DermaGenie– AI-Powered Skincare Intelligence Platform

You can add the following installation instructions for DermaGenie:

Installation Steps

1. Clone the Repository

git clone https://github.com/<your-username>/DermaGenie.git
cd DermaGenie

2. Install Frontend Dependencies

cd frontend
npm install

3. Install Backend Dependencies

cd ../backend
pip install -r requirements.txt

4. Configure Environment Variables

Create a .env file inside the backend folder:

DATABASE_URL=your_supabase_database_url
GEMINI_API_KEY=your_gemini_api_key
CORS_ORIGINS=https://derma-genie.vercel.app

Create a .env.local file inside the frontend folder:

NEXT_PUBLIC_API_URL=http://localhost:8000

5. Start the Backend Server

cd backend
uvicorn app.main:app --reload

Backend will run at:

http://localhost:8000

6. Start the Frontend

Open a new terminal:

cd frontend
npm run dev

Frontend will run at:

http://localhost:3000

7. Open the Application

Visit:

http://localhost:3000

8. How to Use

1. Create your profile.
2. Select your skin type and concerns.
3. Upload a skincare product ingredient image.
4. The system extracts ingredients using Tesseract OCR.
5. The backend analyzes the ingredients using the weighted scoring engine.
6. View the suitability score, ingredient insights, and personalized skincare recommendations. 

Prerequisites

• Python 3.11+
• Node.js 18+
• npm
• Git
• Tesseract OCR (for local OCR support)
• Supabase Account
• Google Gemini API Key

Usage Guide

1. Launch the Application

Open the application in your browser and create a new user profile by entering your name, age, gender, and location.

2. Complete Skin Analysis

Select your skin type, skin concerns (such as acne, pigmentation, dryness, or sensitivity), issue duration, and severity level. This information is used to personalize the analysis.

3. Upload Product Ingredient Image

Upload a clear image of the skincare product's ingredient list. The system uses Tesseract OCR to extract ingredient names from the uploaded image automatically.

4. Analyze the Product

Click Analyze Product. The backend processes the extracted ingredients using the weighted scoring engine, compares them with your skin profile, and evaluates the product's suitability.

5. View Analysis Results

The application displays:

• Overall product suitability score (0–100)
• Score category (Excellent, Good, Moderate, Poor)
• Extracted ingredient list
• Beneficial ingredients and their benefits
• Harmful ingredients with associated risks
• Personalized AI-generated skincare recommendations
• Recommended skincare routine (Morning & Night)
• Natural precautions and skincare tips

6. Repeat Analysis

Users can upload different skincare products and compare their suitability for their skin profile.

Expected Output

• Accurate ingredient extraction using OCR
• Personalized skincare suitability score
• Ingredient-wise analysis
• Recommended skincare routine
• AI-generated skincare insights and product recommendations

Hardware Requirements

• Processor: Intel Core i3 (8th Gen) / AMD Ryzen 3 or higher
• RAM: Minimum 4 GB (8 GB Recommended)
• Storage: At least 2 GB free disk space
• Internet: Stable internet connection for API access and cloud services

Software Requirements

• Operating System: Windows 10/11, macOS, or Linux
• Python: Version 3.11 or later
• Node.js: Version 18 or later
• npm: Version 9 or later
• Git: Latest version
• Visual Studio Code: Latest version (Recommended)
• Web Browser: Google Chrome, Microsoft Edge, or Mozilla Firefox

Database

• Supabase PostgreSQL (Cloud Database)

Runtime & Frameworks

• Frontend: Next.js, React, TypeScript, Tailwind CSS
• Backend: FastAPI, Python
• API Architecture: REST APIs
• OCR Engine: Tesseract OCR

Required Services

• Supabase account (Database)
• Google Gemini API Key (AI-powered recommendations)
• Vercel (Frontend Deployment)
• Render (Backend Deployment)

Environment Variables

DATABASE_URL=<your_supabase_database_url>
GEMINI_API_KEY=<your_gemini_api_key>
NEXT_PUBLIC_API_URL=<backend_api_url>
CORS_ORIGINS=<frontend_url>