Spaces:
Running
Running
| 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) | |
| # Make predictions | |
| result = model.predict(image, conf=conf_thresold, iou=iou_thresold, imgsz=image_size) | |
| # Access object detection results | |
| boxes = result[0].boxes | |
| num_boxes = len(boxes) | |
| # 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]) | |
| predicted_image_save_path = "predicted_image.jpg" | |
| render.save(predicted_image_save_path) | |
| 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=0.2, label="IOU Threshold") | |
| ] | |
| # Use a Row layout to align the textboxes for condition and recommendation | |
| output_image = gr.Image(type="filepath", label="Output Image") | |
| acne_condition = gr.Textbox(label="Acne Condition") | |
| recommendation = gr.Textbox(label="Recommendation") | |
| # Define the layout using Rows and Columns | |
| outputs = [ | |
| output_image, | |
| gr.Row([acne_condition, 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) |