Update app.py

app.py

@@ -20,12 +20,6 @@ def yolov8_func(image,
     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"
@@ -37,9 +31,6 @@ def yolov8_func(image,
         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])
     
@@ -47,33 +38,27 @@ def yolov8_func(image,
     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")
+# Create the Gradio app using Blocks
+with gr.Blocks() as yolo_app:
+    gr.Markdown("# YOLOv8: An Object Detection for Acne")
 
-# Define the layout using Rows and Columns
-outputs = [
-    output_image,
-    gr.Row([acne_condition, recommendation])
-]
+    with gr.Row():
+        with gr.Column():
+            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")
 
-# Set the title of the Gradio app
-title = "YOLOv8: An Object Detection for Acne"
+        with gr.Column():
+            output_image = gr.Image(type="filepath", label="Output Image")
+            acne_condition = gr.Textbox(label="Acne Condition")
+            recommendation = gr.Textbox(label="Recommendation")
 
-# Create the Gradio interface
-yolo_app = gr.Interface(fn=yolov8_func,
-                    inputs=inputs,
-                    outputs=outputs,
-                    title=title)
+    # 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])
 
 # Launch the app
 yolo_app.launch(debug=True)