Animal Classifier App 🐾

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. The app leverages the power of TensorFlow and ImageNet-trained weights to classify images.

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Features ✨

• 🖼️ Upload Image: Upload any .jpg, .jpeg, or .png file.
• 🧠 AI-Powered Classification: Uses MobileNetV2, a state-of-the-art pre-trained deep learning model.
• 🚀 Real-time Results: Provides instant predictions with a simple and intuitive interface.
• 🌐 Web-Based: Accessible from any device with a browser.

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Demo 🎥

Click here to try the app!

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How It Works 🛠️

1. Upload an Image:
   • Drag and drop or browse to select an image of an animal.
2. AI Processing:
   • The app resizes and preprocesses the image to match the model's input format.
   • The MobileNetV2 model predicts the animal name based on the image.
3. Get Results:
   • The predicted animal name is displayed instantly on the screen.

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Installation & Usage 🖥️

Prerequisites

• Python 3.8 or higher
• pip (Python package manager)

1. Clone the Repository

git clone https://github.com/talaatmagdyx/animal-classifier-app.git
cd animal-classifier-app

2. Install Dependencies

pip install -r requirements.txt

3. Run the App Locally

streamlit run animal_classifier_app.py

The app will run locally and can be accessed at http://localhost:8501 in your browser.

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Deployment 🚀

Streamlit Cloud

1. Push the code to your GitHub repository.
2. Log in to Streamlit Cloud and deploy the app.

Docker

1. Build the Docker image:

   docker build -t animal-classifier .

2. Run the Docker container:

   docker run -p 8501:8501 animal-classifier

The app will be accessible at http://localhost:8501.

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File Structure 📂

animal-classifier-app/
│
├── animal_classifier_app.py   # Main Streamlit app code
├── requirements.txt           # List of dependencies
├── Dockerfile                 # For containerization (optional)
└── README.md                  # Documentation file

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Dependencies 📦

• Streamlit: For building the web app.
• TensorFlow: For using the pre-trained MobileNetV2 model.
• Pillow: For image processing.

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Screenshots 📸

Upload Image

Prediction Output

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Future Improvements 🚧

• Fine-tune the model with an animal-specific dataset for better accuracy.
• Add support for identifying multiple animals in a single image.
• Enable multilingual support in the app interface.

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Contributing 🤝

Contributions are welcome! Feel free to submit a pull request or open an issue for bug fixes, enhancements, or suggestions.

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License 📜

This project is licensed under the MIT License.

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Feel free to reach out with any questions or suggestions!