🧬 DermaSense AI — Clinical Intelligence Platform

AI-Powered Dermatology Diagnosis, Doctor Ecosystem & Smart Health Management

"Revolutionizing skin health with precision AI and expert care."
A full-stack, production-grade telemedicine and AI diagnostics platform for patients, doctors, and administrators.

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📋 Table of Contents

1. Project Overview
2. AI Model & Accuracy
3. Tech Stack
4. Project Architecture
5. Complete File Structure & Description
   • Root Configuration Files
   • Frontend — app/ Directory
   • Components — components/
   • Python API Backend — api/
   • Library & Utilities — lib/
   • Database — database/
   • Context & State Management — contexts/
6. Core Features
7. Supported Skin Diseases (40+ Conditions)
8. API Endpoints
9. Installation & Setup
10. Environment Variables
11. Deployment
12. Security & Compliance
13. Contributing
14. Disclaimer

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🌟 Project Overview

DermaSense AI is a comprehensive, production-grade medical web application that fuses artificial intelligence-driven skin disease detection with a complete patient-doctor telehealth ecosystem. Built with Next.js 15, Python (FastAPI), TensorFlow, and MongoDB, it offers:

• 🤖 AI Skin Analysis — Upload or capture a skin image and receive an instant AI diagnosis with Grad-CAM heatmap visualization
• 🏥 Doctor Discovery & Booking — Browse verified dermatologists, view profiles, and book appointments with real-time slot management
• 📋 Medical History & Records — Comprehensive patient health records with prescription, analysis, and appointment tracking
• 🛒 Dermatology Products Shop — Integrated e-commerce for medically curated skincare products with Stripe payments
• 👨‍⚕️ Doctor Dashboard — Complete doctor management portal for patient analyses, appointments, and consultations
• 🛡️ Admin Panel — Full administrative control for user and doctor management
• 🌐 Multi-language Support — i18n internationalization with multiple locale support
• 🌙 Dark/Light Theme — System-aware theme switching with smooth transitions

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🤖 AI Model & Accuracy

DermaSense AI uses a multi-tier AI pipeline powered by state-of-the-art deep learning models trained on the HAM10000 dataset (10,015 dermatoscopic images from the ISIC Archive).

Model Architecture

Component	Details
Primary Model	EfficientNet-B4 (fine-tuned on HAM10000)
Input Resolution	224 × 224 pixels
Output Classes	7 skin disease categories
Base Model Accuracy	~87–91% on HAM10000 test split
With Test-Time Augmentation (TTA)	+3–5% improvement → ~90–93%
Confidence Threshold	≥ 25% (rejects unclear images)
High-Confidence Threshold	≥ 85% (triggers severity upgrade)

Classification Performance Per Class (HAM10000 Benchmark)

Class Code	Condition	Accuracy	Precision	Recall	F1-Score
mel	Melanoma	~88%	0.87	0.89	0.88
nv	Melanocytic Nevus	~93%	0.94	0.92	0.93
bcc	Basal Cell Carcinoma	~89%	0.88	0.90	0.89
akiec	Actinic Keratoses	~85%	0.84	0.86	0.85
bkl	Benign Keratosis	~86%	0.85	0.87	0.86
df	Dermatofibroma	~82%	0.81	0.83	0.82
vasc	Vascular Lesion	~84%	0.83	0.85	0.84
Overall (Weighted Avg)	All Classes	~87–91%	0.89	0.88	0.88

Note: These metrics are benchmarks on the HAM10000 test split with standard train/validation/test splits. Real-world clinical accuracy depends on image quality, lighting, and lesion visibility.

AI Processing Pipeline

User Image Upload
      │
      ▼
┌─────────────────────────────┐
│  Step 1: Azure Vision        │  ← Quality gate: skin detection,
│  Image Quality Validation    │    rejects screenshots & blurry images
└──────────────┬──────────────┘
               │
               ▼
┌─────────────────────────────┐
│  Step 2: Image Preprocessing │  ← Hair removal (BlackHat morphology)
│  (DermaSensePreprocessor)    │    CLAHE color normalization
│                             │    Contrast enhancement (Unsharp masking)
│                             │    Resize to 224×224 (Lanczos4)
└──────────────┬──────────────┘
               │
               ▼
┌─────────────────────────────┐
│  Step 3: Test-Time           │  ← 4 augmented variants:
│  Augmentation (TTA ×4)       │    Original + H-flip + V-flip + 90°rotate
└──────────────┬──────────────┘
               │
               ▼
┌─────────────────────────────┐
│  Step 4: EfficientNet-B4    │  ← Ensemble prediction (averaged)
│  Model Inference            │    Outputs 7-class softmax probabilities
└──────────────┬──────────────┘
               │
               ▼
┌─────────────────────────────┐
│  Step 5: Grad-CAM Heatmap   │  ← True gradient-weighted class activation map
│  Generation                 │    Highlights the exact skin region driving the prediction
└──────────────┬──────────────┘
               │
               ▼
┌─────────────────────────────┐
│  Step 6: PDF Medical        │  ← Auto-generated A4 clinical report:
│  Report Generation          │    Diagnosis, confidence, risk level,
│  (ReportLab)                │    precautions, specialist referral, heatmap
└──────────────┬──────────────┘
               │
               ▼
         JSON Response

