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SkinTellect - AI-Powered Skin Care Analysis

SkinTellect is a web application that uses artificial intelligence to analyze skin conditions and provide personalized skincare recommendations.

Features

  • AI-powered skin condition analysis
  • Personalized skincare product recommendations
  • Appointment booking system
  • User authentication and profile management
  • Email and SMS notifications
  • Doctor's dashboard for appointment management

Tech Stack

  • Python 3.12
  • Flask
  • SQLite
  • Roboflow AI
  • Twilio (SMS)
  • Flask-Mail
  • HTML/CSS/JavaScript

Setup Instructions

  1. Clone the repository:
git clone https://github.com/sudo-Harshk/Skintelite.git
cd Skintelite
  1. Create and activate a virtual environment:
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
  1. Install dependencies:
pip install -r requirements.txt
  1. Create a .env file in the root directory with the following variables:
# Flask Configuration
FLASK_SECRET_KEY=your_secret_key
DATABASE_PATH=app.db

# Twilio Credentials
TWILIO_ACCOUNT_SID=your_twilio_account_sid
TWILIO_AUTH_TOKEN=your_twilio_auth_token
TWILIO_PHONE_NUMBER=your_twilio_phone_number

# Email Configuration
MAIL_SERVER=smtp.gmail.com
MAIL_PORT=587
MAIL_USE_TLS=True
MAIL_USERNAME=your_email@gmail.com
MAIL_PASSWORD=your_email_password
MAIL_DEFAULT_SENDER_NAME=SkinTellect
MAIL_DEFAULT_SENDER_EMAIL=your_email@gmail.com
MAIL_MAX_EMAILS=5
MAIL_ASCII_ATTACHMENTS=False
MAIL_USE_SSL=False

# Roboflow API Keys
ROBOFLOW_API_KEY=your_roboflow_api_key
ROBOFLOW_INFERENCE_API_KEY=your_roboflow_inference_api_key
  1. Run the application:
python app.py

The application will be available at http://localhost:5000

Contributing

  1. Fork the repository
  2. Create your feature branch (git checkout -b feature/AmazingFeature)
  3. Commit your changes (git commit -m 'Add some AmazingFeature')
  4. Push to the branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

Acknowledgments

  • Roboflow for AI model hosting
  • Twilio for SMS services
  • Flask community for the web framework

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  • HTML 46.0%
  • Jupyter Notebook 37.2%
  • Python 14.3%
  • CSS 2.5%