Skintellect Documentation

1. Project Overview

Skintellect is an AI-driven skincare analysis platform that leverages advanced image processing and machine learning techniques to provide personalized skincare recommendations. It seamlessly integrates a user-friendly interface with robust backend services, making advanced dermatological analysis accessible to everyone.

2. Key Features

• AI-Powered Skin Analysis:
  • Utilizes a custom Convolutional Neural Network (CNN) combined with YOLOv8 for precise detection of skin conditions.
  • Delivers real-time, detailed metrics with confidence scores.
• Personalized Recommendations:
  • Matches skin concerns with a curated database of skincare products.
  • Delivers tailored suggestions using a hybrid recommendation engine.
• Image-Based Assessments:
  • Intuitive interface for uploading or capturing images.
  • Visual annotations highlight areas of concern with descriptive analysis.
• Appointment Booking:
  • Integrated scheduling system for booking consultations with dermatologists.
  • Automated notifications and reminders for appointments.
• User Profile Management:
  • Secure account management and profile editing.
  • Tracks analysis history, recommendations, and appointment records.

3. Technology Stack

Backend & API

• Framework: FastAPI with Uvicorn for asynchronous, high-performance API handling.
• Language: Python (3.9+)
• Authentication: JWT-based secure authentication with bcrypt hashing.

Machine Learning

• Models:
  • Custom CNN built with TensorFlow/Keras using EfficientNet for feature extraction.
  • YOLOv8 for object and lesion detection.
• Training: Trained on over 10,000 labeled skin images with extensive data augmentation.

Database

• Primary Storage: PostgreSQL managed via SQLAlchemy.
• Caching: Redis for session management and fast query caching.

Frontend

• Mobile Application: Developed in Flutter for cross-platform compatibility.
• Web Interface: React integrated with Jinja2 templating when needed.

Infrastructure & Deployment

• Containerization: Docker for reliable and reproducible builds.
• Orchestration: Kubernetes for scalable production deployments.
• Cloud Services: AWS (EC2, RDS, S3) or equivalent providers.
• CI/CD: Automated pipelines using GitHub Actions for testing and deployments.

4. Installation & Usage

4.1. Installation

1. Clone the Repository & Set Up Environment

   git clone https://github.com/yourusername/Skintellect.git
   cd Skintellect
   python -m venv venv
   source venv/bin/activate   # (On Windows: venv\Scripts\activate)
   pip install -r requirements.txt

2. Configure Environment Variables

   Create a .env file with the following:

   • DATABASE_URL (e.g., postgresql://user:pass@localhost:5432/skintellect)
   • SECRET_KEY (secure, random string)
   • MODEL_PATH (custom path for ML model if needed)
   • ENV (set to development for testing)
3. Database Setup

   Run migrations (using Alembic or provided scripts):

   alembic upgrade head

   Optionally, seed the database:

   python scripts/seed_data.py

4.2. Usage

1. Start the Backend

   uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

   Access the interactive API docs at http://localhost:8000/docs.

2. Mobile App Setup
   1. Navigate to the mobile app directory: cd mobile_app
   2. Install dependencies: flutter pub get
   3. Configure the API endpoint (e.g., in api_config.dart).
   4. Run the app: flutter run
3. Basic Workflow
   • Registration & Login: Create an account via the app or API endpoints (/auth/register and /auth/login).
   • Image Analysis: Upload a skin image via POST /analysis to receive analysis results.
   • Recommendations: Access personalized skincare suggestions through /recommendations.
   • Appointments: Book consultations using the /appointments endpoint.

5. API Documentation

The RESTful API is defined using OpenAPI 3.0 specifications. Key endpoints include:

Authentication

• POST /auth/register:

  Creates a new user account with JSON data (name, email, password).

• POST /auth/login:

  Authenticates users and returns a JWT token.

Skin Analysis

• POST /analysis:

  Uploads a skin image (multipart/form-data) and returns analysis results with detected conditions and metrics.

Recommendations

• GET /recommendations:

  Returns personalized skincare product suggestions, optionally filtered by analysis_id.

Appointments

• POST /appointments:

  Books an appointment using JSON data (e.g., date, specialist ID).

• GET /appointments:

  Retrieves a list of user appointments.

User Profile

• GET /users/me:

  Fetches the current user's profile details.

• PUT /users/me:

  Updates user profile information.

6. System Architecture

A high-level overview of the system architecture:

• Client Layer: User interfaces for image upload, analysis results, and appointment scheduling.
• Backend Layer: Orchestrates API calls, ML model invocations, and database interactions.
• ML Layer: Custom CNN and YOLOv8 for skin analysis and condition detection.
• Data Layer: PostgreSQL and Redis for persistent storage and caching.
• Cloud Integration: Secure storage and scalable deployment using cloud services.

7. Database Schema

Main Entities

• Users:

  Stores account details and profile information.

  CREATE TABLE users (
    user_id SERIAL PRIMARY KEY,
    name VARCHAR(100) NOT NULL,
    email VARCHAR(100) NOT NULL UNIQUE,
    password_hash VARCHAR(255) NOT NULL,
    skin_type VARCHAR(20),
    allergies TEXT,
    created_at TIMESTAMP NOT NULL DEFAULT NOW()
  );

• Analyses:

  Records skin analysis details linked to users.

  CREATE TABLE analyses (
    analysis_id SERIAL PRIMARY KEY,
    user_id INT NOT NULL REFERENCES users(user_id) ON DELETE CASCADE,
    image_url TEXT NOT NULL,
    result JSONB NOT NULL,
    analyzed_at TIMESTAMP NOT NULL DEFAULT NOW()
  );

• Products:

  Contains information about skincare products.

