Leprosy Gradio Detection web application. In this application, I have used a fine tuned YOLOv5s model to detect Leprosy samples based on images. The application uses gradio as the platform and can also be used in the Huggingface online hosting application.

Leprosy, also known as Hansen's disease, is a chronic infectious disease that primarily affects the skin and peripheral nerves. The model used is a YOLO model from Ultralytics with their version YOLOv5.

• Upload an image to the app.
• Utilizes a fine-tuned YOLOv5s model for leprosy detection.
• Detect and label leprosy regions in the uploaded image.

Before running the application, make sure you have the following prerequisites installed on your system:

• Python 3.x
• Git
• Gradio
• Pip package manager
• Conda Virtual environment (optional but recommended)

To install the required Python libraries, navigate to the project directory and run the following command:

# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install
git clone https://huggingface.co/spaces/Arekku21/Leprosy-Detection

# if you want to clone without large files – just their pointers
# prepend your git clone with the following env var:
GIT_LFS_SKIP_SMUDGE=1

#navigate to your cloned repository and location of requirmenents.txt
pip install -r requirements.txt

#ensure that you are using the right environment or have all the requirements installed
#ensure that you are navigated to the cloned repository
python app.py

Your terminal should look like this and follow the local host URL link to use the application.

Downloading: "https://github.com/ultralytics/yolov5/zipball/master" to C:\Users\master.zip

YOLOv5  2023-9-28 Python-3.9.18 torch-2.0.1+cpu CPU

Fusing layers... 
Model summary: 157 layers, 7015519 parameters, 0 gradients, 15.8 GFLOPs
Adding AutoShape...
Running on local URL:  http://127.0.0.1:7860