Update app.py

app.py

@@ -14,13 +14,11 @@ def process_image(image, model):
     for r in results:
         boxes = r.boxes
         for box in boxes:
-            # Get box coordinates and confidence
             x1, y1, x2, y2 = map(int, box.xyxy[0].cpu().numpy())
             conf = float(box.conf[0])
             cls = int(box.cls[0])
             class_name = model.names[cls]
             
-            # Draw box and label
             cv2.rectangle(processed_image, (x1, y1), (x2, y2), (0, 255, 0), 2)
             label = f"{class_name} ({conf:.2f})"
             cv2.putText(processed_image, label, (x1, y1-10), 
@@ -29,40 +27,47 @@ def process_image(image, model):
     return processed_image
 
 def compare_models(input_image):
-    # Convert from Gradio's PIL image to OpenCV format
     image = np.array(input_image)
     
-    # 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)
+    gap = 20  # Add a 20-pixel gap between images
     
-    # Place images side by side (standard left, duck right)
+    # Create canvas with extra width for the gap
+    canvas = np.zeros((height, width * 2 + gap, 3), dtype=np.uint8)
+    
+    # Place images with gap in between
     canvas[:, :width] = standard_image
-    canvas[:, width:] = duck_image
+    canvas[:, width + gap:] = duck_image
+    
+    # Fill gap with white or gray color (optional)
+    canvas[:, width:width + gap] = [128, 128, 128]  # Gray gap
     
     # Add model labels
-    cv2.putText(canvas, "Standard YOLOv8", (10, 30), 
+    cv2.putText(canvas, "YOLOv8", (10, 30), 
                 cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
-    cv2.putText(canvas, "Duck Detector", (width + 10, 30), 
+    cv2.putText(canvas, "YOLOv8 Fine Tune", (width + gap + 10, 30), 
                 cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
     
     return canvas
 
-# Create Gradio interface
-iface = gr.Interface(
-    fn=compare_models,
-    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 standard YOLOv8 model (left) with Duck Detector (right)",
-    examples=[["test_image.jpg"]],
-    cache_examples=True,
-    css="img.output {width: 100% !important; height: auto !important;}"  # Force output to span full width
-)
+# Create Gradio interface with stacked layout
+with gr.Blocks() as iface:
+    gr.Markdown("# YOLO Model Comparison")
+    gr.Markdown("Compare standard YOLOv8 model (left) with Fine-tuned model (right)")
+    
+    with gr.Column():
+        input_image = gr.Image(type="pil", label="Input Image", height=200)
+        submit_btn = gr.Button("Compare Models")
+        output_image = gr.Image(type="numpy", label="Comparison Result", height=400)
+    
+    submit_btn.click(
+        fn=compare_models,
+        inputs=input_image,
+        outputs=output_image,
+    )
 
 # Launch the interface
 if __name__ == "__main__":