https://github.com/talaatmagdyx/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.
https://github.com/talaatmagdyx/animal-classifier-app
ai-app streamlit
Last synced: about 1 month ago
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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.
- Host: GitHub
- URL: https://github.com/talaatmagdyx/animal-classifier-app
- Owner: talaatmagdyx
- Created: 2024-12-24T23:10:30.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-24T23:49:13.000Z (over 1 year ago)
- Last Synced: 2025-09-10T18:50:41.236Z (10 months ago)
- Topics: ai-app, streamlit
- Language: Python
- Homepage: https://animal-classifier-app-talaatx.streamlit.app/
- Size: 6.41 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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.
---
## 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.
---
## Demo 🎥
> [Click here to try the app!](https://animal-classifier-app-talaatx.streamlit.app/)
---
## 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.
---
## Installation & Usage 🖥️
### Prerequisites
- Python 3.8 or higher
- pip (Python package manager)
### 1. Clone the Repository
```bash
git clone https://github.com/talaatmagdyx/animal-classifier-app.git
cd animal-classifier-app
```
### 2. Install Dependencies
```bash
pip install -r requirements.txt
```
### 3. Run the App Locally
```bash
streamlit run animal_classifier_app.py
```
The app will run locally and can be accessed at `http://localhost:8501` in your browser.
---
## Deployment 🚀
### Streamlit Cloud
1. Push the code to your GitHub repository.
2. Log in to [Streamlit Cloud](https://streamlit.io/cloud) and deploy the app.
### Docker
1. Build the Docker image:
```bash
docker build -t animal-classifier .
```
2. Run the Docker container:
```bash
docker run -p 8501:8501 animal-classifier
```
The app will be accessible at `http://localhost:8501`.
---
## File Structure 📂
```plaintext
animal-classifier-app/
│
├── animal_classifier_app.py # Main Streamlit app code
├── requirements.txt # List of dependencies
├── Dockerfile # For containerization (optional)
└── README.md # Documentation file
```
---
## Dependencies 📦
- **Streamlit**: For building the web app.
- **TensorFlow**: For using the pre-trained MobileNetV2 model.
- **Pillow**: For image processing.
---
## Screenshots 📸
### Upload Image
### Prediction Output
---
## 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.
---
## Contributing 🤝
Contributions are welcome! Feel free to submit a pull request or open an issue for bug fixes, enhancements, or suggestions.
---
## License 📜
This project is licensed under the [MIT License](LICENSE).
---
Feel free to reach out with any questions or suggestions!