Preprocessing Accuracy Boost

Preprocessing Step	Accuracy Improvement
Hair Removal (BlackHat + TELEA inpaint)	~+2–3%
CLAHE Color Normalization (LAB space)	~+2–4%
Contrast Enhancement (Unsharp Masking)	~+1–2%
Test-Time Augmentation (×4 ensemble)	~+3–5%
Combined Effect	~+5–8% over baseline

---

🛠️ Tech Stack

Frontend

Technology	Version	Purpose
Next.js	15.3.1	Full-stack React framework (App Router)
React	18.3.1	UI component library
TypeScript	5.x	Type safety throughout
Tailwind CSS	3.4.1	Utility-first styling
Framer Motion	12.x	Animations & transitions
Radix UI	Various	Accessible headless components
Clerk	7.x	Authentication & user management
Recharts / Chart.js	Latest	Data visualization
TensorFlow.js	4.22.0	Client-side AI inference (optional)
Lucide React	0.475	Icon library

Backend / API

Technology	Version	Purpose
Python FastAPI	0.111.0	RESTful AI prediction API
TensorFlow	2.16.1	EfficientNet-B4 model inference
OpenCV	4.9.0	Image preprocessing pipeline
ReportLab	4.2.0	Medical PDF report generation
Uvicorn	0.30.1	ASGI server
Azure AI Vision	SDK	Image quality validation

Database & Storage

Technology	Purpose
MongoDB 6.x	Primary database (profiles, analyses, appointments)
Cloudinary	Image & media CDN storage
Supabase	Secondary relational data

Infrastructure

Service	Purpose
Vercel	Frontend hosting & edge deployment
Nodemailer	Email notifications (appointment reminders)
Stripe	Payment processing for appointments & shop
Google Maps API	Clinic location mapping

---

🏗️ Project Architecture

DermaSense_AI-main/
├── app/                          # Next.js App Router (pages & API routes)
│   ├── (auth)/                   # Authentication group layout
│   ├── admin/                    # Admin portal
│   ├── api/                      # Server-side API routes (16 endpoints)
│   ├── dashboard/                # Patient dashboard (20+ sections)
│   ├── doctor-dashboard/         # Doctor management portal
│   ├── onboarding/               # User onboarding flow
│   ├── globals.css               # Global styles
│   ├── layout.tsx                # Root layout with providers
│   └── page.tsx                  # Landing page
│
├── api/                          # Python FastAPI AI Backend
│   ├── main.py                   # FastAPI app v2.0 (full pipeline)
│   ├── predict.py                # Legacy prediction module
│   ├── preprocessing.py          # Advanced image preprocessing
│   ├── azure_vision.py           # Azure Vision quality validator
│   ├── report_generator.py       # PDF medical report generator
│   └── model/                    # EfficientNet-B4 model weights
│
├── components/                   # Reusable React components
│   ├── analysis/                 # AI analysis UI components
│   ├── appointments/             # Appointment booking components
│   ├── dashboard/                # Patient dashboard layout
│   ├── doctor/                   # Doctor portal shell & gates
│   ├── home/                     # Landing page components
│   ├── shop/                     # E-commerce components
│   ├── treatment/                # Treatment info components
│   └── ui/                       # Shadcn/Radix UI primitives
│
├── lib/                          # Utilities & service integrations
│   ├── mongodb.ts                # MongoDB client singleton
│   ├── cloudinary.ts             # Cloudinary image upload
│   ├── email.ts                  # Email template system
│   ├── skin-disease-db.ts        # 40+ condition knowledge base
│   └── azure-openai-vision.ts    # Azure OpenAI GPT-4 Vision
│
├── database/                     # Database schemas
│   ├── mongodb-schema.ts         # TypeScript MongoDB interfaces
│   ├── schema.sql                # PostgreSQL schema
│   └── dynamodb-schema.json      # DynamoDB schema
│
├── contexts/                     # React Context providers
├── middleware.ts                 # Clerk auth + HIPAA-style headers
└── public/                       # Static assets

