KidsCare Pro

KidsCare Pro is an AI-powered child health management platform with a 95% accuracy Machine Learning model. It helps parents and doctors make informed health decisions, track milestones, and manage child health effectively.

Key Features

• ML-Driven Health Predictions: 95% accurate health predictions based on child data.
• User Authentication: Secure login for parents and doctors using AWS Cognito.
• Health Predictor: Used Fine-tuned Llamma model on medical data for medical advise.
• Appointments: Book healthcare appointments easily.
• Dashboard: Visualized health insights and trends.
• Child Profile Management: Store and manage child health records with AWS DynamoDB.
• Milestones Tracking: Track developmental milestones for timely intervention.
• Doctor's Portal: Tools for healthcare providers to manage child profiles.

Tech Stack

• Frontend: Streamlit
• Backend: Python
• Machine Learning: 95% accuracy model
• Data Visualization: Matplotlib, Seaborn, Plotly
• AWS: Cognito (User Authentication), DynamoDB (Data Storage), Boto3 (AWS SDK)
• Image Processing: Pillow

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Installation

1. Clone the Repository

git clone[ https://github.com/Sa1f27/child_growth.git
cd child_growth

2. Install Dependencies

pip install -r requirements.txt

3. Set Up AWS Cognito and DynamoDB (Optional)

To use AWS Cognito for user authentication and DynamoDB for storing data, follow these steps:

Step 1: Set Up AWS Cognito

1. Create a User Pool:

   • Go to the AWS Cognito console and create a User Pool.
   • Note down the User Pool ID and App Client ID.

2. Configure the App Client:

   • In the User Pool settings, under "App clients," create a new App Client.
   • Disable client secret for easier integration with Streamlit.
   • Note the App Client ID.

Step 2: Set Up DynamoDB

1. Create a DynamoDB Table:

   • Go to the DynamoDB console and create a table for storing child health data (e.g., ChildHealthRecords, Appointments).
   • Configure the primary key (e.g., child_id, ID as the partition key).

2. Set Permissions:

   • Ensure your AWS user has permissions for accessing Cognito and DynamoDB.

Step 3: Set AWS Credentials

Set your AWS access keys and configure your AWS region. You can do this by adding environment variables:

export AWS_ACCESS_KEY=<YourAccessKey>
export AWS_SECRET_KEY=<YourSecretKey>
export AWS_REGION=<YourRegion>
export AWS_USER_POOL_ID=<YourUserPoolID> 
export AWS_APP_CLIENT_ID=<YourAppClientID> 

4. Run the Application

streamlit run app.py

File Structure

KidsCarePro/
├── app/
│   ├── app.py              # Main Streamlit app
│   ├── appointments.py     # Appointment booking
│   ├── dashboard.py        # Dashboard functionality
│   ├── doctor_portal.py    # Doctor's portal functionality
│   ├── home.py             # Home page logic
│   ├── ai_model.py         # ML model for health predictions
│   ├── growth.py           # Child profile management
│   ├── milestone.py        # Milestone tracking
│   ├── show_analytics.py   # Analytics dashboard
│   └── symptoms.py         # Health predictor
├── model/
│   ├── child_dataset.csv
│   ├── eda_report.html
│   ├── model.ipynb 
│   └── symptom_predictor.pkl
├── README.md
├── requirements.txt  # Documentation

Machine Learning Model

• Accuracy: 95% on test data
• Features: Age, weight, height, BMI, symptoms, health history
• Personalized Insights: Health recommendations based on input data.

Customization

• ML Model: Update ai_model.py for improved accuracy or additional features.
• UI: Modify the CSS in app.py for custom styling.
• AWS Integration: Enable DynamoDB and Cognito in app.py.

Dependencies

• Streamlit: Interactive web interface
• Scikit-learn: ML model implementation
• Matplotlib/Seaborn/Plotly: Data visualization
• Pillow: Image handling
• Boto3: AWS SDK for Python
• Numpy: Numerical computations