlib, seaborn, plotly
• scikit-learn
• python-dotenv
• google-generativeai
• gunicorn

Step 4: Configure Environment Variables

Create a .env file in the project root directory:

Windows (Command Prompt):

echo GEMINI_API_KEY=your_api_key_here > .env

Windows (PowerShell):

"GEMINI_API_KEY=your_api_key_here" | Out-File -FilePath .env -Encoding utf8

macOS / Linux:

echo "GEMINI_API_KEY=your_api_key_here" > .env

Or manually create the file with the following content:

GEMINI_API_KEY=your_actual_gemini_api_key

Security Note: Never commit your .env file to version control. It should already be listed in .gitignore.

Step 5: Verify Model Files

Ensure the following pre-trained model files exist in the project root:

• diabetes_model.pkl — Trained classification model
• scaler.pkl — Feature scaler for input normalization
• diabetes.csv — Dataset for visualization features

If missing, you can retrain the model using the provided Jupyter notebook:

# Install Jupyter if not available
pip install jupyter

# Launch Jupyter and run train.ipynb
jupyter notebook train.ipynb

Step 6: Run the Application

Development Mode:

python app.py

Production Mode (using Gunicorn):

gunicorn app:app --bind 0.0.0.0:5000

Step 7: Access the Application

Open your web browser and navigate to:

http://localhost:5000

You should see the Diabetes Care Home landing page.

🐳 Installation (Docker Method)

If you prefer to run the application in a consistent containerized environment, follow these steps instead of the manual installation.

1. Prerequisite: Ensure Docker Desktop is installed and running on your machine.
2. Configure Env: Create the .env file as described in Step 4 above.
3. Run: Open your terminal in the project root and run:

docker-compose up --build

4. Access: Open your browser to http://localhost:5000.

To stop the container, press Ctrl+C in the terminal.

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Project Structure

diabetes-care-home/
│
├── app.py                      # Main Flask application
│   ├── Route definitions       # All URL endpoints
│   ├── ML model loading        # Pickle file loading
│   ├── Gemini integration      # AI chatbot logic
│   └── Forum API               # Post management
│
├── diabetes_model.pkl          # Trained scikit-learn model
├── scaler.pkl                  # StandardScaler for feature normalization
├── diabetes.csv                # Pima Indians Diabetes Dataset
│
├── train.ipynb                 # Jupyter notebook for model training
│
├── requirements.txt            # Python package dependencies
├── Dockerfile                  # Docker build instructions
├── docker-compose.yml          # Container orchestration
├── .env                        # Environment variables (not in repo)
├── .gitignore                  # Git ignore rules
│
├── static/                     # Static assets
│   └── doctor.png              # Image assets
│
└── templates/                  # Jinja2 HTML templates
    ├── home.html               # Landing page (route: /)
    ├── index.html              # Prediction form (route: /index)
    ├── explore.html            # Data visualization (route: /explore)
    ├── chatbot.html            # AI chatbot interface (route: /chatbot)
    ├── life.html               # Lifestyle tips (route: /life)
    └── forum.html              # Community forum (route: /forum)

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API Endpoints

Web Routes

Method	Endpoint	Description
GET	/	Landing page
GET	/index	Diabetes prediction form
POST	/predict	Process prediction form submission
GET	/explore	Data visualization dashboard
GET	/chatbot	AI chatbot interface
GET	/life	Lifestyle recommendations page
GET	/forum	Community forum page

API Routes

Method	Endpoint	Description	Request Body	Response
POST	/generate	Send message to AI chatbot	{"message": "your question"}	{"reply": "AI response"}
GET	/api/posts	Retrieve all forum posts	—	Array of post objects
POST	/api/posts	Create new forum post	{"content": "post text"}	Created post object

Example API Usage

Chat with AI Assistant:

curl -X POST http://localhost:5000/generate \
  -H "Content-Type: application/json" \
  -d '{"message": "What are the symptoms of diabetes?"}'

Create Forum Post:

curl -X POST http://localhost:5000/api/posts \
  -H "Content-Type: application/json" \
  -d '{"content": "Hello, this is my first post!"}'

Get All Forum Posts:

curl http://localhost:5000/api/posts

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Contributing

We welcome contributions from developers of all skill levels.

Follow the Code Of Conduct: CODE_OF_CONDUCT.md

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License

This project is open source and available under the MIT License.

MIT License

Copyright (c) 2024 Anshika Singh

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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Author

Anshika Singh

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Acknowledgments

• Pima Indians Diabetes Dataset — UCI Machine Learning Repository
• Google Gemini — AI chatbot capabilities
• Flask — Web framework
• Tailwind CSS — UI styling

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Making diabetes care smarter and more accessible.