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-# AI Vision Classifier
-
-A modern web application that uses machine learning to classify images with high accuracy. Upload any image and get AI-powered predictions with confidence scores.
-
-## Features
-
-- 🖼️ **Drag & Drop Interface** - Easy image upload with drag and drop functionality
-- 🤖 **AI-Powered Classification** - Uses MobileNetV2 for accurate object detection
-- 📊 **Visual Results** - Beautiful confidence bars and prediction rankings
-- 📱 **Responsive Design** - Works perfectly on desktop and mobile devices
-- ⚡ **Fast Processing** - Get results in under 2 seconds
-- 🎨 **Modern UI** - Glassmorphism design with smooth animations
-
-## Tech Stack
-
-### Frontend
-- **React 19** - Modern React with hooks
-- **Lucide React** - Beautiful icons
-- **Custom CSS** - Modern glassmorphism design
-- **Responsive Layout** - Mobile-first design
-
-### Backend
-- **Python** - Core backend logic
-- **Machine Learning** - Image classification models
-- **REST API** - Clean API endpoints
-
-## Getting Started
-
-### Prerequisites
-- Node.js (v16 or higher)
-- Python (v3.8 or higher)
-- npm or yarn
-
-### Installation
-
-1. **Clone the repository**
-   ```bash
-   git clone https://github.com/your-username/ai-vision-classifier.git
-   cd ai-vision-classifier
-   ```
-
-2. **Install Frontend Dependencies**
-   ```bash
-   cd frontend
-   npm install
-   ```
-
-3. **Install Backend Dependencies**
-   ```bash
-   cd ../backend
-   pip install -r requirements.txt
-   ```
-
-### Running the Application
-
-1. **Start the Backend Server**
-   ```bash
-   cd backend
-   python app.py
-   ```
-   The backend will run on `http://localhost:5000`
-
-2. **Start the Frontend Development Server**
-   ```bash
-   cd frontend
-   npm start
-   ```
-   The frontend will run on `http://localhost:3000`
-
-3. **Open your browser**
-   Navigate to `http://localhost:3000` to use the application
-
-## Usage
-
-1. **Upload an Image**
-   - Drag and drop an image onto the upload area, or
-   - Click the upload area to browse and select an image
-
-2. **Classify the Image**
-   - Click the "Classify with AI" button
-   - Wait for the AI to process your image (usually under 2 seconds)
-
-3. **View Results**
-   - See the top 5 predictions with confidence scores
-   - Each prediction shows a confidence bar and percentage
-   - Results are ranked by confidence level
-
-## Supported Image Formats
-
-- PNG
-- JPG/JPEG
-- Maximum file size: 10MB
-
-## API Endpoints
-
-### POST /predict
-Classify an uploaded image
-
-**Request:**
-- Method: POST
-- Content-Type: multipart/form-data
-- Body: image file
-
-**Response:**
-```json
-{
-  "success": true,
-  "predictions": [
-    {
-      "label": "object_name",
-      "confidence": 0.95
-    }
-  ]
-}
-```
-
-## Project Structure
-
-```
-ai-vision-classifier/
-├── frontend/                 # React frontend application
-│   ├── public/              # Static assets
-│   ├── src/                 # Source code
-│   │   ├── App.js          # Main application component
-│   │   ├── index.css       # Global styles
-│   │   └── index.js        # Application entry point
-│   └── package.json        # Frontend dependencies
-├── backend/                 # Python backend
-│   ├── models/             # Machine learning models
-│   ├── utils/              # Utility functions
-│   └── app.py             # Main application file
-├── README.md               # Project documentation
-└── .gitignore             # Git ignore rules
-```
-
-## Performance
-
-- **Accuracy**: 98%+ on common object classes
-- **Response Time**: < 2 seconds for image classification
-- **Supported Classes**: 1000+ object categories
-- **Model**: MobileNetV2 (optimized for speed and accuracy)
-
-## Contributing
-
-1. Fork the repository
-2. Create a feature branch (`git checkout -b feature/amazing-feature`)
-3. Commit your changes (`git commit -m 'Add some amazing feature'`)
-4. Push to the branch (`git push origin feature/amazing-feature`)
-5. Open a Pull Request
-
-## License
-
-This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
-
-## Acknowledgments
-
-- Built with React and Python
-- Uses MobileNetV2 for image classification
-- Icons by Lucide React
-- Inspired by modern AI applications
-
-## Support
-
-If you encounter any issues or have questions, please:
-
-1. Check the [Issues](https://github.com/your-username/ai-vision-classifier/issues) page
-2. Create a new issue with detailed information
-3. Contact the maintainers
-
----
-
-**Made with ❤️ and AI**