  CREATE TABLE products (
    product_id SERIAL PRIMARY KEY,
    name VARCHAR(200) NOT NULL,
    brand VARCHAR(100),
    category VARCHAR(50),
    description TEXT,
    ingredients TEXT,
    image_url TEXT,
    created_at TIMESTAMP NOT NULL DEFAULT NOW()
  );

• Recommendations:

  Maps analyses to recommended products.

  CREATE TABLE recommendations (
    analysis_id INT NOT NULL REFERENCES analyses(analysis_id) ON DELETE CASCADE,
    product_id INT NOT NULL REFERENCES products(product_id),
    PRIMARY KEY (analysis_id, product_id)
  );

• Appointments:

  Manages bookings between users and specialists.

  CREATE TABLE appointments (
    appointment_id SERIAL PRIMARY KEY,
    user_id INT NOT NULL REFERENCES users(user_id) ON DELETE CASCADE,
    specialist_id INT NOT NULL,
    scheduled_at TIMESTAMP NOT NULL,
    status VARCHAR(20) NOT NULL DEFAULT 'Scheduled'
  );

Refer to the full ERD diagram for a complete view of relationships.

8. AI/ML Model Details

Model Architecture

• Core Model: A custom CNN based on EfficientNet for feature extraction and multi-task analysis.
• Detection Component: YOLOv8 for localized detection of skin lesions.
• Outputs: Provides both condition classifications and quantitative metrics.

Training & Performance

• Dataset: Trained on 10,000+ augmented skin images.
• Accuracy: Approximately 90% with real-time inference (~1 second per image).
• Enhancements: Ongoing model improvements and retraining with additional data.

9. Deployment Guide

Infrastructure Setup

• Cloud Hosting:
  • Utilize AWS, GCP, or Azure for production environments.
  • Deploy backend services on EC2 or Kubernetes clusters.
• Database:
  • Use PostgreSQL managed by AWS RDS or equivalent.
  • Implement Redis for caching and session management.
• Storage:
  • Securely store assets in AWS S3 with encryption enabled.

Deployment Steps

1. Build & Push Docker Image:

   docker build -t skintellect-backend:latest .
   docker tag skintellect-backend:latest myregistry.com/skintellect:1.0.0
   docker push myregistry.com/skintellect:1.0.0

2. Provision Infrastructure:

   Utilize Terraform or CloudFormation for server and network provisioning.

3. Deploy Containers:
   • For Kubernetes, configure Deployment and Service manifests.
   • For direct deployments, run containers with Nginx as a reverse proxy.
4. Database Migration:

   Execute migration scripts (e.g., Alembic) to initialize the schema.

5. CI/CD Integration:

   Set up automated pipelines with GitHub Actions to build, test, and deploy updates seamlessly.

10. Security & Monitoring

Security Best Practices

• Authentication & Authorization:
  • Enforce HTTPS/TLS for all data transmissions.
  • Implement JWT tokens and role-based access controls.
• Data Protection:
  • Encrypt sensitive data; hash passwords with bcrypt.
  • Sanitize and validate all user inputs.
• File Handling:
  • Validate image file types and sizes prior to processing.
• Access Control:
  • Restrict users to their own data and resources.
• Auditing:
  • Conduct regular security audits and penetration tests.

Monitoring & Logging

• Logging:
  • Use structured logging for all system events and errors.
  • Aggregate logs with CloudWatch, ELK, or similar solutions.
• Performance Monitoring:
  • Leverage APM tools such as Datadog to monitor latency and error rates.
  • Custom metrics for image analysis throughput and appointment bookings.
• Alerts:
  • Configure alerts for abnormal system behaviors and security events.

11. FAQs

1. What skin conditions does Skintellect detect?

   It analyzes common issues like acne, hyperpigmentation, wrinkles, and general skin health metrics such as oiliness or dryness.

2. How accurate is the analysis?

   The models achieve approximately 90% accuracy on high-quality images.

3. Is my data secure?

   Yes, all communications are secured via HTTPS and sensitive data is encrypted.

4. How can I book an appointment?

   Use the integrated appointment booking feature or the /appointments API endpoint.

12. Competitive Analysis

Skintellect distinguishes itself with a holistic and integrated approach:

• Comprehensive Solution: Combines AI-powered analysis, personalized recommendations, and appointment booking.
• User-Centric Design: Provides an intuitive experience from self-assessment to professional consultation.
• Continuous Innovation: Regular updates and model improvements driven by user feedback and emerging trends.
• Seamless Integration: Designed for both consumer use and third-party integrations.

Unlike platforms that focus solely on image analysis or cosmetic suggestions, Skintellect delivers a complete end-to-end skincare solution.

13. Branding & Styling

Visual Identity

• Logo & Colors: A modern, clean logo paired with a soothing palette exuding professionalism and innovation.
• Typography: Crisp, legible fonts with a clear hierarchy to enhance readability.
• Imagery: Custom diagrams and visuals to illustrate complex systems and enhance clarity.

Conclusion

Skintellect represents a transformative approach to personalized skincare. Leveraging advanced AI and a robust technology stack, it provides actionable insights and seamless integration with professional dermatology services. This documentation offers a detailed roadmap—from installation and API usage to deployment and security—ensuring a clear understanding for developers, investors, and users alike.

For additional details, refer to the full repository documentation or contact our team directly.