Add application file

app.py

@@ -0,0 +1,48 @@
+
+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)