🐱 Flask Cat Detection Project

Welcome to the Flask Cats Project! This project offers a friendly Flask-based web interface for detecting cats in images using the YOLOv8 object detection model. Just upload an image and find out if there are any adorable cats hidden within! 🖼️🐾

---

🚀 Features

• Cat Detection with YOLOv8: Accurate and fast object detection using a model trained on a custom cat dataset.
• Flask Web Interface: Simple, interactive UI for uploading images and viewing detection results.
• Docker Support: Run the entire app with a single Docker command.

---

🧑‍💻 Getting Started

🔧 Prerequisites

Make sure you have the following installed:

• Python 3.8+
• pip – Python package manager
• Virtual environment tool (optional, but recommended)

---

⚙️ Run Locally (Without Docker)

# Clone the repository
git clone https://github.com/PhillMckinnon/flask_cats_project.git
cd flask_cats_project/main_app

# (Optional) Create and activate a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

# Install dependencies
pip install -r requirements.txt

# Run the Flask app
python app.py

---

🐳 Run with Docker (Recommended)

docker pull phillipmckinnon/flask-cats:latest
docker run -p 8080:8080 phillipmckinnon/flask-cats:latest

The app will be available at: http://localhost:8080

---

🧠 Model Details

• YOLOv8: A state-of-the-art object detection model, trained on a custom dataset of cat images.
• Detects cats with high precision and speed using a lightweight, optimized model.

---

💻 Technologies Used

• YOLOv8 – Object detection model
• Flask – Lightweight web framework for Python
• PIL (Pillow) – Image processing library for Python

---

📈 Example

Upload an image containing a cat, like the one below:

---

📫 Contact

For questions, feedback, or collaboration, feel free to reach out:

• 📧 Email: phillipmckinnonwork@proton.me
• 🐙 GitHub: @PhillMckinnon

Happy cat detecting! 😺🐾