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