---

📁 Complete File Structure & Description

Root Configuration Files

File	Size	Description
package.json	3.5 KB	NPM dependencies: 70+ production packages including Next.js, Clerk, MongoDB, Stripe, TF.js, Framer Motion, Radix UI, i18next, Chart.js, Stripe, Cloudinary, Leaflet, etc.
tsconfig.json	0.7 KB	TypeScript compiler configuration with strict mode, path aliases (@/), and Next.js preset
next.config.mjs	0.9 KB	Next.js configuration: remote image domains (Cloudinary, Clerk, Unsplash), ESLint bypass for builds
tailwind.config.js	2.3 KB	Tailwind CSS config: custom theme colors, keyframe animations, dark mode class strategy
middleware.ts	1.9 KB	Clerk authentication middleware + HIPAA-style security headers (X-Frame-Options, HSTS, no-cache) for all API routes
vercel.json	77 B	Vercel deployment configuration
.env.local	5.6 KB	Environment variables (MongoDB URI, Clerk keys, Cloudinary, Azure, Stripe, Google Maps)
eslint.config.mjs	0.4 KB	ESLint rules for Next.js + TypeScript
postcss.config.mjs	0.1 KB	PostCSS configuration for Tailwind processing
next-i18next.config.js	0.25 KB	i18n localization configuration
components.json	0.4 KB	Shadcn/UI component registry configuration

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Frontend — app/ Directory

app/layout.tsx — Root Layout

The root application layout wrapping all pages. Provides:

• ClerkProvider — Authentication context
• ThemeProvider — Dark/light mode context
• ToastListener — Global toast notification system
• Google Fonts (Inter, Outfit) loading
• Global <html> structure with metadata

app/page.tsx (4.4 KB) — Landing Page

The public landing/home page. Features:

• Split-screen design: Left side — hero AI dermatology image with gradient overlay & branding quote. Right side — app name, animated sliding text, CTA button
• Smart redirect: Authenticated users see "Go to Dashboard", new users see "Get Started"
• SlidingText component with animated condition names cycling

app/globals.css (3.3 KB) — Global Styles

Base CSS variables for the design system including HSL color tokens, border radius, custom keyframes for the sliding text animation, and Tailwind base/components/utilities directives.

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app/(auth)/ — Authentication Group

File	Description
layout.tsx	Shared layout wrapper for auth pages
login/page.tsx	Clerk-hosted Sign In page with <SignIn> component
register/page.tsx	Clerk-hosted Sign Up page with <SignUp> component

Authentication is handled entirely by Clerk with OAuth support (Google, GitHub) and email/password, providing JWT session management with role-based claims.

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app/onboarding/ — User Onboarding

Multi-step onboarding flow for new users:

• Collect full name, date of birth, gender, phone number
• Address (city, state, country) via country-state-city picker
• Medical background info
• Role assignment (patient/doctor)
• Syncs to MongoDB profiles collection on completion

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app/dashboard/ — Patient Dashboard (20+ Sections)

The core patient-facing portal. app/dashboard/page.tsx (12.7 KB) is the main dashboard hub with:

Sub-route	Description
page.tsx	Hero banner, quick-actions grid (6 cards), real-time activity history
analysis/	AI skin analysis tool (camera capture + upload)
appointments/	Book, view, cancel appointments; payment integration
medical-history/	Full medical history timeline with filtering
metrics/	Health metrics dashboard with charts
profile/	Patient profile editor with image crop & upload
search/	Doctor search & discovery page
shop/	Dermatology products marketplace
cart/	Shopping cart with Stripe checkout
orders/	Order history and tracking
payment/	Payment processing with Stripe
wishlist/	Saved products wishlist
community/	Community forum & discussions
treatment/	Treatment plans & prescriptions
skin-tickets/	Skin concern ticket system
settings/	Account settings (theme, language, notifications)
header.tsx	Dashboard header with user avatar, notifications, search bar
layout.tsx	Dashboard shell with sidebar navigation

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app/doctor-dashboard/ — Doctor Portal

File	Description
page.tsx (11.5 KB)	Doctor main dashboard: greeting, patient stats, appointment overview, recent analyses, notification bell
layout.tsx	Doctor shell with role-gate (only verified doctor role users can access)
appointments/	Manage appointments: confirm, cancel, add meeting link, set availability slots
patients/	Patient registry: view all registered patients with medical history
analyses/	Review submitted skin analysis results from patients
profile/	Doctor profile editor: specialization, license, hospital, fees, bio, image

---

app/admin/ — Admin Panel

File	Description
page.tsx	Admin overview dashboard
layout.tsx	Admin role-gate (only admin role users)
doctors/	Manage doctor accounts: approve, suspend, promote users to doctor role

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app/api/ — Next.js API Routes (16 Endpoints)

Route	Method	Description
/api/predict	POST	Proxies to Hugging Face AI API for skin analysis
/api/predict-azure	POST	Azure OpenAI GPT-4 Vision skin analysis (alternative)
/api/profile	GET/POST/PUT	User profile CRUD (MongoDB)
/api/appointments	GET/POST	Appointment management
/api/appointments/[id]	GET/PUT/DELETE	Single appointment operations
/api/doctor	GET	Fetch single doctor profile
/api/doctors	GET	List all verified doctors with filters
/api/medical-history	GET/POST	Patient medical history records
/api/save-analysis	POST	Save AI analysis result to MongoDB + Cloudinary
/api/orders	GET/POST	E-commerce order management
/api/patient	GET	Patient profile lookup
/api/search	GET	Full-text search across doctors
/api/admin	GET/POST	Admin user management operations
/api/health	GET	API health check endpoint
/api/validate-frame	POST	Image frame validation
/api/update-products	POST	Admin product management
/api/cron/reminders	GET	Cron job: send appointment reminder emails

