{"id":23536207,"url":"https://github.com/talaatmagdyx/animal-classifier-app","last_synced_at":"2026-05-16T11:34:26.079Z","repository":{"id":269627397,"uuid":"908027928","full_name":"talaatmagdyx/animal-classifier-app","owner":"talaatmagdyx","description":"This is a Streamlit web application that allows users to upload an image of an animal and predicts the name of the animal using a pre-trained MobileNetV2 model. 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The app leverages the power of **TensorFlow** and **ImageNet**-trained weights to classify images.\n\n---\n\n## Features ✨\n\n- 🖼️ **Upload Image**: Upload any `.jpg`, `.jpeg`, or `.png` file.\n- 🧠 **AI-Powered Classification**: Uses MobileNetV2, a state-of-the-art pre-trained deep learning model.\n- 🚀 **Real-time Results**: Provides instant predictions with a simple and intuitive interface.\n- 🌐 **Web-Based**: Accessible from any device with a browser.\n\n---\n\n## Demo 🎥\n\n\u003e [Click here to try the app!](https://animal-classifier-app-talaatx.streamlit.app/)\n\n---\n\n## How It Works 🛠️\n\n1. **Upload an Image**:\n   - Drag and drop or browse to select an image of an animal.\n2. **AI Processing**:\n   - The app resizes and preprocesses the image to match the model's input format.\n   - The MobileNetV2 model predicts the animal name based on the image.\n3. **Get Results**:\n   - The predicted animal name is displayed instantly on the screen.\n\n---\n\n## Installation \u0026 Usage 🖥️\n\n### Prerequisites\n- Python 3.8 or higher\n- pip (Python package manager)\n\n### 1. Clone the Repository\n```bash\ngit clone https://github.com/talaatmagdyx/animal-classifier-app.git\ncd animal-classifier-app\n```\n\n### 2. Install Dependencies\n```bash\npip install -r requirements.txt\n```\n\n### 3. Run the App Locally\n```bash\nstreamlit run animal_classifier_app.py\n```\n\nThe app will run locally and can be accessed at `http://localhost:8501` in your browser.\n\n---\n\n## Deployment 🚀\n\n### Streamlit Cloud\n1. Push the code to your GitHub repository.\n2. Log in to [Streamlit Cloud](https://streamlit.io/cloud) and deploy the app.\n\n### Docker\n1. Build the Docker image:\n   ```bash\n   docker build -t animal-classifier .\n   ```\n2. Run the Docker container:\n   ```bash\n   docker run -p 8501:8501 animal-classifier\n   ```\n\nThe app will be accessible at `http://localhost:8501`.\n\n---\n\n## File Structure 📂\n\n```plaintext\nanimal-classifier-app/\n│\n├── animal_classifier_app.py   # Main Streamlit app code\n├── requirements.txt           # List of dependencies\n├── Dockerfile                 # For containerization (optional)\n└── README.md                  # Documentation file\n```\n\n---\n\n## Dependencies 📦\n\n- **Streamlit**: For building the web app.\n- **TensorFlow**: For using the pre-trained MobileNetV2 model.\n- **Pillow**: For image processing.\n\n---\n\n## Screenshots 📸\n\n### Upload Image\n![Upload Screenshot](screenshot/start_point.png)\n\n### Prediction Output\n![Prediction Screenshot](screenshot/lion_example.png)\n\n---\n\n## Future Improvements 🚧\n\n- Fine-tune the model with an animal-specific dataset for better accuracy.\n- Add support for identifying multiple animals in a single image.\n- Enable multilingual support in the app interface.\n\n---\n\n## Contributing 🤝\n\nContributions are welcome! Feel free to submit a pull request or open an issue for bug fixes, enhancements, or suggestions.\n\n---\n\n## License 📜\n\nThis project is licensed under the [MIT License](LICENSE).\n\n---\nFeel free to reach out with any questions or suggestions!\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftalaatmagdyx%2Fanimal-classifier-app","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftalaatmagdyx%2Fanimal-classifier-app","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftalaatmagdyx%2Fanimal-classifier-app/lists"}