import gradio as gr from ultralytics import YOLO model = YOLO('./best_model.pt') # load your custom trained model import torch #from ultralyticsplus import render_result from render import custom_render_result def yoloV8_func(image: gr.Image = None, image_size: int = 640, conf_threshold: float = 0.4, iou_threshold: float = 0.5): """This function performs YOLOv8 object detection on the given image. Args: image (gr.Image, optional): Input image to detect objects on. Defaults to None. image_size (int, optional): Desired image size for the model. Defaults to 640. conf_threshold (float, optional): Confidence threshold for object detection. Defaults to 0.4. iou_threshold (float, optional): Intersection over Union threshold for object detection. Defaults to 0.50. """ # Load the YOLOv8 model from the 'best.pt' checkpoint model_path = "yolov5s.pt" # model = torch.hub.load('ultralytics/yolov8', 'custom', path='/content/best.pt', force_reload=True, trust_repo=True) # Perform object detection on the input image using the YOLOv8 model results = model.predict(image, conf=conf_threshold, iou=iou_threshold, imgsz=image_size) # Print the detected objects' information (class, coordinates, and probability) box = results[0].boxes print("Object type:", box.cls) print("Coordinates:", box.xyxy) print("Probability:", box.conf) # Render the output image with bounding boxes around detected objects render = custom_render_result(model=model, image=image, result=results[0]) return render inputs = [ gr.Image(type="filepath", label="Input Image"), gr.Slider(minimum=320, maximum=1280, step=32, label="Image Size", value=640), gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label="Confidence Threshold"), gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label="IOU Threshold"), ] outputs = gr.Image(type="filepath", label="Output Image") title = "Custom_YOLOV9_model 🤖: room-cleanliness-detector 👔🧦💫 " examples = [['one.jpg', 640, 0.5, 0.7], ['two.jpg', 640, 0.5, 0.6], ['three.jpg', 640, 0.5, 0.8]] yolo_app = gr.Interface( fn=yoloV8_func, inputs=inputs, outputs=outputs, title=title, examples=examples, cache_examples=False, ) # Launch the Gradio interface in debug mode with queue enabled yolo_app.launch(debug=True, share=True).queue()