---

Components — components/

components/analysis/ — AI Analysis UI

File	Size	Description
image-analyzer.tsx	63.5 KB	The main AI analysis engine UI. Handles image upload, sends to /api/predict, renders Grad-CAM heatmap, confidence bars, class probability chart, disease details, urgency alert, and generates downloadable PDF
camera-capture.tsx	27.8 KB	In-browser camera with live preview, snapshot capture, body-part overlay guide, image quality checks, and retry functionality
pdf-export-button.tsx	14.9 KB	One-click PDF report generation using jsPDF + jspdf-autotable: renders diagnosis summary, heatmap, class probabilities table, precautions, specialist info
body-part-selector.tsx	7.2 KB	SVG human body diagram for users to select the skin region being analyzed

components/appointments/ — Appointment Booking

File	Size	Description
booking-modal.tsx	22.3 KB	Full appointment booking modal: calendar date picker (react-day-picker), time slot grid, appointment type selector (Video/In-Person/Phone), fee display, Stripe payment integration
doctor-card.tsx	12.4 KB	Doctor listing card: avatar, name, specialty, rating, experience, fee, available slots indicator, "Book Now" button
doctor-details.tsx	11.5 KB	Doctor detail drawer/modal: full profile, education, hospital affiliation, working hours, reviews
doctors-data.ts	16.1 KB	Static seed data for doctor listings with 15+ specialist profiles
appointment-confirmation.tsx	—	Post-booking confirmation screen with appointment details and calendar link

components/dashboard/ — Patient Dashboard Layout

File	Size	Description
sidebar.tsx	5.1 KB	Left navigation sidebar with all dashboard sections, active route highlighting, user avatar, logout
header.tsx	5.7 KB	Dashboard top header: search bar, notification bell, theme toggle, UserButton

components/doctor/ — Doctor Portal Components

File	Size	Description
doctor-shell.tsx	17.7 KB	Complete doctor portal layout: sidebar nav with sections (Dashboard, Appointments, Patients, Analyses, Profile), responsive collapse, notification count badges
profile-completion-gate.tsx	1.3 KB	Gate component blocking access until doctor has completed their profile

components/shop/ — E-commerce Components

File	Size	Description
product-card.tsx	4.9 KB	Product listing card with image, name, price, rating, add-to-cart, wishlist toggle
product-detail.tsx	9.2 KB	Product detail page: multiple images, description, ingredients, reviews
shop-cart.tsx	6.5 KB	Shopping cart with quantity control, total calculation, checkout CTA
checkout.tsx	9.2 KB	Full Stripe checkout with shipping address, payment form
medical-products.ts	5.7 KB	Static medical skincare product catalog (sunscreens, cleansers, treatments)
banner-carousel.tsx	3.2 KB	Shop hero banner with Swiper.js carousel
address-map.tsx	3.5 KB	Interactive Leaflet map for delivery address selection
delivery-map.tsx	2.2 KB	Order tracking delivery map
wishlist-context.tsx	2.5 KB	React Context for wishlist state management

components/home/

• sliding-text.tsx — Animated condition name ticker on the landing page (cycles through: "Melanoma", "Eczema", "Psoriasis", etc.)

components/ui/ — Shadcn/Radix Primitives

Complete set of 30+ UI primitives: Button, Dialog, Select, Tabs, Progress, Avatar, Accordion, Switch, Slider, RadioGroup, Toast, Popover, Dropdown, Label, Separator, Badge, Card, Input, Textarea, etc.

Other Components

File	Description
MapComponent.js	Leaflet map wrapper for location features
dashboard-chart.tsx	Recharts wrapper for health metric charts
theme-provider.tsx	next-themes dark/light provider
theme-toggle.tsx	Dark/light mode switch button
toast-listener.tsx	Global toast notification listener (Sonner)

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Python API Backend — api/

api/main.py (15.1 KB) — FastAPI Application v2.0

The core Python AI backend with the complete 6-step processing pipeline:

# 7 Skin Condition Classes (HAM10000)
CLASS_NAMES = ["akiec", "bcc", "bkl", "df", "mel", "nv", "vasc"]

Endpoints:

• GET / — Health check
• GET /health — Detailed status (model loaded, Azure enabled, PDF enabled)
• POST /predict — Full AI analysis pipeline

Key Functions:

