Delete app.py

app.py

@@ -1,64 +0,0 @@
-import gradio as gr
-from ultralyticsplus import YOLO, render_result
-
-
-def yoloV8_func(image: gr.inputs.Image = None,
-                image_size: gr.inputs.Slider = 640,
-                conf_threshold: gr.inputs.Slider = 0.4,
-                iou_threshold: gr.inputs.Slider = 0.50):
-    """This function performs YOLOv8 object detection on the given image.
-
-    Args:
-        image (gr.inputs.Image, optional): Input image to detect objects on. Defaults to None.
-        image_size (gr.inputs.Slider, optional): Desired image size for the model. Defaults to 640.
-        conf_threshold (gr.inputs.Slider, optional): Confidence threshold for object detection. Defaults to 0.4.
-        iou_threshold (gr.inputs.Slider, optional): Intersection over Union threshold for object detection. Defaults to 0.50.
-    """
-    # Load the YOLOv8 model from the 'best.pt' checkpoint
-    model_path = "best.pt"
-    model = YOLO(model_path)
-
-    # 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 = render_result(model=model, image=image, result=results[0])
-    return render
-
-
-inputs = [
-    gr.inputs.Image(type="filepath", label="Input Image"),
-    gr.inputs.Slider(minimum=320, maximum=1280, default=640,
-                     step=32, label="Image Size"),
-    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25,
-                     step=0.05, label="Confidence Threshold"),
-    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45,
-                     step=0.05, label="IOU Threshold"),
-]
-
-
-outputs = gr.outputs.Image(type="filepath", label="Output Image")
-
-title = "Water Meter"
-
-
-
-yolo_app = gr.Interface(
-    fn=yoloV8_func,
-    inputs=inputs,
-    outputs=outputs,
-    title=title,
-    cache_examples=True,
-)
-
-# Launch the Gradio interface in debug mode with queue enabled
-yolo_app.launch(debug=True, enable_queue=True)