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import gradio as gr
import torch
from ultralyticsplus import YOLO, render_result
from PIL import Image
import os
def yolov8_func(image,
image_size,
conf_thresold=0.4,
iou_thresold=0.50):
# Load the YOLOv8 model
model_path = "best.pt"
model = YOLO(model_path) # 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 the result
render = render_result(model=model, image=image, result=result[0])
# Save the rendered image locally
save_path = "predicted_image.jpg" # Specify the output path
render.save(save_path) # Save using PIL's save method
return save_path
# 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)
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