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https://github.com/aayushpaigwar/skinsense_ai

AI-powered skin disease detection app with image analysis, skincare tips, PDF reports, and dermatologist finder.
https://github.com/aayushpaigwar/skinsense_ai

azure azure-devops dart fastapi flutter

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AI-powered skin disease detection app with image analysis, skincare tips, PDF reports, and dermatologist finder.

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SkinSense AI Logo

# SkinSense AI 🔬

### AI-powered dermatological analysis for skin conditions

[![Flutter Version](https://img.shields.io/badge/Flutter-02569B?style=for-the-badge&logo=flutter&logoColor=white)](https://flutter.dev/)
[![Dart](https://img.shields.io/badge/Dart-0175C2?style=for-the-badge&logo=dart&logoColor=white)](https://dart.dev/)
[![Python](https://img.shields.io/badge/Python-3776AB?style=for-the-badge&logo=python&logoColor=white)](https://python.org/)
[![FastAPI](https://img.shields.io/badge/FastAPI-009688?style=for-the-badge&logo=fastapi&logoColor=white)](https://fastapi.tiangolo.com/)
[![Azure](https://img.shields.io/badge/Microsoft_Azure-0089D0?style=for-the-badge&logo=microsoft-azure&logoColor=white)](https://azure.microsoft.com/)

## 📱 App Screenshots


Analysis Results
AI Chatbot
Home Screen
PDF Report


Home Screen
Analysis Results
AI Chatbot
PDF Report

## 🌟 Features

- **Skin Image Analysis**: AI-powered analysis of skin conditions with confidence scores
- **AI Chatbot**: Interactive assistant for skin health questions and personalized advice
- **PDF Reports**: Professional medical-grade reports for sharing with healthcare providers
- **History Tracking**: Monitor skin condition changes over time
- **User-Friendly Interface**: Easy photo capture from camera or gallery
- **Easy Sharing**: Share analysis results and PDF reports via preferred apps

## 📁 Project Structure

```
lib/
├── models/
│ └── analysis_result.dart # Data model for analysis results
├── pages/
│ ├── analysis_page.dart # Display analysis results
│ ├── history_page.dart # View history of analyses
│ └── home_page.dart # Main app screen
├── services/
│ ├── chatbot_service.dart # Handle AI chatbot interactions
│ ├── gemini_service.dart # Integration with Google Gemini AI
│ ├── pdf_service.dart # Generate PDF reports
│ └── skin_analysis_service.dart # Process skin images and store results
├── widgets/
│ └── chatbot_widget.dart # UI for chatbot interactions
├── image_upload.dart # Helpers for camera and gallery uploads
├── main.dart # App entry point
└── theme.dart # App theming
```

## 🛠️ Tech Stack

- **Frontend**: Flutter 3.32.1
- **Fine-tuned Chatbot**: Google Gemini AI
- **AI/ML**: MobileNet V2 for image classification
- **Backend**: FastAPI for RESTful services
- **Model Deployment**: Azure WebApps

## 🚀 Getting Started

### Prerequisites

- Flutter SDK (3.32.1 or higher)
- Android Studio / VS Code
- Git

### Installation & Setup

1. **Clone the repository**

```bash
git clone https://github.com/aayushpaigwar/skinsense_ai.git
cd skinsense_ai
```

2. **Set up environment variables**

Create a `.env` file in the `assets/creds/` directory:

```env
BASE_URL=your_api_base_url_here
```

3. **Install dependencies**

```bash
flutter pub get
```

4. **Run the application**

```bash
flutter run
```

## 📱 Usage

### Analyzing Skin Images

1. Tap "Take Photo" to use the camera
2. Tap "Upload Photo" to select from gallery
3. Review the analysis results and recommendations

### Using the AI Chatbot

1. Tap the "Ask AI" floating action button
2. Type your skin health questions
3. Review the AI's responses

### Accessing Analysis History

1. Tap the history icon in the top-right corner
2. View and filter past analyses
3. Select an analysis to view detailed results

### Generating Reports

1. From an analysis result page, tap "Export as PDF"
2. Review and share the PDF report with healthcare providers

## ⚠️ Important Medical Disclaimer

**This application is for educational and informational purposes only and should not be used as a substitute for professional medical advice, diagnosis, or treatment. Always consult with qualified healthcare providers for medical concerns.**

## 📝 License

This project is licensed under the MIT License.

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