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app.py

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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 object detection results
+    boxes = result[0].boxes  # Bounding boxes
+    num_boxes = len(boxes)   # Count the number of bounding boxes (detections)
+
+    # Print object detection details (optional)
+    print("Object type: ", boxes.cls)
+    print("Confidence: ", boxes.conf)
+    print("Coordinates: ", boxes.xyxy)
+    print(f"Number of bounding boxes: {num_boxes}")
+
+    # Categorize based on number of boxes (detections) and provide recommendations
+    if num_boxes > 10:
+        severity = "Worse"
+        recommendation = "It is recommended to see a dermatologist and start stronger acne treatment."
+    elif 5 <= num_boxes <= 10:
+        severity = "Medium"
+        recommendation = "You should follow a consistent skincare routine with proper cleansing and moisturizing."
+    else:
+        severity = "Good"
+        recommendation = "Your skin looks good! Keep up with your current skincare routine."
+
+    print(f"Acne condition: {severity}")
+    print(f"Recommendation: {recommendation}")
+
+    # Render the result (with bounding boxes/labels)
+    render = render_result(model=model, image=image, result=result[0])
+    
+    # Save the rendered image (with predictions)
+    predicted_image_save_path = "predicted_image.jpg"
+    render.save(predicted_image_save_path)
+
+    # Return the saved image, severity, and recommendation for Gradio output
+    return predicted_image_save_path, f"Acne condition: {severity}", recommendation
+
+# 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=0.15, label="Confidence Threshold"),
+    gr.Slider(minimum=0, maximum=1, step=0.05, value=0.15, label="IOU Threshold")
+]
+
+# Define the output for the Gradio app (image + text for severity and recommendation)
+outputs = [
+    gr.Image(type="filepath", label="Output Image"),
+    gr.Textbox(label="Acne Condition"),
+    gr.Textbox(label="Recommendation")
+]
+
+# 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)

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:88dd8766ff1f53969339c030cb78f8736148c25a1bbeec111d171e5126ce08e4
+size 19956335

requirements.txt

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+gradio==5.0.0
+torch
+ultralyticsplus==0.1.0