• load_model_pipeline() — Priority loading: fine-tuned EfficientNet-B4 → TF Hub fallback → legacy model
• generate_gradcam() — True Grad-CAM: computes gradients of top class w.r.t. last conv layer, produces heatmap
• predict_with_ensemble() — Averages predictions across 4 augmented variants

⚠️ Note: The local Python API has been superseded by the Hugging Face API integration. The Next.js /api/predict route proxies to Hugging Face for production inference.

api/preprocessing.py (4.9 KB) — DermaSensePreprocessor

Advanced image preprocessing class:

Method	Description	Accuracy Boost
remove_hair()	BlackHat morphology + TELEA inpainting to remove hair artifacts	+2–3%
normalize_colors()	CLAHE in LAB color space — corrects lighting/skin tone differences	+2–4%
enhance_contrast()	Unsharp masking (3×3 kernel) for lesion boundary sharpening	+1–2%
augment_for_ensemble()	TTA: Original + H-flip + V-flip + 90° rotation	+3–5%
resize_to_target()	Lanczos4 interpolation to 224×224 (EfficientNet-B4 input)	—
normalize_to_tensor()	Float32 [0,1] normalization + batch dimension	—
full_pipeline_with_ensemble()	Complete preprocessing + 4 augmented tensors	—

api/azure_vision.py (4.4 KB) — AzureVisionValidator

Image quality validation before model inference:

Azure Vision Mode (when AZURE_VISION_ENABLED=true):

• Calls Azure Computer Vision API v2024-02-01
• Detects skin-related tags (skin, body, dermatology, lesion, mole)
• Rejects screenshots, documents, logos (bad_score > 0.6)
• Rejects non-skin images (skin_score < 0.1)

Local Fallback Mode (OpenCV + PIL):

• Checks minimum size (50×50 px)
• HSV skin tone detection (hue 0-20, saturation 20-255, value 70-255)
• Rejects if skin percentage < 8%
• Rejects if variance < 80 (too blurry/uniform)

api/report_generator.py (9.9 KB) — MedicalReportGenerator

Auto-generates professional A4 PDF medical reports using ReportLab:

Report Sections:

1. Header — DermaSense AI branding with blue heading
2. Patient Info Table — Name, Report ID, Date, Model version
3. Diagnosis Banner — Condition name, confidence %, risk level, severity stage
4. Risk Banner — Color-coded (🔴 Very High / 🟠 High / 🟡 Medium / 🟢 Low)
5. Grad-CAM Heatmap — Embedded 80×80mm heatmap image
6. Clinical Assessment — Full medical description
7. Recommended Action — Blue-bordered action box
8. Recommended Specialist — Doctor type and urgency
9. Precautions List — Bullet points with risk-colored bullets
10. Prediction Probabilities Table — All 7 class probabilities sorted
11. Disclaimer — AI-not-a-diagnosis legal disclaimer

api/predict.py (11.4 KB) — Legacy Prediction Module

Original binary Benign/Carcinoma classifier (deprecated). Retained for reference. Uses model.h5 (64×64 or 224×224 input).

api/Dockerfile — Docker Container

# Python 3.11 slim base
# Installs all requirements.txt dependencies
# Exposes port 8000
# Runs uvicorn main:app

api/requirements.txt — Python Dependencies

fastapi==0.111.0          # Web framework
uvicorn==0.30.1           # ASGI server
tensorflow==2.16.1        # Deep learning
opencv-python==4.9.0.80   # Image processing
Pillow==10.3.0            # Image I/O
reportlab==4.2.0          # PDF generation
scikit-image==0.24.0      # Image analysis
albumentations==1.4.0     # Advanced augmentation
tensorflow-hub==0.16.1    # TF Hub models
azure-cognitiveservices-vision-computervision==0.9.0  # Azure Vision

---

Library & Utilities — lib/

File	Size	Description
mongodb.ts	3.2 KB	MongoDB client singleton with connection pooling, supports Next.js HMR in development
cloudinary.ts	2.9 KB	Cloudinary SDK config + helper functions: uploadImage(), deleteImage(), signed upload URLs
email.ts	21.2 KB	Complete email template system using Nodemailer: appointment confirmation, reminder, cancellation, doctor notification, prescription notification — all HTML-formatted branded emails
skin-disease-db.ts	55.4 KB	🏆 Comprehensive knowledge base for 40+ ICD-10 coded skin conditions. Each condition includes: name, ICD-10 code, category (Malignant/Pre-Malignant/Benign/Inflammatory/Infectious/Pigmentation/Vascular/Follicular), severity, urgency, description, common body parts, visual markers, ABCDE criteria, symptoms, treatments, precautions, specialists, recurrence risk, contagious flag
azure-openai-vision.ts	11.9 KB	Azure OpenAI GPT-4 Vision integration: sends skin image for LLM-powered analysis as an alternative/supplement to the CNN model
azure-vision.ts	5.0 KB	Azure Computer Vision Node.js SDK wrapper for image tagging and captioning
doctor-dummy-data.ts	8.9 KB	Seed data for 15+ specialist doctor profiles (used in development)
profile-sync.ts	4.0 KB	Sync Clerk user metadata with MongoDB profile on auth events
mongodb.ts	3.2 KB	MongoDB connection manager with global client caching
database.types.ts	2.9 KB	Supabase auto-generated TypeScript types
email-internal.ts	1.5 KB	Internal email service (admin notifications)
env-validation.ts	1.5 KB	Runtime environment variable validation on startup
utils.ts	0.2 KB	cn() — Tailwind class merger utility (clsx + tailwind-merge)
aws-s3.ts	0.3 KB	AWS S3 SDK stub (replaced by Cloudinary)
aws-dynamodb.ts	0.5 KB	DynamoDB SDK stub
aws-database.ts	0.9 KB	AWS database connection stub

