Update app.py

app.py

@@ -16,19 +16,39 @@ def yolov8_func(image,
     # 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)
+    # Access object detection results
+    boxes = result[0].boxes  # Bounding boxes
+    num_boxes = len(boxes)   # Count the number of bounding boxes (detections)
 
-    # Render the result
+    # 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 locally
-    save_path = "predicted_image.jpg"  # Specify the output path
-    render.save(save_path)  # Save using PIL's save method
-    return save_path
+    # 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 = [
@@ -38,8 +58,12 @@ inputs = [
     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")
+# 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"