Update app.py

app.py

@@ -32,22 +32,22 @@ def compare_models(input_image):
     # Convert from Gradio's PIL image to OpenCV format
     image = np.array(input_image)
     
-    # Process with both models
-    duck_image = process_image(image, duck_model)
+    # Process with both models (standard first, then duck)
     standard_image = process_image(image, standard_model)
+    duck_image = process_image(image, duck_model)
     
     # Create side-by-side comparison
     height, width = image.shape[:2]
     canvas = np.zeros((height, width * 2, 3), dtype=np.uint8)
     
-    # Place images side by side
-    canvas[:, :width] = duck_image
-    canvas[:, width:] = standard_image
+    # Place images side by side (standard left, duck right)
+    canvas[:, :width] = standard_image
+    canvas[:, width:] = duck_image
     
     # Add model labels
-    cv2.putText(canvas, "Duck Detector", (10, 30), 
+    cv2.putText(canvas, "Standard YOLOv8", (10, 30), 
                 cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
-    cv2.putText(canvas, "Standard YOLOv8", (width + 10, 30), 
+    cv2.putText(canvas, "Duck Detector", (width + 10, 30), 
                 cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
     
     return canvas
@@ -55,12 +55,13 @@ def compare_models(input_image):
 # Create Gradio interface
 iface = gr.Interface(
     fn=compare_models,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Image(type="numpy"),
+    inputs=gr.Image(type="pil", height=200),  # Smaller input image
+    outputs=gr.Image(type="numpy", height=400),  # Larger output for side-by-side comparison
     title="YOLO Model Comparison",
-    description="Compare Duck Detector with standard YOLOv8 model",
+    description="Compare standard YOLOv8 model (left) with Duck Detector (right)",
     examples=[["test_image.jpg"]],
-    cache_examples=True
+    cache_examples=True,
+    css="img.output {width: 100% !important; height: auto !important;}"  # Force output to span full width
 )
 
 # Launch the interface