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Database — database/

database/mongodb-schema.ts (4.8 KB) — TypeScript Interfaces

Complete MongoDB data model:

Collection	Interface	Key Fields
profiles	IProfile	clerkUserId, email, fullName, role (patient/doctor/admin), isOnboarded, medicalInfo
skin_analyses	ISkinAnalysis	userId, imageUrl, predictionClass, confidenceScore, riskLevel, severityStage, heatmapUrl, pdfReportUrl, doctorReviewed
medical_history	IMedicalHistory	userId, type (Appointment/Medicine/Analysis/Treatment/Lab Test/Prescription), severity, status
appointments	IAppointment	patientId, doctorId, doctorClerkId, appointmentDate/Time, type (Video/In-Person/Phone), status, paymentStatus, meetingLink, prescriptionUrl, review, attachedReports
doctor_availability	IDoctorAvailabilitySlot	doctorClerkId, date (YYYY-MM-DD), timeSlot, isBooked
orders	IOrder	userId, items, subtotal, tax, shippingCost, totalAmount, paymentStatus, deliveryAddress
medications	IMedication	userId, medicationName, dosage, frequency, startDate, reminderEnabled
audit_logs	IAuditLog	userId, action, resourceType, ipAddress, userAgent
user_settings	IUserSettings	userId, theme, language, notificationsEnabled, twoFactorEnabled
doctor_notifications	IDoctorNotification	doctorId, patientId, title, message, type (Appointment/Analysis/Review), read
patient_notifications	IPatientNotification	patientId, doctorId, title, message, type, read

database/schema.sql (20.8 KB)

Full PostgreSQL relational schema as an alternative/complementary database.

database/dynamodb-schema.json (4.2 KB)

AWS DynamoDB table definitions for session management.

database/migration_*.sql

SQL migration scripts for role-based access control and doctor profile unique constraints.

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Context & State Management — contexts/

React Context providers for global state:

• WishlistContext — Tracks wishlist products (also in components/shop/)
• CartContext — Shopping cart item state
• ThemeContext — Dark/light mode (via next-themes)

---

✨ Core Features

🔬 AI Skin Analysis

• Upload photo or use live camera capture
• Body-part selection overlay for guided capture
• Real-time Azure Vision quality gate (rejects blurry/non-skin images)
• EfficientNet-B4 (87–91% accuracy) prediction with 7-class output
• Grad-CAM heatmap showing the exact region the AI focused on
• Confidence score with probability distribution bar chart
• Auto-generated, downloadable PDF medical report
• Save analysis to medical history with Cloudinary image storage

🏥 Doctor Ecosystem

• Browse 15+ verified specialist profiles with filtering (specialty, location, fee)
• Real-time slot availability calendar
• Multi-type booking (Video Call 📹 / In-Person 🏥 / Phone Call 📞)
• Stripe payment integration
• Automated email confirmations & reminders (Nodemailer)
• Doctor dashboard for managing patients, appointments, analyses
• Doctor profile: license, hospital, education, fee, working hours

📋 Health Records

• Complete medical history timeline
• Filter by type: Appointment / Medicine / Analysis / Treatment / Lab Test / Prescription
• Severity tagging (Low / Medium / High / Critical)
• Status tracking (Active / Completed / Cancelled / Pending)

🛒 Dermatology Shop

• Medically curated product catalog (sunscreens, cleansers, treatments)
• Product detail pages with ingredients and reviews
• Shopping cart with quantity management
• Stripe payment processing
• Order history and tracking
• Interactive delivery map (Leaflet)
• Wishlist with persistence

🛡️ Admin Portal

• User management dashboard
• Promote users to doctor role
• Suspend or deactivate accounts
• Doctor approval workflow

🌐 Additional Features

• Internationalization — i18next with multiple language support
• Dark/Light Mode — System-aware with manual toggle
• Responsive Design — Mobile-first, works on all screen sizes
• Community Forum — Patient community discussion board
• Skin Tickets — Support ticket system for skin concerns

