GitHub Integration

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How GitShare is Better

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Complex

Without GitShare

  • GitHub account required

    Recipients need a GitHub account to view your repositories

  • Add as collaborator

    Need to add recipients as contributors to grant access

  • Limited sharing options

    Difficult to share with non-GitHub users like employers

  • No usage analytics

    Can't track repository views or engagement metrics

  • Permanent access

    Collaborators retain access until manually removed

Simple

With GitShare

  • No GitHub account required

    Anyone with the link can view your repositories

  • One-click sharing

    Generate shareable links in seconds

  • Universal access

    Share with anyone, GitHub user or not

  • Detailed analytics

    Track views and engagement for each shared repository

  • Time-limited access

    Set expiration dates for your shared links

Easy Setup

How to Share Your Code in 3 Steps

Follow this simple guide to share your private repositories with anyone

Connect Your GitHub

Screenshot showing Connect Your GitHub process
  • Click 'Sign in with GitHub'
  • Authorize GitShare access
  • Your private repos are now available for sharing

Share Your Repository

Screenshot showing Share Your Repository process
  • Select the repository to share
  • Set expiration date (optional)
  • Click 'Add Repository' and copy the generated link

View Analytics

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  • Access your repository dashboard
  • View detailed analytics
  • Set up email notifications for views
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Powerful Features For Developers

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Repository Sharing

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Key Capabilities

  • One-click shareable link generation
  • No GitHub account required for recipients
  • Customizable access permissions
  • Set expiration dates for shared links

Universal Access

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Time-Limited Access

Control exactly how long your repositories can be accessed by others

Privacy Protected

Your repositories remain private on GitHub while being selectively shared

Team Collaboration

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AI-Powered README Generator

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README.md Preview

SkinScanAI

Overview

This project aims to provide a solution for skin condition analysis using AI. While the initial description is absent, this README will guide you through understanding the project's current structure, technologies involved, and setup process.

Key Features & Benefits

  • AI-Powered Analysis: Utilizes a pre-trained model for skin condition assessment.
  • Web Interface: Provides a user-friendly frontend for interacting with the system.
  • Containerized Deployment: Leverages Docker for easy and consistent deployment.
  • Modular Architecture: Separates the frontend, backend, and AI model for maintainability.

Prerequisites & Dependencies

Before you begin, ensure you have the following installed:

Installation & Setup Instructions

Follow these steps to get the project up and running:

  1. Clone the Repository:

    git clone <repository_url>
    cd SkinScanAI
    
  2. Build and Run with Docker Compose:

    docker-compose up --build
    

    This command will build the Docker images for the frontend, backend, and start all services. It might take a while on the first run as it downloads and installs the necessary dependencies.

  3. Access the Application:

    Once the Docker containers are running, you can access the application in your web browser at http://localhost:3000. (The frontend port is configured to 3000, but check docker-compose.yml to confirm).

Detailed Setup (Optional - Individual Components)

If you prefer setting up each component individually, follow these steps:

A. AI Model (Optional - Mostly for development)

  1. Navigate to the AI-model directory:

    cd AI-model
    
  2. Create a virtual environment (recommended):

    python -m venv venv
    source venv/bin/activate  # On Linux/macOS
    venv\Scripts\activate  # On Windows
    
  3. Install dependencies:

    pip install -r requirements.txt
    
  4. Run the script (for testing/development):

    python script.py
    

B. Backend (Optional - Mostly for development/customization)

  1. Navigate to the backend directory:

    cd ../backend
    
  2. Create a virtual environment (recommended):

    python -m venv venv
    source venv/bin/activate  # On Linux/macOS
    venv\Scripts\activate  # On Windows
    
  3. Install dependencies:

    pip install -r requirements.txt
    
  4. Set up environment variables (if needed). You may need to configure the path to the model.

  5. Run the backend application:

    python app.py
    

    This will typically start the Flask server on port 5000.

C. Frontend (Optional - Mostly for development/customization)

  1. Navigate to the frontend directory:

    cd ../frontend
    
  2. Install dependencies:

    npm install
    
  3. Start the development server:

    npm run dev
    

    This will usually start the Next.js development server on port 3000.

Usage Examples & API Documentation

Frontend Usage

The frontend provides a user interface for uploading skin images and receiving analysis results. Detailed instructions for usage will be included in the frontend's own README.md file within the frontend directory.

Backend API

The backend provides the following API endpoint:

  • /predict (POST): Accepts an image file and returns the prediction from the AI model.

    • Request:
      • Content-Type: multipart/form-data
      • File field: image
    • Response:
      {
          "prediction": "Diagnosis Result",
          "confidence": 0.87
      }
      

    The confidence value represents the model's certainty in the prediction.

    • Example (using curl):

      curl -X POST -F "image=@/path/to/image.jpg" http://localhost:5000/predict
      

Configuration Options

Backend

  • Model Path: The path to the model_acc_0.87.pth file can be configured as an environment variable. This allows you to use a different model or specify an absolute path. Example:
    MODEL_PATH=/path/to/your/model.pth
    
    You will need to modify the app.py file to read this environment variable.
  • CORS Configuration: The CORS settings in app.py can be adjusted to restrict access to specific origins or allow all origins.

Frontend

  • The frontend configuration can be found in next.config.ts. You might want to change the backend URL if it's hosted on a different server.

Contributing Guidelines

We welcome contributions to this project! Please follow these guidelines:

  1. Fork the repository.
  2. Create a new branch for your feature or bug fix.
  3. Implement your changes.
  4. Write tests to ensure your changes are working correctly.
  5. Submit a pull request with a clear description of your changes.

License Information

License not specified.

Acknowledgments

  • The AI model is based on publicly available datasets and pre-trained architectures.
  • The frontend utilizes the Next.js framework.
  • The backend uses Flask and PyTorch.

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3 Credits

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$2.00 per credit

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25 Credits

Best value for teams and power users

$25total

$1.00 per credit • Save 50%

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