Update app.py

app.py

@@ -11,54 +11,68 @@ def yolov8_func(image,
 
     # Load the YOLOv8 model
     model_path = "best.pt"
-    model = YOLO(model_path)  
-    
+    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  
-    num_boxes = len(boxes)   
+    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:
+    if num_boxes > 20:
         severity = "Worse"
         recommendation = "It is recommended to see a dermatologist and start stronger acne treatment."
-    elif 5 <= num_boxes <= 10:
+    elif 10 <= num_boxes <= 20:
         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
 
-# Create the Gradio
-with gr.Blocks() as yolo_app:
-    gr.Markdown("# YOLOv8: An Object Detection for Acne")
+# 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")
+]
 
-    with gr.Row():
-        with gr.Column(scale=1):  # Left side with input
-            input_image = gr.Image(type="filepath", label="Input Image")
-            image_size = gr.Slider(minimum=320, maximum=1280, step=32, value=640, label="Image Size")
-            conf_thresh = gr.Slider(minimum=0, maximum=1, step=0.05, value=0.15, label="Confidence Threshold")
-            iou_thresh = gr.Slider(minimum=0, maximum=1, step=0.05, value=0.2, label="IOU Threshold")
-            submit_btn = gr.Button("Submit")
+# 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")
+]
 
-        with gr.Column(scale=1):  # Right side with output
-            output_image = gr.Image(type="filepath", label="Output Image")
-            acne_condition = gr.Textbox(label="Acne Condition")
-            recommendation = gr.Textbox(label="Recommendation")
+# Set the title of the Gradio app
+title = "YOLOv8: An Object Detection for Acne"
 
-    # Link the submit button to the function
-    submit_btn.click(fn=yolov8_func, 
-                     inputs=[input_image, image_size, conf_thresh, iou_thresh], 
-                     outputs=[output_image, acne_condition, recommendation])
+# 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)