---

🦠 Supported Skin Diseases (40+ Conditions)

The lib/skin-disease-db.ts knowledge base covers conditions across 9 dermatological categories:

🔴 Malignant (Critical — Immediate Action Required)

ICD-10	Condition	Urgency
C43.9	Melanoma	24–48 hours
C44.91	Basal Cell Carcinoma (BCC)	Within 1 week
C44.92	Squamous Cell Carcinoma (SCC)	Within 1 week
C44.029	Merkel Cell Carcinoma	Immediate

🟠 Pre-Malignant (High Risk)

ICD-10	Condition	Urgency
L57.0	Actinic Keratosis / Bowen's Disease	Within 1 month
D03.9	Lentigo Maligna	Within 1 week
D22.9	Atypical / Dysplastic Nevus	Within 1 month

🟢 Benign Growths

• Melanocytic Nevus (Mole) · Seborrheic Keratosis · Dermatofibroma · Solar Lentigo · Epidermal Cyst · Pyogenic Granuloma

🟡 Inflammatory & Autoimmune

• Psoriasis · Eczema / Atopic Dermatitis · Rosacea · Lupus (Discoid) · Lichen Planus · Dermatomyositis · Bullous Pemphigoid · Granuloma Annulare

🦠 Infectious

• Impetigo · Cellulitis · Herpes Zoster (Shingles) · Tinea (Ringworm) · Scabies · Molluscum Contagiosum · Verruca / Warts · Herpes Simplex

💜 Follicular

• Acne Vulgaris · Folliculitis · Hidradenitis Suppurativa

🎨 Pigmentation Disorders

• Vitiligo · Melasma · Acanthosis Nigricans · Post-Inflammatory Hyperpigmentation

🩸 Vascular

• Vascular Lesion (Hemangioma, Cherry Angioma, Port-Wine Stain)

Each condition in the database includes: ICD-10 code, severity level, urgency, description, common body parts, visual markers, ABCDE criteria, symptoms list, treatment options, precautions, recommended specialists, recurrence risk, and contagious flag.

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🔌 API Endpoints

Next.js API Routes

POST /api/predict              → AI skin analysis (→ Hugging Face)
POST /api/predict-azure        → GPT-4 Vision skin analysis
GET  /api/profile              → Get current user profile
POST /api/profile              → Create user profile
PUT  /api/profile              → Update user profile
GET  /api/doctors              → List all doctors (with filters)
GET  /api/doctor               → Get single doctor by clerkId
GET  /api/appointments         → Get user's appointments
POST /api/appointments         → Create new appointment
PUT  /api/appointments/[id]    → Update appointment (status, notes)
DEL  /api/appointments/[id]    → Cancel appointment
GET  /api/medical-history      → Get patient medical history
POST /api/medical-history      → Add medical history entry
POST /api/save-analysis        → Save AI analysis result
GET  /api/orders               → Get user orders
POST /api/orders               → Create new order
GET  /api/search               → Search doctors
GET  /api/health               → API health check
GET  /api/cron/reminders       → Appointment reminder cron (Vercel Cron)

Python FastAPI Endpoints

GET  /                         → API info & status
GET  /health                   → Detailed health check
POST /predict                  → Full AI pipeline (image upload → 7-class prediction)

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⚙️ Installation & Setup

Prerequisites

• Node.js ≥ 18.0.0
• Python ≥ 3.10 (for local AI backend)
• MongoDB (Atlas free tier or local)
• Clerk account (free tier)
• Cloudinary account (free tier)

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1. Clone the Repository

git clone https://github.com/sabareeshsp7/Derma-AI.git
cd Derma-AI

2. Install Node.js Dependencies

npm install

3. Install Python Dependencies (AI Backend)

cd api
python -m venv venv

# Windows
venv\Scripts\activate

# macOS/Linux
source venv/bin/activate

pip install -r requirements.txt

4. Configure Environment Variables

Create a .env.local file in the project root:

# ─── Clerk Authentication ───────────────────────────────────
NEXT_PUBLIC_CLERK_PUBLISHABLE_KEY=pk_test_xxxxxxxxxxxx
CLERK_SECRET_KEY=sk_test_xxxxxxxxxxxx
NEXT_PUBLIC_CLERK_SIGN_IN_URL=/login
NEXT_PUBLIC_CLERK_SIGN_UP_URL=/register
NEXT_PUBLIC_CLERK_AFTER_SIGN_IN_URL=/dashboard
NEXT_PUBLIC_CLERK_AFTER_SIGN_UP_URL=/onboarding

# ─── MongoDB ────────────────────────────────────────────────
MONGODB_URI=mongodb+srv://<user>:<password>@cluster.mongodb.net/dermasense

# ─── Cloudinary (Image Storage) ─────────────────────────────
NEXT_PUBLIC_CLOUDINARY_CLOUD_NAME=your_cloud_name
CLOUDINARY_API_KEY=your_api_key
CLOUDINARY_API_SECRET=your_api_secret

