app.py

import gradio as gr
import torch
from ultralyticsplus import YOLO, render_result

def yolov8_func(image, 
                image_size, 
                conf_thresold=0.4,
                iou_thresold=0.50):

    # Load the YOLOv8 model
    model = YOLO('/content/runs/detect/train/weights/best.pt')  # Use your custom model path here

    # Make predictions
    result = model.predict(image, conf=conf_thresold, iou=iou_thresold, imgsz=image_size)

    # Access and print object detection results
    box = result[0].boxes
    print("Object type: ", box.cls)
    print("Confidence: ", box.conf)
    print("Coordinates: ", box.xyxy)

    # Render and return the result
    render = render_result(model=model, image=image, result=result[0])
    return render

# Define inputs for the Gradio app
inputs = [
    gr.Image(type="filepath", label="Input Image"),
    gr.Slider(minimum=320, maximum=1280, step=32, value=640, label="Image Size"),
    gr.Slider(minimum=0, maximum=1, step=0.05, value=0.25, label="Confidence Threshold"),
    gr.Slider(minimum=0, maximum=1, step=0.05, value=0.45, label="IOU Threshold")
]

# Define the output for the Gradio app
outputs = gr.Image(type="filepath", label="Output Image")

# Set the title of the Gradio app
title = "YOLOv8: An Object Detection for Acne"

# Create the Gradio interface
yolo_app = gr.Interface(fn=yolov8_func,
                    inputs=inputs,
                    outputs=outputs,
                    title=title)

# Launch the app
yolo_app.launch(debug=True)