🐱🐶 Cat vs Dog Classifier

An interactive deep learning + Streamlit web app that classifies cats and dogs from uploaded images with confidence scores.

Built to practice real-world deep learning workflows using TensorFlow, Streamlit, and your custom dataset.

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🚀 Features

• Upload an image of a cat or dog
• See the predicted label with emoji (🐱 or 🐶)
• Displays confidence (accuracy) %
• Clean, centered, user-friendly interface
• Runs locally or can be deployed online (Streamlit Cloud)

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🖥️ Demo

Above: Example usage of the app with a sample image. Upload your own cat or dog photo to see predictions!

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🛠️ Tech Stack

• Python 3.10+
• TensorFlow (for the trained CNN model)
• Streamlit (for the web app)
• Pillow & NumPy (for image processing)

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🗂️ Project Structure

cat-dog-classifier/
│
├── app.py                  # Streamlit app
├── cat_dog_classifier.h5   # Trained model
├── requirements.txt        # Dependencies for deployment
└── README.md               # Project documentation

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⚡ Installation & Running Locally

1️⃣ Clone this repository

git clone https://github.com/SultanAhmmed/cat-dog-classifier.git
cd cat-dog-classifier

2️⃣ Create and activate a virtual environment (recommended)

Windows:

python -m venv dl_env
.\dl_env\Scripts\activate

Linux / Mac:

python3 -m venv dl_env
source dl_env/bin/activate

3️⃣ Install dependencies

pip install -r requirements.txt

4️⃣ Run the app

streamlit run app.py

Your browser will open automatically. Upload a cat or dog image and get your prediction!

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📸 Example Predictions

• Upload image: dog.jpg
  Output: "✅ It's a Dog 🐶", Confidence: 98.3%

• Upload image: cat.jpg
  Output: "✅ It's a Cat 🐱", Confidence: 97.1%

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🎯 Why this project?

• Practice image-based deep learning projects
• Learn TensorFlow workflow (training, saving, loading models)
• Learn building and deploying ML apps with Streamlit
• Build a portfolio project for your CV

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🪴 Future Improvements

• Allow live webcam capture
• Display model architecture and training accuracy
• Add batch image classification
• Support mobile responsiveness

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🙌 Contributing

PRs and suggestions are welcome to improve this project and help learners build stronger deep learning + deployment workflows.

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📜 License

This project is under the MIT License.

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💡 Author

👤 Sultan Ahmmed
🌱 Learning and building practical AI projects to improve real-world skills.