This is a Bird Detection application that allows users to upload bird images and automatically get identification using an object detection model (YOLOv1). The app is built as a final project for the partial fulfillment of a Bachelor's in Computer Science and Information Technology.

The system uses YOLOv1 for real-time object detection and inference. The frontend is developed with Nuxt.js, and the backend API for model inference is powered by FastAPI.

• Frontend: Nuxt.js (Vue.js framework)
• Backend: FastAPI
• Model: YOLOv1 for object detection
• Database: SQLite and drizzle-orm
• Authentication: nuxt-auth-utils for authentication and password hashing

Before starting the setup, ensure you have the following installed:

• Python 3.x (preferably 3.8 or higher)
• pnpm (for nuxt package installation)
• pip (for Python package installation)
• git (for cloning the repository)

Follow the steps below to set up the project on your local machine.

Start by cloning the repository to your local machine:

git clone https://github.com/yourusername/bird-detection-app.git
cd bird-detection-app

cd frontend
pnpm install

cd ../backend
pip install -r requirements.txt

Make sure you have your model file inside backend folder.Then, Run the fast api service

uvicorn main:app --reload

Open new terminal. Make sure you are inside frontend folder

pnpm dev

There you go!!!. you can access the system from browser at "http://localhost:3000" or as per your setup. Rest the api handles all.. ENJOY!!!!