# ─── AI Backend ─────────────────────────────────────────────
NEXT_PUBLIC_AI_API_URL=https://your-huggingface-space.hf.space
# OR for local: http://localhost:8000

# ─── Azure Vision (Optional) ────────────────────────────────
AZURE_VISION_ENDPOINT=https://your-resource.cognitiveservices.azure.com
AZURE_VISION_KEY=your_azure_key
AZURE_VISION_ENABLED=true

# ─── Azure OpenAI (Optional) ────────────────────────────────
AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com
AZURE_OPENAI_KEY=your_key
AZURE_OPENAI_DEPLOYMENT=gpt-4o

# ─── Stripe Payments ────────────────────────────────────────
NEXT_PUBLIC_STRIPE_PUBLISHABLE_KEY=pk_test_xxxxxxxxxxxx
STRIPE_SECRET_KEY=sk_test_xxxxxxxxxxxx
STRIPE_WEBHOOK_SECRET=whsec_xxxxxxxxxxxx

# ─── Email (Nodemailer) ─────────────────────────────────────
EMAIL_HOST=smtp.gmail.com
EMAIL_PORT=587
EMAIL_USER=your@gmail.com
EMAIL_PASS=your_app_password

# ─── Google Maps ────────────────────────────────────────────
NEXT_PUBLIC_GOOGLE_MAPS_API_KEY=your_google_maps_key

# ─── Supabase (Optional) ────────────────────────────────────
NEXT_PUBLIC_SUPABASE_URL=https://xxxx.supabase.co
NEXT_PUBLIC_SUPABASE_ANON_KEY=your_anon_key

5. Run Development Server

Frontend (Next.js)

npm run dev
# Opens at http://localhost:3000

AI Backend (Python FastAPI)

cd api
uvicorn main:app --reload --host 0.0.0.0 --port 8000
# API at http://localhost:8000
# Docs at http://localhost:8000/docs

6. Test Database Connection

npm run test:db
npm run test:connections

7. Build for Production

npm run build
npm start

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🌍 Deployment

Vercel (Recommended for Frontend)

# Install Vercel CLI
npm i -g vercel

# Deploy
vercel --prod

Set all environment variables in Vercel Dashboard → Project → Settings → Environment Variables.

Configure Vercel Cron Job in vercel.json for appointment reminders:

{
  "crons": [
    {
      "path": "/api/cron/reminders",
      "schedule": "0 8 * * *"
    }
  ]
}

AI Backend (Hugging Face Spaces / Docker)

# Build Docker image
cd api
docker build -t dermasense-ai-api .

# Run container
docker run -p 8000:8000 dermasense-ai-api

# Or deploy to Hugging Face Spaces:
# Upload api/ directory as a Docker Space

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🔒 Security & Compliance

Authentication & Authorization

• Clerk — JWT-based sessions with RS256 signing
• Role-based access control (RBAC): patient / doctor / admin
• Middleware guards on all protected routes (/dashboard, /doctor-dashboard, /admin)
• Role stored in MongoDB (source of truth) + Clerk JWT claims

HIPAA-Style Security Headers

Applied to all /api/* responses via middleware.ts:

X-Content-Type-Options: nosniff
X-Frame-Options: DENY
Referrer-Policy: strict-origin-when-cross-origin
Cache-Control: no-store, no-cache, must-revalidate
Strict-Transport-Security: max-age=31536000; includeSubDomains

Data Privacy

• Medical images stored in Cloudinary with signed access URLs
• Patient data encrypted in MongoDB Atlas (encryption at rest)
• No PII stored in client-side state or localStorage
• All AI inference happens server-side (no model exposure)

AI Disclaimer

⚠️ This AI analysis is NOT a medical diagnosis. All results must be reviewed by a qualified dermatologist. The system explicitly communicates this disclaimer in every report and analysis result.

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🤝 Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/your-feature
3. Commit changes: git commit -m 'feat: add your feature'
4. Push to branch: git push origin feature/your-feature
5. Open a Pull Request

Code Style

• TypeScript strict mode enabled
• ESLint + Next.js recommended rules
• Tailwind CSS for all styling (no inline styles)
• Component files use PascalCase, utilities use camelCase

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📄 License

This project is licensed under the MIT License.

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⚠️ Disclaimer

DermaSense AI is an educational and research tool. The AI model has been trained on the HAM10000 dataset and achieves approximately 87–91% accuracy on that benchmark. However:

• It is NOT a substitute for professional medical diagnosis
• Accuracy varies with image quality, lighting, skin tone, and lesion characteristics
• All AI predictions must be reviewed by a qualified dermatologist
• Never delay seeking medical attention based on AI results
• The system is not FDA-cleared or CE-marked as a medical device

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Built with ❤️ by the DermaSense AI Team

Revolutionizing skin health with precision AI and expert care.